Course Catalog
All Courses
Browse the full catalog or choose a category from the left panel.
AWS Cloud Practitioner
Foundation in AWS cloud concepts, services, security, and pricing. Perfect for non-technical roles and beginners.
- Navigate AWS Management Console confidently
- Understand cloud computing fundamentals
- Identify core AWS services and use cases
- Configure basic IAM users and policies
- Create and manage S3 buckets
- Launch EC2 instances with proper settings
- Understand AWS pricing and billing models
- Apply AWS shared responsibility model
- Use AWS Well-Architected Framework basics
- Prepare for AWS Cloud Practitioner exam
AWS Solutions Architect Associate
Design distributed systems on AWS. Cover compute, storage, networking, databases, and security best practices.
- Design highly available multi-tier architectures
- Configure VPCs with public/private subnets
- Implement Auto Scaling and Load Balancers
- Set up RDS with Multi-AZ deployments
- Build serverless apps with Lambda and API Gateway
- Configure S3 lifecycle policies and replication
- Implement IAM roles and security best practices
- Design disaster recovery strategies
- Optimize costs with Reserved Instances and Savings Plans
- Monitor resources with CloudWatch and CloudTrail
AWS Solutions Architect Professional
Advanced architecture patterns, multi-account strategies, cost optimization, and migration at scale.
- Design multi-account strategies with AWS Organizations
- Implement landing zones with Control Tower
- Build hybrid connectivity with Direct Connect
- Configure Transit Gateway for network hub
- Migrate workloads with AWS Migration Hub
- Implement data lakes with Lake Formation
- Design for compliance (HIPAA, PCI, SOC)
- Optimize costs at enterprise scale
- Implement advanced security with GuardDuty, Macie
- Design global applications with Route 53 and CloudFront
AWS Developer Associate
Build serverless applications with Lambda, API Gateway, DynamoDB, and AWS SDKs. CI/CD with CodePipeline.
- Build Lambda functions in Python and Node.js
- Create REST APIs with API Gateway
- Design DynamoDB tables with proper keys
- Implement authentication with Cognito
- Use AWS SDK for programmatic access
- Set up CI/CD with CodePipeline and CodeBuild
- Deploy with SAM and CloudFormation
- Implement SQS and SNS for messaging
- Use Step Functions for workflow orchestration
- Debug with X-Ray and CloudWatch Logs
AWS Machine Learning Specialty
Build ML solutions with SageMaker, feature engineering, model training, and MLOps on AWS.
- Train models with SageMaker built-in algorithms
- Build custom containers for SageMaker training
- Implement feature engineering with SageMaker Processing
- Deploy models with SageMaker endpoints
- Set up MLOps pipelines with SageMaker Pipelines
- Use Bedrock for generative AI applications
- Implement NLP with Amazon Comprehend
- Build computer vision with Rekognition
- Monitor models with SageMaker Model Monitor
- Optimize inference with SageMaker Neo
AWS Data Analytics Specialty
Design data lakes, ETL pipelines with Glue, analytics with Athena, and real-time streaming with Kinesis.
- Design data lake architectures on S3
- Build ETL jobs with AWS Glue and PySpark
- Query data with Athena and Glue Data Catalog
- Stream data with Kinesis Data Streams
- Process streams with Kinesis Data Analytics
- Build data warehouses with Redshift
- Implement data governance with Lake Formation
- Visualize data with QuickSight dashboards
- Orchestrate pipelines with Step Functions
- Optimize query performance and costs
Azure Fundamentals (AZ-900)
Core Azure concepts, services, pricing, and support. Foundation for all Azure certifications.
- Navigate Azure Portal and Cloud Shell
- Understand Azure regions and availability zones
- Create and manage resource groups
- Deploy virtual machines and storage accounts
- Configure Azure Active Directory basics
- Implement network security groups
- Understand Azure pricing calculator
- Apply Azure governance with policies
- Use Azure Monitor for basic monitoring
- Prepare for AZ-900 certification exam
Azure Administrator (AZ-104)
Manage Azure identities, governance, storage, compute, and virtual networks.
- Manage Azure AD users, groups, and roles
- Configure virtual networks and subnets
- Implement VNet peering and VPN gateways
- Deploy and manage Azure VMs at scale
- Configure Azure Storage accounts and access
- Implement Azure Backup and Site Recovery
- Set up Azure Monitor alerts and dashboards
- Apply Azure Policy for governance
- Manage costs with Azure Cost Management
- Automate with PowerShell and Azure CLI
Azure Solutions Architect Expert (AZ-305)
Design identity, governance, data storage, business continuity, and infrastructure solutions.
- Design Azure landing zones architecture
- Implement hub-spoke network topology
- Design identity solutions with Azure AD B2C
- Architect data solutions with Cosmos DB, Synapse
- Design disaster recovery and high availability
- Implement zero-trust security architecture
- Design hybrid solutions with Azure Arc
- Optimize cost with Azure Advisor recommendations
- Design for compliance and governance at scale
- Implement application migration strategies
Azure Developer Associate (AZ-204)
Develop Azure compute, storage, security solutions. Build with Azure Functions, App Service, and Cosmos DB.
- Deploy web apps to Azure App Service
- Build serverless with Azure Functions
- Implement Cosmos DB data solutions
- Use Azure Blob and Queue storage
- Secure apps with Azure Key Vault
- Implement Azure Service Bus messaging
- Build APIs with Azure API Management
- Implement caching with Azure Redis
- Monitor with Application Insights
- Deploy containers to Azure Container Apps
Azure AI Engineer (AI-102)
Build AI solutions with Azure Cognitive Services, Azure OpenAI, Bot Service, and custom ML models.
- Build apps with Azure OpenAI GPT models
- Implement Azure AI Search with vectors
- Extract data with Document Intelligence
- Build vision solutions with Computer Vision
- Implement speech-to-text and text-to-speech
- Create custom language understanding models
- Build conversational AI with Bot Framework
- Implement content moderation and safety
- Deploy AI models with responsible AI practices
- Integrate AI services into applications
GCP Cloud Digital Leader
Fundamental knowledge of cloud concepts and Google Cloud products, services, and tools.
- Navigate Google Cloud Console effectively
- Understand GCP global infrastructure
- Identify core compute and storage services
- Configure basic IAM roles and permissions
- Use Cloud Shell for command-line operations
- Understand GCP pricing and billing
- Apply cloud security best practices
- Compare GCP services with competitors
- Understand digital transformation with GCP
- Prepare for Cloud Digital Leader exam
GCP Associate Cloud Engineer
Deploy applications, monitor operations, and manage GCP solutions using Cloud Console and CLI.
- Deploy and manage Compute Engine instances
- Configure VPC networks and firewall rules
- Deploy containers to GKE clusters
- Build serverless apps with Cloud Run
- Manage Cloud Storage buckets and objects
- Configure Cloud SQL and Cloud Spanner
- Set up Cloud Monitoring and Logging
- Implement IAM best practices
- Use Deployment Manager for IaC
- Prepare for ACE certification exam
GCP Professional Cloud Architect
Design and plan cloud solutions architecture. Manage and provision cloud infrastructure.
- Design highly available architectures
- Implement hybrid cloud with Anthos
- Configure Cloud Interconnect and VPN
- Design for disaster recovery
- Implement Security Command Center
- Apply organization policies at scale
- Optimize costs with committed use discounts
- Design microservices architectures
- Implement CI/CD with Cloud Build
- Prepare for PCA certification exam
GCP Professional Data Engineer
Design data processing systems, build ML pipelines, and analyze data with BigQuery and Dataflow.
- Design data warehouse with BigQuery
- Build streaming pipelines with Dataflow
- Implement real-time messaging with Pub/Sub
- Process data with Dataproc Spark clusters
- Orchestrate workflows with Cloud Composer
- Implement data lakes on Cloud Storage
- Build ETL/ELT pipelines with Dataform
- Secure data with column-level security
- Optimize BigQuery performance and costs
- Prepare for PDE certification exam
GCP Professional ML Engineer
Build ML systems on GCP with Vertex AI, AutoML, and custom training. Deploy and monitor ML models.
- Train custom models with Vertex AI Training
- Build models with AutoML Tables and Vision
- Implement feature engineering with Feature Store
- Deploy models to Vertex AI Endpoints
- Build MLOps pipelines with Vertex Pipelines
- Integrate Gemini API for generative AI
- Monitor model performance and drift
- Implement explainable AI with Vertex
- Optimize inference with model optimization
- Prepare for PMLE certification exam
GCP BigQuery & Data Analytics
Master BigQuery for large-scale data analytics. Covers advanced SQL, partitioning, ML in BigQuery, cost optimization, and Looker Studio dashboards.
- Write advanced analytical SQL in BigQuery.
- Optimize query performance with partitioning and clustering.
- Build ML models directly in BigQuery ML.
- Create dashboards with Looker Studio.
- Manage BigQuery costs and access controls.
GCP Cloud Functions & Serverless
Build event-driven serverless applications on GCP. Covers Cloud Functions, Cloud Run, Pub/Sub triggers, and serverless best practices.
- Deploy Cloud Functions with HTTP and event triggers.
- Build containerized services with Cloud Run.
- Implement event-driven architectures with Pub/Sub.
- Configure IAM and security for serverless workloads.
- Monitor and debug serverless applications.
GenAI Essentials
Foundation in LLM concepts, prompt engineering, and API integration. Build your first AI-powered applications.
- Understand transformer architecture fundamentals
- Master prompt engineering techniques
- Integrate OpenAI and Claude APIs
- Build chat applications with Streamlit
- Implement basic RAG with embeddings
- Handle API rate limits and errors
- Apply prompt templates and chains
- Evaluate LLM outputs effectively
- Understand tokenization and context limits
- Build simple AI-powered tools
GenAI Developer
Build production RAG systems, fine-tune LLMs with LoRA, and master LangChain/LlamaIndex frameworks.
- Build production RAG pipelines
- Implement vector databases with Pinecone
- Master LangChain chains and agents
- Use LlamaIndex for document indexing
- Fine-tune LLMs with LoRA and QLoRA
- Implement semantic chunking strategies
- Build hybrid search with reranking
- Deploy RAG apps to production
- Evaluate RAG with RAGAS metrics
- Optimize retrieval performance
GenAI Engineer
Master multimodal AI, autonomous agents with LangGraph, guardrails, and production deployment with vLLM.
- Build autonomous agents with LangGraph
- Implement multi-agent orchestration
- Deploy with vLLM for high throughput
- Add guardrails for safety and compliance
- Build multimodal vision-language apps
- Implement tool use and function calling
- Design agent memory systems
- Handle complex reasoning workflows
- Monitor and trace agent execution
- Scale inference with batching
GenAI Architect
Design enterprise AI systems with governance, MLOps, multi-model orchestration, and strategic planning.
- Design enterprise GenAI architecture
- Implement AI governance frameworks
- Build multi-model orchestration systems
- Design for scale with Kubernetes and Ray
- Implement cost optimization strategies
- Build LLMOps pipelines with MLflow
- Design security and compliance controls
- Implement model routing and fallbacks
- Build evaluation and monitoring systems
- Create GenAI center of excellence
GenAI Full Stack Bootcamp
Intensive 5-day program covering foundation through specialist. Complete GenAI stack in one week.
- Build complete GenAI applications end-to-end
- Master prompt engineering and RAG
- Implement vector search and embeddings
- Build and deploy AI agents
- Fine-tune models with LoRA
- Deploy to production with Docker
- Implement guardrails and safety
- Build multimodal applications
- Monitor and evaluate LLM systems
- Complete capstone project
GenAI for Leaders
Strategic AI overview for executives. Understand capabilities, build business cases, and lead transformation.
- Evaluate AI use cases for your business
- Understand LLM capabilities and limits
- Calculate AI ROI and TCO
- Build AI governance frameworks
- Identify AI risks and mitigations
- Lead AI change management
- Structure AI teams and roles
- Evaluate build vs. buy decisions
- Navigate AI vendor landscape
- Create AI adoption roadmap
Data Engineering Fundamentals
Core concepts of data engineering: pipelines, ETL/ELT, data modeling, and warehouse fundamentals.
- Design data models with normalization
- Write complex SQL queries and CTEs
- Build ETL pipelines with Python
- Implement data quality checks
- Understand star and snowflake schemas
- Work with JSON and semi-structured data
- Apply data governance principles
- Use version control for data projects
- Debug and optimize slow queries
- Document data pipelines effectively
Databricks Data Engineer
Build data pipelines with Delta Lake, Spark, and Databricks. ETL, streaming, and lakehouse architecture.
- Build Delta Lake tables with ACID guarantees
- Implement medallion architecture
- Write PySpark transformations
- Configure Unity Catalog for governance
- Build streaming pipelines with Auto Loader
- Schedule jobs with Databricks Workflows
- Optimize Spark performance with tuning
- Implement CDC with Delta Live Tables
- Query data with Databricks SQL
- Prepare for Databricks certification
Apache Spark SkillUp
Master distributed data processing with PySpark. RDDs, DataFrames, Spark SQL, and performance tuning.
- Understand Spark architecture and execution
- Work with RDDs, DataFrames, and Datasets
- Write efficient Spark SQL queries
- Build streaming apps with Structured Streaming
- Implement window functions and aggregations
- Optimize jobs with partitioning and caching
- Debug with Spark UI and metrics
- Use MLlib for machine learning
- Deploy Spark on YARN and Kubernetes
- Handle skewed data and performance issues
Snowflake Data Engineering
Build modern data warehouses with Snowflake. Data loading, transformations, and Snowpark development.
- Design Snowflake data warehouse architecture
- Load data with COPY and Snowpipe
- Write transformations with Snowpark Python
- Implement time travel and fail-safe
- Configure data sharing across accounts
- Build with Snowflake Marketplace
- Optimize costs with warehouse sizing
- Implement role-based access control
- Use dbt for transformations
- Monitor with Query History and Account Usage
Apache Airflow & Orchestration
Build and manage data pipelines with Airflow. DAGs, operators, sensors, and production deployment.
- Design DAGs with best practices
- Implement operators for different systems
- Use sensors for event-driven workflows
- Build dynamic DAGs with TaskFlow API
- Configure connections and variables
- Implement branching and conditional logic
- Monitor DAGs with alerts and SLAs
- Deploy Airflow on Kubernetes
- Handle failures with retries and callbacks
- Scale with Celery and Kubernetes executors
Apache Iceberg & Lakehouse
Build modern lakehouse architectures with Apache Iceberg. Covers table formats, schema evolution, time travel, and integration with Spark and Trino.
- Create and manage Iceberg tables on cloud storage.
- Implement schema evolution and partition evolution.
- Use time travel for data versioning and rollback.
- Optimize query performance with hidden partitioning.
- Integrate Iceberg with Spark, Trino, and Flink.
Redis & In-Memory Data
Master Redis for caching, real-time analytics, and message brokering. Covers data structures, clustering, persistence, and production deployment.
- Implement caching strategies with Redis.
- Use Redis data structures for real-time analytics.
- Configure Redis clustering for high availability.
- Implement pub/sub messaging patterns.
- Monitor and optimize Redis performance in production.
Medallion Architecture & Data Lakehouse
Design and implement medallion (bronze-silver-gold) data architectures. Covers data quality patterns, Delta Lake, incremental processing, and governance.
- Design bronze-silver-gold data pipeline architectures.
- Implement data quality checks at each medallion layer.
- Build incremental processing pipelines with Delta Lake.
- Apply data governance and lineage tracking.
- Optimize lakehouse query performance.
Machine Learning Fundamentals
Core ML concepts: supervised/unsupervised learning, model evaluation, feature engineering, and sklearn workflows.
- Implement linear & logistic regression from scratch
- Build decision trees and random forest models
- Apply cross-validation and hyperparameter tuning
- Perform feature scaling and normalization
- Handle missing data and outlier detection
- Create end-to-end ML pipelines with sklearn
- Evaluate models using precision, recall, F1
- Implement k-means and hierarchical clustering
- Apply dimensionality reduction with PCA
- Deploy models with pickle and joblib
Deep Learning with PyTorch
Build neural networks from scratch. CNNs, RNNs, transformers, and training optimization with PyTorch.
- Build neural networks with PyTorch tensors
- Implement CNNs for image classification
- Design RNNs and LSTMs for sequence data
- Apply attention mechanisms and transformers
- Optimize training with Adam, learning rate schedulers
- Implement batch normalization and dropout
- Use transfer learning with pretrained models
- Debug models with TensorBoard visualization
- Train models on GPU with CUDA acceleration
- Export models to ONNX for production
MLOps & Model Deployment
End-to-end ML pipelines with MLflow, Kubeflow, and model serving. CI/CD for ML, monitoring, and drift detection.
- Track experiments with MLflow tracking server
- Version models with MLflow Model Registry
- Build ML pipelines with Kubeflow Pipelines
- Containerize models with Docker for deployment
- Serve models with FastAPI and Flask endpoints
- Implement CI/CD pipelines for ML with GitHub Actions
- Monitor model performance and data drift
- Set up A/B testing for model comparison
- Implement feature stores for ML features
- Scale inference with Kubernetes and Seldon
Computer Vision & Image AI
Object detection, image segmentation, and vision transformers. Build production CV pipelines with YOLO and OpenCV.
- Train custom object detection with YOLOv8
- Implement image segmentation with U-Net
- Apply Vision Transformers (ViT) for classification
- Build real-time video processing pipelines
- Perform data augmentation with Albumentations
- Annotate datasets with CVAT and Label Studio
- Deploy CV models with TensorRT optimization
- Implement face detection and recognition
- Build OCR pipelines with Tesseract and EasyOCR
- Create pose estimation with MediaPipe
NLP & Text Analytics
Text processing, embeddings, sentiment analysis, and NER. Build NLP pipelines with spaCy, NLTK, and Hugging Face.
- Implement text preprocessing and tokenization
- Build word embeddings with Word2Vec, GloVe
- Fine-tune BERT for text classification
- Train custom NER models with spaCy
- Perform sentiment analysis at scale
- Build text summarization pipelines
- Implement question answering systems
- Create semantic search with sentence transformers
- Deploy NLP models with Hugging Face Inference API
- Build multilingual NLP applications
Kubernetes Administrator (CKA)
Master Kubernetes cluster administration, deployment, networking, storage, and troubleshooting.
- Deploy and manage Kubernetes clusters
- Create deployments, services, and ingress
- Configure ConfigMaps and Secrets
- Implement persistent storage with PVs
- Set up RBAC and network policies
- Troubleshoot pods and containers
- Configure resource limits and quotas
- Implement rolling updates and rollbacks
- Set up cluster monitoring and logging
- Prepare for CKA certification exam
Terraform & Infrastructure as Code
Multi-cloud infrastructure automation with Terraform. Modules, state management, and best practices.
- Write Terraform configurations with HCL
- Manage state with remote backends
- Create reusable modules
- Use workspaces for environments
- Implement variables and outputs
- Handle secrets securely
- Deploy to AWS, Azure, and GCP
- Use Terraform Cloud for collaboration
- Implement CI/CD with Terraform
- Debug and troubleshoot deployments
Platform Engineering
Build internal developer platforms with Backstage, GitOps, and self-service infrastructure.
- Build internal developer platforms
- Set up Backstage software catalog
- Implement GitOps with ArgoCD
- Create self-service infrastructure
- Build golden paths for developers
- Implement platform APIs and abstractions
- Set up developer portals
- Measure platform adoption and value
- Build CI/CD platform components
- Implement platform security controls
Generative AI Fundamentals
Understand generative AI concepts and model architectures
- Explore Large Language Models (LLMs) and their capabilities.
- Learn prompt engineering techniques for optimal outputs.
- Understand ethical considerations and responsible AI practices.
- Evaluate and select appropriate GenAI tools for use cases.
- Basic understanding of AI/ML concepts, Familiarity with Python (helpful).
Prompt Engineering Masterclass
Design effective prompts for various LLM tasks
- Implement chain-of-thought and few-shot prompting.
- Build prompt templates for enterprise applications.
- Evaluate and iterate on prompt effectiveness.
- Basic understanding of LLMs, Experience using ChatGPT or similar tools.
- Understand core concepts and terminology
LangChain & LLM Application Development
Build LLM-powered applications using LangChain framework
- Implement chains, agents, and memory for complex workflows.
- Create document Q&A systems with retrieval augmentation.
- Integrate multiple LLM providers and tools.
- Deploy production-ready LLM applications.
- Python proficiency, Understanding of LLMs, API integration experience.
Retrieval Augmented Generation (RAG)
Understand RAG architecture and components
- Implement document processing and chunking strategies.
- Configure vector databases for semantic search.
- Build production-grade RAG pipelines.
- Optimize retrieval quality and response accuracy.
- Python proficiency, LLM fundamentals, Basic understanding of embeddings.
Fine-tuning Large Language Models
Understand LLM fine-tuning approaches and trade-offs
- Implement full fine-tuning and parameter-efficient methods.
- Apply LoRA, QLoRA, and adapter-based fine-tuning.
- Prepare datasets and evaluate fine-tuned models.
- Deploy and serve fine-tuned LLMs.
- Deep learning experience, LLM fundamentals, PyTorch proficiency.
Implement custom training with Vertex AI Training
Leverage Google's pre-trained AI APIs
- Build GenAI applications with Vertex AI.
- GCP fundamentals, ML basics, Python proficiency.
- GCP Account with Vertex AI access, Cloud SDK, Python 3.10+, Jupyter Notebook.
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
Generative AI for Enterprise
Identify enterprise use cases for Generative AI
- Design secure and compliant GenAI architectures.
- Implement governance frameworks for AI systems.
- Evaluate build vs. buy decisions for GenAI.
- Basic AI/ML understanding, Enterprise IT experience, Strategic planning exposure.
- Understand core concepts and terminology
Natural Language Processing Fundamentals
NLP skills are prerequisite for LLM and GenAI work. This program builds essential text processing and modeling skills,...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Generative AI & LLM Fundamentals
GenAI literacy is becoming mandatory across all IT roles. This program ensures every fresher understands GenAI...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
LangChain & LLM Application Development
LLM applications are the future of enterprise software. This program creates developers capable of building...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
RAG Systems Development
RAG enables LLMs to use organizational knowledge. This specialized program creates developers who can build enterprise...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Test Automation with Python & Selenium
Test automation is essential for CI/CD and rapid delivery. This program creates automation engineers who can build and...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
LLM Testing & AI Model Evaluation
Testing AI systems requires specialized approaches. This cutting-edge program creates testers capable of evaluating LLM...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Prompt Engineering Mastery
Advanced prompt engineering covering zero-shot, few-shot, chain-of-thought, role-based prompting, prompt templates, evaluation frameworks, and building enterprise prompt libraries.
- Apply prompt engineering principles for optimal results.
- Design effective zero-shot and few-shot prompts.
- Implement chain-of-thought prompting for complex reasoning.
- Use role-based and persona prompting effectively.
- Create prompt templates for repeatable tasks.
- Evaluate and iterate on prompt effectiveness.
- Build prompt libraries for team use.
Building Production RAG Systems
Scale RAG beyond prototypes. Covers chunking strategies, embedding optimization, hybrid search, evaluation frameworks, and production deployment.
- Design production-grade RAG architectures.
- Implement advanced chunking and embedding strategies.
- Build hybrid search with vector and keyword retrieval.
- Evaluate RAG quality with automated testing frameworks.
- Deploy and monitor RAG systems at scale.
Multi-Agent AI Systems
Build collaborative multi-agent systems for complex enterprise workflows. Covers agent orchestration, tool use, memory, and production deployment.
- Design multi-agent architectures for complex tasks.
- Implement agent orchestration with CrewAI and LangGraph.
- Build custom tools and integrations for agents.
- Manage agent memory and conversation state.
- Deploy and monitor multi-agent systems in production.
LLM Security & Red Teaming
Secure LLM applications against prompt injection, jailbreaks, and data leakage. Covers OWASP LLM Top 10, guardrails, and red teaming methodologies.
- Identify and mitigate prompt injection attacks.
- Implement input/output guardrails for LLM applications.
- Conduct red team exercises against AI systems.
- Apply OWASP LLM Top 10 security controls.
- Design defense-in-depth strategies for GenAI deployments.
Machine Learning Foundations
Understand core ML concepts, terminology, and workflow
- Implement supervised learning algorithms (regression, classification).
- Apply unsupervised learning techniques (clustering, dimensionality reduction).
- Evaluate and validate ML models using appropriate metrics.
- Build end-to-end ML pipelines using scikit-learn.
- Python programming proficiency, Basic statistics, Linear algebra fundamentals.
Deep Learning with TensorFlow
Understand neural network architecture and backpropagation
- Build and train deep neural networks using TensorFlow/Keras.
- Implement CNNs for image classification and object detection.
- Design RNNs and LSTMs for sequence modeling.
- Deploy trained models using TensorFlow Serving.
- Python proficiency, ML fundamentals, Basic calculus and linear algebra.
Deep Learning with PyTorch
Master PyTorch tensors, autograd, and computational graphs
- Build custom neural network architectures using nn.Module.
- Implement CNNs and RNNs for various applications.
- Use PyTorch Lightning for scalable training.
- Deploy models using TorchScript and ONNX.
- Python proficiency, ML fundamentals, Understanding of neural networks.
Computer Vision with Deep Learning
Master image processing and augmentation techniques
- Implement CNN architectures for classification and detection.
- Apply transfer learning with pre-trained models.
- Build object detection systems using YOLO/Faster R-CNN.
- Deploy computer vision models in production.
- Python proficiency, Deep learning fundamentals, Basic linear algebra.
MLOps & ML Engineering
Design end-to-end ML pipelines with best practices
- Implement experiment tracking and model versioning.
- Build automated training and deployment pipelines.
- Configure monitoring and observability for ML systems.
- Apply DevOps principles to ML workflows.
- ML fundamentals, Python proficiency, Docker basics, CI/CD understanding.
AWS Machine Learning Specialty
Master AWS ML services (SageMaker, Comprehend, Rekognition)
- Build and deploy ML models on SageMaker.
- Implement MLOps practices using AWS services.
- Design scalable ML architectures on AWS.
- Prepare for AWS ML Specialty certification.
- AWS fundamentals, ML basics, Python proficiency.
Azure AI & Machine Learning
Leverage Azure ML Studio for end-to-end ML workflows
- Use Azure Cognitive Services for AI applications.
- Implement Azure OpenAI Service for GenAI solutions.
- Build MLOps pipelines with Azure ML.
- Design AI solutions using Azure AI platform.
- Azure fundamentals, ML basics, Python proficiency.
Google Cloud AI & Vertex AI
Master Vertex AI for unified ML platform capabilities
- Use AutoML for rapid model development.
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
Time Series Forecasting with ML
Master time series analysis and preprocessing
- Implement classical and ML forecasting methods.
- Apply deep learning models for time series.
- Build ensemble and hybrid forecasting systems.
- Deploy forecasting models in production.
- Python proficiency, ML fundamentals, Basic statistics.
Edge AI & TinyML
Understand edge AI constraints and optimization techniques
- Implement model quantization and pruning.
- Deploy models on edge devices (Raspberry Pi, Jetson).
- Work with TensorFlow Lite and ONNX Runtime.
- Design end-to-end edge AI solutions.
- Deep learning experience, Python proficiency, Basic embedded systems knowledge.
Python Programming for Developers
Python's versatility makes it essential for modern development, data engineering, and AI/ML projects. This program...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Machine Learning Foundations
This comprehensive program builds strong machine learning foundations. From mathematical concepts to implementing...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Deep Learning Essentials
This intensive program covers deep learning from neural network basics to practical implementations with...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
AI Machine Learning & Data Analysis
Understand fundamental ML concepts and algorithm types
- Implement supervised learning (regression, classification).
- Apply unsupervised learning techniques (clustering, dimensionality reduction).
- Build and evaluate ML models using scikit-learn.
- Perform exploratory data analysis and feature engineering.
- Python programming proficiency, Basic statistics knowledge, Linear algebra fundamentals.
Kubernetes, Node.js, React.js, Gen AI & Deep Learning
Deploy and manage applications on Kubernetes clusters
- Build scalable backend services with Node.js.
- Develop modern frontend applications with React.js.
- Implement Generative AI solutions using LLMs.
- Design and train deep learning models.
- Programming experience (JavaScript/Python), Basic cloud concepts, Understanding of APIs.
Python Programming
AI Machine Learning & Data Analysis
- Business Analysis & Technical Writing.
- Test Automation (Web, Data & BI Applications).
- Application Security Framework Foundation.
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
Feature Engineering & Feature Stores
Master feature engineering for production ML. Covers feature stores, real-time features, data validation, and integration with ML pipelines.
- Build reusable feature pipelines for ML models.
- Set up and operate Feast feature store.
- Implement real-time feature computation.
- Apply data validation and drift detection.
- Integrate feature stores with training and serving infrastructure.
ML Model Monitoring & Observability
Monitor ML models in production for drift, performance degradation, and reliability. Covers model observability, alerting, and retraining triggers.
- Detect data drift and model performance degradation.
- Set up automated model monitoring dashboards.
- Configure alerting for model quality thresholds.
- Implement A/B testing frameworks for model comparison.
- Design automated retraining pipelines.
Core Java Programming
Intensive program transforming fresh graduates into productive Java developers. Covers Core Java from fundamentals to advanced concepts like multithreading, collections, JDBC, and JUnit testing.
- Utilize Java Collections Framework for efficient data management.
- Develop multithreaded applications for concurrent processing.
- Work with Java I/O and file handling operations.
- Connect to databases using JDBC for data persistence.
- Write unit tests using JUnit for code quality assurance.
Python Programming Fundamentals
Comprehensive Python programming covering OOP, data structures, file handling, automation scripting, virtual environments, and building web applications with Flask.
- Handle files, JSON, CSV, and API interactions.
- Develop automation scripts for repetitive tasks.
- Use virtual environments and package management (pip, conda).
- Write unit tests using pytest for quality assurance.
- Build basic web applications using Flask framework.
Python for Data Science & Analytics
This focused program builds data science proficiency with Python's powerful ecosystem. Covering NumPy for numerical...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Selenium Test Automation with Python
Master web test automation using Selenium WebDriver with Python. Covers page object models, pytest frameworks, Allure reporting, and cross-browser testing strategies.
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
API Testing Mastery
APIs are the backbone of modern applications. This program creates testers who can thoroughly validate API...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Python Programming for Developers
Full Stack Web Development Foundations
- Git, GitHub & Version Control Mastery.
- Software Engineering Best Practices.
- AWS Essential Services for Developers.
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
Python Programming
Master Python fundamentals including data types, control structures, and functions
- Develop proficiency in object-oriented programming concepts.
- Work with file handling, exception handling, and modules.
- Utilize popular libraries for data manipulation (NumPy, Pandas).
- Build practical applications and automate tasks.
- Basic computer literacy, Logical thinking ability, No prior programming experience required.
Advanced SQL
Master complex query writing including subqueries and CTEs
- Optimize query performance using indexes and execution plans.
- Work with window functions and analytical queries.
- Design and implement stored procedures and functions.
- Basic SQL knowledge (SELECT, INSERT, UPDATE, DELETE), Understanding of relational database concepts.
- Understand core concepts and terminology
Python Web Frameworks (Django/Flask)
Build web applications using Django and Flask frameworks
- Implement MVC/MVT architecture patterns.
- Create REST APIs with Django REST Framework/Flask-RESTful.
- Configure database models and ORM.
- Deploy applications to cloud platforms.
- Python proficiency, HTML/CSS basics, Understanding of HTTP and web concepts.
Full Stack JavaScript (React + Node)
Build complete web applications with the MERN stack. Covers React components, Node.js APIs, Express routing, MongoDB, and deployment.
- Build React applications with hooks and state management.
- Create RESTful APIs with Node.js and Express.
- Design MongoDB schemas and perform CRUD operations.
- Implement authentication with JWT tokens.
- Deploy full-stack applications to cloud platforms.
Data Structures & Algorithms
Master essential data structures and algorithms for technical interviews and production code. Covers arrays, trees, graphs, dynamic programming, and complexity analysis.
- Implement core data structures (arrays, linked lists, trees, graphs).
- Apply sorting and searching algorithms efficiently.
- Solve dynamic programming problems systematically.
- Analyze time and space complexity of solutions.
- Apply algorithmic thinking to real-world engineering problems.
Cloud Computing Fundamentals
Foundational cloud computing covering concepts, service models (IaaS, PaaS, SaaS), all major providers (AWS, Azure, GCP), networking, security, cost management, and architecture patterns.
- Understand cloud computing concepts, benefits, and service models.
- Compare major cloud providers and their core services.
- Navigate cloud consoles and perform basic operations.
- Understand cloud networking, storage, and compute fundamentals.
- Implement basic security practices in cloud environments.
- Estimate cloud costs and understand pricing models.
- Deploy simple applications on cloud platforms.
- Understand cloud architecture patterns and best practices.
Git, GitHub & Version Control Mastery
Complete Git proficiency from basics to advanced workflows. Covers GitHub collaboration, pull requests, code reviews, branching strategies (GitFlow, trunk-based), hooks, and recovery techniques.
- Perform all essential Git operations confidently.
- Work with branches effectively for feature development.
- Resolve merge conflicts systematically.
- Collaborate using GitHub pull requests and code reviews.
- Follow GitFlow and trunk-based development workflows.
- Use Git hooks and automation for quality control.
- Recover from common Git mistakes safely.
Docker & Containerization
Containers are the standard for modern application deployment. This program ensures freshers understand...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Kubernetes Fundamentals
Kubernetes is the de facto standard for container orchestration. This program ensures freshers can work in Kubernetes...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
GitLab CI/CD & DevOps
Master Git version control and GitLab platform features
- Design and implement CI/CD pipelines using GitLab CI.
- Configure automated testing, building, and deployment stages.
- Implement containerization with Docker in pipelines.
- Apply DevOps best practices and GitOps principles.
- Basic command line proficiency, Understanding of software development workflow, Docker basics (helpful).
Ansible Automation
Automate infrastructure provisioning and configuration with Ansible. Covers playbooks, roles, modules, vault, and enterprise automation patterns.
- Write Ansible playbooks for infrastructure automation.
- Organize code with roles and collections.
- Manage secrets securely with Ansible Vault.
- Integrate Ansible with CI/CD pipelines.
- Automate multi-tier application deployments.
ArgoCD & GitOps
Implement GitOps workflows with ArgoCD for Kubernetes. Covers declarative deployments, sync strategies, multi-cluster management, and rollback patterns.
- Set up ArgoCD for Kubernetes GitOps workflows.
- Implement declarative application deployments.
- Configure sync policies and health checks.
- Manage multi-cluster deployments from a single control plane.
- Implement progressive delivery with rollback strategies.
Jenkins CI/CD Pipelines
Build enterprise CI/CD pipelines with Jenkins. Covers declarative pipelines, shared libraries, distributed builds, and integration with cloud platforms.
- Create declarative and scripted Jenkins pipelines.
- Build shared libraries for pipeline reuse.
- Configure distributed builds with Jenkins agents.
- Integrate with Docker, Kubernetes, and cloud platforms.
- Implement security scanning and quality gates.
Prometheus & Grafana Monitoring
Build production monitoring and observability stacks. Covers metrics collection, custom dashboards, alerting rules, and SRE practices.
- Deploy Prometheus for metrics collection and storage.
- Build custom Grafana dashboards for infrastructure and apps.
- Configure AlertManager rules and notification channels.
- Implement SLI/SLO monitoring for service reliability.
- Monitor Kubernetes clusters with Prometheus Operator.
AWS S3
Master S3 bucket configuration, security best practices, lifecycle policies, versioning, replication, and cost optimization for production-grade object storage.
- Master S3 bucket configuration and management.
- Implement security best practices with policies and encryption.
- Configure lifecycle policies and storage classes.
- Enable versioning, replication, and event notifications.
- Optimize cost and performance for S3 workloads.
AWS Glue
Master AWS Glue for serverless ETL. Covers Glue architecture, Data Catalog, crawlers, Glue Studio, PySpark-based ETL job development, and performance optimization.
- Understand AWS Glue architecture and components.
- Create and manage Glue Data Catalog.
- Develop ETL jobs using Glue Studio and PySpark.
- Implement crawlers for schema discovery.
- Optimize Glue job performance and cost.
AWS Essential Services for Developers
AWS dominates enterprise cloud adoption. This hands-on program ensures freshers can navigate AWS services confidently...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
AWS Airflow (MWAA)
Understand Apache Airflow architecture and concepts
- Design and implement DAGs for workflow orchestration.
- Configure AWS Managed Workflows for Apache Airflow (MWAA).
- Integrate with AWS services (S3, Glue, Redshift, Lambda).
- Monitor, troubleshoot, and optimize Airflow workflows.
- Python programming skills, AWS fundamentals, Understanding of ETL concepts.
AWS Monitoring (CloudWatch)
Configure CloudWatch metrics and alarms
- Implement log aggregation and analysis with CloudWatch Logs.
- Create dashboards for infrastructure monitoring.
- Set up automated responses with CloudWatch Events.
- Monitor applications using X-Ray and Container Insights.
- AWS fundamentals, Basic understanding of cloud infrastructure, EC2/Lambda experience.
Amazon Redshift
Understand Redshift architecture and cluster management
- Design optimized schemas using distribution and sort keys.
- Load data efficiently using COPY command and Glue.
- Write performant analytical queries.
- Implement security, monitoring, and cost optimization.
- SQL proficiency, Data warehousing concepts, AWS fundamentals.
AWS DevOps
Implement CI/CD pipelines using AWS CodePipeline, CodeBuild, CodeDeploy
- Manage infrastructure as code with CloudFormation and CDK.
- Configure containerized deployments with ECS/EKS.
- Implement monitoring and logging with CloudWatch.
- Apply DevOps best practices in AWS environment.
- AWS fundamentals, Linux command line, Basic programming/scripting, Git knowledge.
AWS Solutions Architect
Design highly available, fault-tolerant architectures
- Select appropriate AWS services for various use cases.
- Implement security best practices and compliance.
- Prepare for AWS Solutions Architect certification.
- AWS fundamentals, Networking concepts, Hands-on AWS experience (6+ months recommended).
- Understand core concepts and terminology
AWS Lambda & Serverless
Build production serverless applications with AWS Lambda. Covers API Gateway, DynamoDB, Step Functions, SAM, and serverless best practices.
- Build and deploy Lambda functions with multiple triggers.
- Design RESTful APIs with API Gateway and Lambda.
- Implement event-driven architectures with SNS/SQS.
- Use SAM and CloudFormation for serverless IaC.
- Optimize Lambda performance and manage cold starts.
AWS EKS & Kubernetes on AWS
Deploy and manage Kubernetes workloads on AWS EKS. Covers cluster setup, networking, Helm charts, Fargate profiles, and production operations.
- Provision and configure EKS clusters.
- Deploy applications using Helm charts and manifests.
- Configure EKS networking with VPC CNI and ALB.
- Implement autoscaling with Karpenter and HPA.
- Monitor EKS workloads with CloudWatch Container Insights.
Azure Core Services for Developers
Azure is the preferred cloud for enterprises with Microsoft investments. This program ensures freshers can work...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Azure DevOps & Pipelines
Master Azure DevOps for end-to-end software delivery. Covers Repos, Pipelines, Boards, Artifacts, and enterprise CI/CD workflows.
- Set up Azure DevOps projects and repositories.
- Build multi-stage YAML pipelines for CI/CD.
- Implement release gates and approvals.
- Configure artifact feeds and dependency management.
- Integrate automated testing into pipeline workflows.
Azure Data Factory & Data Integration
Build enterprise data pipelines with Azure Data Factory. Covers data flows, mapping transformations, Synapse integration, and monitoring.
- Design data pipelines with Azure Data Factory.
- Implement mapping and wrangling data flows.
- Orchestrate complex ETL workflows with triggers.
- Integrate with Azure Synapse and Data Lake.
- Monitor and troubleshoot pipeline executions.
Cloud Security Fundamentals
Master cloud security principles across AWS and Azure. Covers IAM, network security, encryption, compliance frameworks, and security monitoring.
- Implement IAM policies and least-privilege access.
- Configure network security groups and firewalls.
- Set up encryption at rest and in transit.
- Apply compliance frameworks (SOC 2, ISO 27001).
- Monitor security events and respond to incidents.
DevSecOps & Secure CI/CD
Integrate security into every stage of the CI/CD pipeline. Covers SAST, DAST, container scanning, secrets management, and security-as-code.
- Embed security scanning in CI/CD pipelines.
- Implement SAST and DAST tools effectively.
- Secure container images and registries.
- Manage secrets with Vault and AWS Secrets Manager.
- Automate compliance checks in deployment workflows.
Ethical Hacking & Penetration Testing
Learn offensive security techniques to identify vulnerabilities. Covers network penetration testing, web app testing, social engineering, and reporting.
- Conduct network reconnaissance and enumeration.
- Exploit common web application vulnerabilities (OWASP Top 10).
- Perform privilege escalation and lateral movement.
- Use Burp Suite for web application testing.
- Write professional penetration test reports.
Zero Trust Architecture
Design and implement Zero Trust security models for enterprise environments. Covers identity-centric security, micro-segmentation, and continuous verification.
- Design Zero Trust network architectures.
- Implement identity-based access controls.
- Configure micro-segmentation strategies.
- Deploy continuous authentication and authorization.
- Integrate SASE frameworks for remote workforce security.
SOC Analyst & Threat Detection
Build SOC analyst skills for enterprise threat detection and incident response. Covers SIEM operations, log analysis, threat hunting, and incident workflows.
- Operate SIEM platforms for security monitoring.
- Analyze security logs and detect anomalies.
- Perform threat hunting using structured methodologies.
- Execute incident response playbooks.
- Produce threat intelligence reports for stakeholders.
Natural Language Processing (NLP)
Master text preprocessing and feature extraction techniques
- Implement traditional and deep learning NLP models.
- Build text classification and sentiment analysis systems.
- Apply named entity recognition and information extraction.
- Work with transformer-based models for NLP tasks.
- Python proficiency, ML fundamentals, Basic understanding of neural networks.
Transformers & Attention Mechanisms
Understand transformer architecture and self-attention
- Implement attention mechanisms from scratch.
- Work with encoder-only, decoder-only, and encoder-decoder models.
- Fine-tune pre-trained transformers for custom tasks.
- Optimize transformer models for inference.
- Deep learning experience, PyTorch proficiency, Strong linear algebra background.
AI Agents & Autonomous Systems
Understand AI agent architectures and design patterns
- Build autonomous agents using LangChain/AutoGen.
- Implement tool use and function calling.
- Design multi-agent systems for complex tasks.
- Deploy and monitor production AI agents.
- LLM experience, Python proficiency, LangChain basics.
Responsible AI & Ethics
Understand AI ethics principles and frameworks
- Identify and mitigate bias in ML systems.
- Implement fairness metrics and testing.
- Design transparent and explainable AI systems.
- Basic ML understanding, Interest in AI ethics and policy.
- Understand core concepts and terminology
Diffusion Models & Image Generation
Understand diffusion model theory and architecture
- Work with Stable Diffusion and DALL-E.
- Implement image generation pipelines.
- Fine-tune diffusion models for custom domains.
- Apply ControlNet and other conditioning techniques.
- Deep learning experience, PyTorch proficiency, Understanding of generative models.
Speech AI & Conversational Systems
Implement speech recognition (ASR) systems
- Build text-to-speech (TTS) applications.
- Design conversational AI architectures.
- Integrate speech with LLMs for voice assistants.
- Deploy real-time speech processing systems.
- Python proficiency, ML basics, Understanding of audio/signal processing helpful.
Recommendation Systems
Understand recommendation system architectures
- Implement collaborative and content-based filtering.
- Build deep learning recommendation models.
- Design real-time recommendation pipelines.
- Evaluate and optimize recommendation quality.
- Python proficiency, ML fundamentals, Linear algebra.
Reinforcement Learning
Master RL fundamentals and Markov Decision Processes
- Implement value-based and policy-based methods.
- Apply deep reinforcement learning algorithms.
- Use RL frameworks (Stable Baselines, RLlib).
- Design RL solutions for real-world problems.
- Strong Python skills, Deep learning experience, Probability theory.
AI Product Management
Define AI product vision and success metrics
- Translate business requirements to ML problem statements.
- Manage AI/ML development lifecycle.
- Evaluate build vs. buy decisions for AI capabilities.
- Address ethical and regulatory considerations.
- Product management experience, Basic understanding of AI/ML concepts.
Full Stack Web Development Foundations
Full-stack developers are in highest demand. This accelerated program creates versatile developers capable of handling...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
REST API Design & Development
APIs are the backbone of modern applications and microservices. This focused program ensures freshers can design,...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Software Engineering Best Practices
Writing code that works is not enough—it must be maintainable, testable, and scalable. This program elevates fresh...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Computer Vision Fundamentals
Computer vision applications are proliferating across industries. This specialized program creates developers capable...
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
- Prepare for professional certification (if applicable)
Business Analysis & Technical Writing
Master requirements gathering and documentation techniques
- Create comprehensive Business Requirements Documents (BRD).
- Develop user stories and acceptance criteria.
- Write clear technical documentation and API specifications.
- Apply UML diagrams for process and system modeling.
- Basic understanding of software development lifecycle, Good communication skills, MS Office proficiency.
Test Automation (Web, Data & BI Applications)
Design test automation frameworks for web applications
- Implement Selenium/Playwright for UI testing.
- Automate data validation and ETL testing.
- Test BI dashboards and reports programmatically.
- Integrate automated tests with CI/CD pipelines.
- Programming knowledge (Python/Java), Manual testing experience, Basic SQL knowledge.
Unix Shell Scripting
Master Unix/Linux command line operations
- Write efficient shell scripts for automation.
- Implement text processing with sed, awk, and grep.
- Handle file operations and process management.
- Basic computer operations knowledge, Access to Unix/Linux environment.
- Understand core concepts and terminology
Angular 17+
Master Angular architecture and component-based development
- Implement reactive forms and template-driven forms.
- Configure routing and navigation with guards.
- Manage state using services and RxJS.
- Consume REST APIs and implement authentication.
- HTML/CSS proficiency, JavaScript/TypeScript knowledge, Basic understanding of web development.
Application Security Framework Foundation
Understand OWASP Top 10 vulnerabilities and mitigations
- Implement secure coding practices across applications.
- Perform security assessments and code reviews.
- Configure application security tools (SAST/DAST).
- Design secure authentication and authorization mechanisms.
- Programming experience (any language), Basic web application understanding, Familiarity with HTTP/HTTPS.
Project Management
Apply project management frameworks (Waterfall, Agile, Hybrid)
- Create project plans, WBS, and Gantt charts.
- Manage project scope, schedule, and budget.
- Implement risk management and stakeholder communication.
- Use project management tools effectively.
- Basic business understanding, Team collaboration experience, MS Office proficiency.
Spring/Struts/Hibernate
Build enterprise applications using Spring Boot framework
- Implement dependency injection and aspect-oriented programming.
- Create REST APIs with Spring MVC.
- Configure Hibernate ORM for database operations.
- Apply security with Spring Security.
- Core Java proficiency, Basic SQL knowledge, Understanding of OOP concepts.
Katalon Studio
Set up and configure Katalon Studio for test automation
- Create web and API test cases using Record & Playback.
- Implement data-driven testing with external data sources.
- Generate comprehensive test reports.
- Integrate Katalon with CI/CD pipelines.
- Manual testing experience, Basic understanding of test automation concepts, Groovy/Java basics (helpful).
MicroStrategy
Navigate MicroStrategy Desktop and Web interfaces
- Create reports, dashboards, and visualizations.
- Design and implement data models (schema objects).
- Apply security and access control.
- SQL basics, BI/reporting concepts, Data analysis fundamentals.
- Understand core concepts and terminology
Elicitation Workshop
Preparing for elicitation; Question formulation; Active listening techniques; Handling difficult stakeholders; Mock...
- Functional Requirements Documentation.
- Understand core concepts and terminology
- Build hands-on projects with real-world applications
- Apply industry best practices and design patterns
- Develop practical problem-solving skills
Technical Interview Preparation
Comprehensive preparation for technical interviews at top companies. Covers coding challenges, system design, and behavioral interview techniques.
- Solve coding problems under time constraints.
- Design scalable systems for common interview scenarios.
- Structure behavioral answers with the STAR framework.
- Communicate technical decisions clearly and concisely.
- Build confidence through mock interview practice.
Leadership for Tech Managers
Transition from individual contributor to effective engineering manager. Covers team building, 1:1s, performance management, and technical decision-making.
- Run effective 1:1s and team meetings.
- Provide constructive feedback and coaching.
- Make technical decisions at the team level.
- Manage stakeholder expectations and priorities.
- Build high-performing engineering teams.
Technical Writing for Engineers
Write clear technical documentation, API guides, and architecture decision records. Covers writing style, tooling, and documentation-as-code workflows.
- Write clear and concise technical documentation.
- Create API documentation with OpenAPI/Swagger.
- Produce architecture decision records (ADRs).
- Use documentation-as-code workflows with Git.
- Structure knowledge bases for engineering teams.
Apache Kafka & Event Streaming
Build real-time event-driven architectures with Apache Kafka. Producers, consumers, Kafka Streams, Connect, and Schema Registry for scalable data pipelines.
- Design event-driven streaming architectures
- Build producers and consumers with Java/Python
- Implement stream processing with Kafka Streams
- Configure Kafka Connect for data integration
- Manage schemas with Schema Registry
dbt & Modern Data Stack
Transform raw data into analytics-ready models with dbt. Testing, documentation, incremental models, and integration with Snowflake, BigQuery, and Redshift.
- Build modular data transformation pipelines with dbt
- Write and run data quality tests
- Implement incremental materialization strategies
- Generate automated documentation from models
- Integrate dbt into CI/CD workflows
Data Warehouse Design & Modeling
Design scalable data warehouse architectures. Star and snowflake schemas, slowly changing dimensions, ETL patterns, and modern lakehouse approaches.
- Design star and snowflake schemas for analytics
- Implement slowly changing dimension strategies
- Build ETL pipelines with data quality checks
- Choose between warehouse and lakehouse architectures
- Optimize query performance with partitioning and indexing
MongoDB for Developers
Master MongoDB document database — schema design, aggregation pipelines, indexing, replication, and Atlas cloud deployment for modern applications.
- Design document schemas for real-world applications
- Build complex queries with the aggregation framework
- Implement indexing strategies for query optimization
- Configure replication and sharding for scalability
- Deploy and manage clusters on MongoDB Atlas
PostgreSQL Advanced Administration
Advanced PostgreSQL — performance tuning, replication, partitioning, backup strategies, extensions, and high-availability architecture for production workloads.
- Tune PostgreSQL for high-performance workloads
- Configure streaming replication and failover
- Implement table partitioning for large datasets
- Write stored procedures with PL/pgSQL
- Design backup and disaster recovery strategies
Apache Flink Real-Time Processing
Build low-latency stream processing applications with Apache Flink. Event time, watermarks, stateful processing, and exactly-once guarantees at scale.
- Build stream processing pipelines with Flink DataStream API
- Handle event time and watermarks correctly
- Implement stateful processing with checkpointing
- Use Flink SQL for real-time analytics
- Deploy and monitor Flink applications in production
Data Governance & Quality Engineering
Implement data governance frameworks, data quality monitoring, lineage tracking, cataloging, and compliance strategies for enterprise data platforms.
- Design enterprise data governance frameworks
- Implement automated data quality checks
- Track data lineage across pipelines
- Build data catalogs for discoverability
- Ensure compliance with GDPR and data regulations
Java Enterprise Development
Build production Java applications — Spring Framework, REST APIs, JPA/Hibernate, testing, and deployment. From core Java to enterprise-grade backends.
- Write clean, modern Java with records and sealed classes
- Build REST APIs with Spring Boot
- Implement data persistence with JPA and Hibernate
- Write unit and integration tests with JUnit 5
- Package and deploy applications with Maven and Docker
Go Programming for Backend Engineers
Learn Go from scratch — goroutines, channels, HTTP servers, database access, and building production CLI tools and microservices.
- Write idiomatic Go with structs, interfaces, and error handling
- Build concurrent applications with goroutines and channels
- Create REST APIs with Gin framework
- Implement gRPC services for microservice communication
- Connect to databases and manage migrations
Rust Systems Programming
Master Rust ownership model, lifetimes, traits, and concurrency. Build safe, fast systems software — CLI tools, web servers, and data processors.
- Understand ownership, borrowing, and lifetimes deeply
- Build safe concurrent programs without data races
- Create CLI tools with clap and serde
- Write async code with Tokio runtime
- Implement zero-cost abstractions with traits and generics
TypeScript & Node.js Fullstack
End-to-end TypeScript — type system deep dive, Node.js APIs with Express, React frontends, and full-stack project deployment.
- Master TypeScript type system and generics
- Build type-safe REST APIs with Express and Zod
- Create React frontends with TypeScript
- Implement authentication and authorization flows
- Deploy full-stack applications with Docker
C# & .NET Development
Build modern applications with C# and .NET 8. Web APIs, Entity Framework, Blazor frontends, and Azure deployment for enterprise solutions.
- Write modern C# with records, pattern matching, and LINQ
- Build Web APIs with ASP.NET Core minimal APIs
- Implement data access with Entity Framework Core
- Create interactive UIs with Blazor
- Deploy .NET applications to Azure App Service
System Design & Architecture
Design scalable distributed systems — load balancing, caching, message queues, database sharding, and microservice patterns for high-traffic applications.
- Design systems that scale to millions of users
- Choose appropriate database and caching strategies
- Implement message queue patterns for async processing
- Apply CAP theorem and consistency trade-offs
- Architect microservices with proper service boundaries
Microservices with Spring Boot
Build production microservices — service discovery, API gateway, circuit breakers, distributed tracing, and event-driven communication with Spring Cloud.
- Design and decompose monoliths into microservices
- Implement service discovery with Eureka
- Build API gateways with Spring Cloud Gateway
- Add resilience with circuit breakers and retries
- Set up distributed tracing with Zipkin and Sleuth
AI Agents with CrewAI & AutoGen
Build multi-agent AI systems with CrewAI and Microsoft AutoGen. Agent orchestration, tool use, memory, and production deployment for autonomous workflows.
- Design multi-agent architectures for complex tasks
- Build autonomous agents with CrewAI framework
- Implement agent collaboration with Microsoft AutoGen
- Add custom tools and memory to agents
- Deploy and monitor agent systems in production
Multimodal AI & Vision-Language Models
Work with models that understand text, images, and audio. GPT-4V, Gemini, CLIP, and building applications that combine multiple modalities.
- Build applications with vision-language models
- Implement image understanding and generation pipelines
- Combine speech, text, and vision in unified workflows
- Fine-tune multimodal models for domain-specific tasks
- Evaluate multimodal system performance and safety
AI for Cybersecurity
Apply AI and ML to threat detection, malware analysis, anomaly detection, and automated incident response. Build intelligent security operations.
- Build ML models for network anomaly detection
- Implement AI-powered threat intelligence analysis
- Automate incident response with AI agents
- Detect malware using machine learning classifiers
- Design AI-augmented security operations workflows
Federated Learning & Privacy-Preserving AI
Train ML models across distributed data without sharing raw data. Federated learning, differential privacy, and secure computation for sensitive applications.
- Implement federated learning with Flower framework
- Apply differential privacy to model training
- Design privacy-preserving ML pipelines
- Handle non-IID data distributions across clients
- Deploy federated systems for healthcare and finance use cases
AI-Powered Test Automation
Use AI to generate test cases, self-heal broken tests, and automate visual regression. Integrate LLMs into your testing pipeline for faster, smarter QA.
- Generate test cases automatically with LLMs
- Build self-healing test suites that adapt to UI changes
- Implement AI-powered visual regression testing
- Integrate LLMs into CI/CD test pipelines
- Reduce test maintenance with intelligent locators
Generative AI for Data Analytics
Use LLMs to query databases with natural language, generate reports, automate EDA, and build conversational BI dashboards for non-technical stakeholders.
- Build natural language to SQL query interfaces
- Automate exploratory data analysis with LLMs
- Create conversational dashboards with Streamlit
- Generate automated reports from raw datasets
- Implement data-aware chatbots for business users
Site Reliability Engineering (SRE)
Apply Google SRE principles — SLOs, error budgets, incident management, observability, and toil reduction for reliable production systems.
- Define and track SLOs and error budgets
- Build observability stacks with Prometheus and Grafana
- Implement effective incident management processes
- Identify and eliminate operational toil
- Design systems for reliability and graceful degradation
Data Visualization with Tableau & Power BI
Create compelling data stories with Tableau and Power BI. Dashboard design, DAX formulas, calculated fields, and publishing interactive reports.
- Build interactive dashboards in Tableau and Power BI
- Write DAX measures and calculated fields
- Apply data storytelling principles for executive audiences
- Connect to live data sources and schedule refreshes
- Publish and share reports with row-level security
Cybersecurity Fundamentals
Core cybersecurity concepts — network security, encryption, identity management, vulnerability assessment, and compliance frameworks for IT professionals.
- Understand common attack vectors and defense strategies
- Implement encryption and secure communication protocols
- Configure identity and access management controls
- Perform basic vulnerability assessments
- Apply security compliance frameworks (ISO 27001, SOC 2)
Agile & Scrum Master Certification Prep
Master Agile methodology and Scrum framework. Sprint planning, retrospectives, backlog management, and team facilitation for Scrum Master certification.
- Facilitate Scrum ceremonies effectively
- Manage product backlogs and sprint planning
- Coach teams on Agile principles and practices
- Use Jira for Agile project tracking
- Prepare for Professional Scrum Master (PSM) exam
Cloud Cost Optimization & FinOps
Reduce cloud spend with FinOps practices. Cost allocation, rightsizing, reserved instances, spot strategies, and building a cost-aware engineering culture.
- Implement FinOps practices across engineering teams
- Analyze and allocate cloud costs by team and project
- Rightsize compute and storage resources
- Optimize spend with reserved instances and spot strategies
- Build cost dashboards and automated alerting
Azure AI Agents
Build intelligent AI agents using Azure AI Services, Bot Framework, and Azure OpenAI. From agent architectures to production deployment.
- Understand AI agent architectures and design patterns
- Build intelligent agents using Azure AI Services
- Implement conversational AI with Azure Bot Service
- Integrate Azure OpenAI for advanced agent capabilities
- Deploy and monitor production-ready AI agents
Databricks Advanced Analytics & Lakehouse
Master Delta Lake, Delta Live Tables, advanced analytics, and data governance on Databricks. Build end-to-end data pipelines at scale.
- Master Databricks workspace and cluster management
- Implement Delta Lake for reliable data lakehouse
- Build end-to-end data pipelines with Delta Live Tables
- Perform advanced analytics and ML with Databricks
- Optimize performance and manage data governance
PySpark Performance Optimization & Pipeline Development
Master Spark internals, advanced optimization techniques, and production-ready streaming and batch pipeline architectures.
- Master Spark internals and execution model
- Implement advanced optimization techniques
- Design scalable data pipeline architectures
- Handle data skew and performance bottlenecks
- Build production-ready streaming and batch pipelines
AI RAG - Retrieval-Augmented Generation
Build production RAG pipelines with LangChain and LlamaIndex. Document processing, vector databases, semantic search, and evaluation.
- Understand RAG architecture and components
- Implement document processing and chunking strategies
- Configure vector databases for semantic search
- Build production RAG pipelines with LangChain/LlamaIndex
- Evaluate and optimize RAG system performance
LLM Models & Integration
Master prompt engineering, build LLM-powered applications, create complex agent workflows with LangChain, and deploy production LLM systems.
- Understand LLM architectures and capabilities
- Master prompt engineering and optimization techniques
- Implement LLM-powered applications with various APIs
- Build complex workflows with LangChain and agents
- Deploy and monitor production LLM systems
Advanced Microsoft Fabric
End-to-end data engineering, real-time analytics, data science workflows, and Power BI integration on Microsoft Fabric.
- Master Microsoft Fabric architecture and workloads
- Implement end-to-end data engineering with Fabric
- Build real-time analytics solutions
- Develop data science workflows with Fabric notebooks
- Implement data governance and security best practices
Azure Cloud & AI Services
Master Azure AI portfolio — computer vision, speech, language, Azure OpenAI, and Azure Machine Learning. Design enterprise AI architectures.
- Master Azure AI services portfolio and integration
- Implement computer vision and speech solutions
- Build intelligent applications with Azure OpenAI
- Deploy ML models using Azure Machine Learning
- Design enterprise AI architectures on Azure
Data Analytics Foundations
Build core analytics thinking with data literacy, KPI basics, descriptive analysis, and practical reporting workflows for business teams.
- Frame business questions into measurable metrics
- Clean and organize tabular datasets
- Build descriptive summaries and trend analysis
- Create basic KPI dashboards and reports
- Present analytical findings to stakeholders
SQL for Data Analytics
Learn production-style SQL for analysts: joins, CTEs, window functions, cohort analysis, and reusable query patterns for reporting teams.
- Write clean SQL for analytics use cases
- Use joins and CTEs for reusable reporting logic
- Apply window functions for ranking and cohorts
- Optimize queries for analyst workflows
- Build metric-ready datasets for BI tools
Python for Data Analytics
Use Python to automate analysis, clean data, build visualizations, and package repeatable analytical workflows for business reporting.
- Clean and transform data using pandas
- Automate recurring analytical workflows
- Build charts for trend and variance analysis
- Validate data quality before reporting
- Prepare outputs for BI and stakeholder dashboards
Product Analytics & Experimentation
Analyze funnels, cohorts, retention, and experiments to support product decisions with robust metrics and experimentation practices.
- Design product metrics and event taxonomies
- Analyze funnels, retention, and engagement cohorts
- Interpret A/B test outcomes responsibly
- Communicate product insights for prioritization
- Build reusable product analytics reporting workflows
Customer Analytics & Segmentation
Use customer data to build segmentation models, churn signals, and campaign performance analytics for marketing and growth teams.
- Build customer segments from behavioral data
- Measure funnel and campaign performance
- Identify churn indicators and retention opportunities
- Create decision-ready stakeholder dashboards
- Translate analytics into growth actions
Data Analytics Professional Pathway
End-to-end analyst pathway covering SQL, Python, KPI design, dashboarding, stakeholder communication, and capstone business analysis.
- Build analytics workflows from raw data to decisions
- Develop metric definitions and governance basics
- Create executive and operational dashboards
- Perform capstone business impact analysis
- Present findings with stakeholder-ready narratives
Business Intelligence Foundations & KPI Design
Learn BI fundamentals, KPI frameworks, dimensional thinking, and dashboard design basics for reliable business reporting.
- Define useful KPIs and reporting dimensions
- Avoid misleading dashboards and vanity metrics
- Design stakeholder-specific dashboard layouts
- Prepare datasets for BI consumption
- Document metric definitions and assumptions
Power BI Essentials
Build production-grade Power BI reports with data modeling, Power Query, DAX fundamentals, and publishing workflows.
- Build Power BI semantic models and relationships
- Create DAX measures for business metrics
- Design interactive reports and drill-downs
- Publish and manage datasets and reports
- Apply performance and usability best practices
Tableau Analyst Professional
Create Tableau dashboards, calculated fields, storytelling views, and governed workbook publishing for analytics teams.
- Build interactive dashboards in Tableau
- Use calculations and LOD expressions effectively
- Design narrative dashboards for stakeholders
- Publish and govern workbooks in shared environments
- Optimize Tableau workbook performance
Looker & Semantic Modeling
Build governed BI experiences using semantic modeling, reusable metrics, and secure self-service reporting workflows with Looker.
- Design governed semantic models and reusable metrics
- Build explores and self-service BI workflows
- Implement row-level security and access controls
- Reduce metric inconsistency across teams
- Publish scalable BI experiences for business users
Enterprise BI with Microsoft Fabric & Power BI
Build enterprise BI pipelines and dashboards using Microsoft Fabric, OneLake, semantic models, and Power BI deployment practices.
- Build end-to-end BI workflows on Microsoft Fabric
- Design enterprise semantic models and governance
- Publish Power BI reports with controlled access
- Integrate lakehouse and warehouse BI use cases
- Operationalize enterprise reporting environments
Business Intelligence Professional Pathway
Professional BI pathway covering KPI design, semantic modeling, dashboard engineering, governance, and stakeholder-ready decision support.
- Design enterprise BI architectures and reporting layers
- Build governed metrics and semantic models
- Create dashboards for operations and leadership
- Implement BI delivery standards and QA workflows
- Lead BI projects from requirements to rollout
Ready to Transform Your Skills?
Contact us for custom training, enterprise licensing, or to discuss your team's learning objectives.