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Module 1

AI ML Refresher for Production

• Python Refresher, ML Fundamentals Recap
• Supervised Vs Unsupervised Learning
• Evaluation Metrics (Classification & Regression)
• Bias-Variance In Production
• Dataset Shift Basics
• Intro To DL & LLM System Boundaries (Conceptual Positioning Only)

Module 2

DevOps & Cloud Foundations for AI

• Devops Philosophy
• CI/CD Pipeline
• Git Workflows
• Linux CLI Essentials
• Cloud Service Models (AWS/Azure/GCP)
• Cloud Security Basics
• Cost Awareness
• Integrating ML Lifecycle Into CI/CD

Module 3

Containerization & Orchestration For AI

• Docker Architecture
• Dockerfiles
• Container Networking
• Kubernetes Core Concepts (Pods, Services, Deployments)
• Autoscaling (HPA)
• GPU Scheduling Fundamentals For Inference Workloads
• Scaling ML Services

Module 4

Infrastructure as Code & Environment Automation

• IaC Principles
• Terraform Fundamentals
• Environment Reproducibility
• Secrets Management
• Infrastructure Versioning
• Provisioning ML-Ready Environments
• Advanced Terraform Modules & Workspaces
• Hybrid IaC + Kubernetes Integration
• Policy-as-Code & Compliance Automation
• Disaster Recovery & Scaling Automation

Module 5

ML Lifecycle & Pipeline Automation

• ML System Architecture
• Data Ingestion & Validation
• Feature Engineering Pipelines
• Feature Store Integration
• DAG Design
• Pipeline Orchestration Patterns
• Automation Checkpoints Before Deployment
• Reliable ML Pipeline Deployment

Module 6

Experiment Tracking & Reproducibility

• Experiment Tracking Principles
• Hyperparameter Logging
• Artifact Tracking
• Data & Model Versioning
• Reproducibility Workflows
• Governance Checkpoints In Experimentation

Module 7

Model Packaging & Deployment

• Model Serialization (Pickle, ONNX)
• REST API Design For ML
• Batch Vs Real-Time Inference
• API Performance Testing
• Inference Latency Considerations
• Deployment Patterns (Containerized Serving), Scalability with Tools

Module 8

CI/CD for ML Systems

• Continuous Training Pipelines
• Automated Validation Gates
• Model Approval Workflows
• Trigger-Based Deployment
• Rollback Strategies
• Integration With Orchestration Pipelines

Module 9

Monitoring, Drift & Governance

• Model Performance Monitoring
• Data Drift & Concept Drift Detection
• A/B Testing Strategies
• Retraining Triggers
• Governance Dashboards
• Interpreting Monitoring Metrics For Business Decisions

Module 10

Model Registry & Enterprise Lifecycle

• Model Registry Design
• Approval Workflows
• Artifact Lineage
• Audit Trails
• Compliance Tracking
• Enterprise AI Lifecycle Management

Module 11

Enterprise LLM Hosting & Optimization

• LLM Serving Architectures (Managed APIs Vs Self-Hosted)
• Inference Optimization Basics
• VLLM Fundamentals
• Quantization Concepts (INT8/4-Bit Overview)
• GPU Utilization
• Throughput Benchmarking
• Latency Vs Cost Trade-Offs
• Production LLM Deployment Patterns

Module 12

AI Agent Architecture & Foundations

• Fundamentals of AI Agents and Architectures
• Agent Runtime Hosting, Service Isolation, Concurrency
• Docker/Kubernetes-Based Deployment, Observability, Audit Logging
• Operational Safety Controls
• Security and Compliance
• Resource Allocation and Infrastructure-Level Scaling of Enterprise Agent Based Systems

Module 13

Enterprise AI Hosting Strategies

• Advanced Inference Optimization
• Distributed LLM Serving
• Prompt Caching Strategies
• Enterprise-Grade APIs For Scalable, High-Performance and Cost-Efficient Production Deployments.

Module 14

LLM Cost Engineering & Secure Deployment

• Token Cost Modeling
• Caching Strategies
• Rate Limiting
• API Gateways
• Access Control
• Secure Model Endpoints
• Cost-Performance Dashboards
• Enterprise Proxy Patterns For LLM Usage, SLA/SLO Modeling
• Cost-Performance Trade-Offs
• High Availability Architectures
• Multi-Region Deployment Concepts
• Disaster Recovery Strategies
• Architecture Case Studies For ML & LLM Systems

Module 15

Vector Infrastructure & Retrieval Infrastructure

• Vector Database Architecture
• Indexing Strategies
• Sharding & Scaling
• Embedding Lifecycle Management
• Embedding Drift Detection
• Storage Tiering (Hot Vs Cold)
• Operational Considerations For Large-Scale Vector Systems

Module 16

RAG Ops

• Fundamentals of RAG
• Vector Databases and Indexing
• Secure Retrieval Workflows
• Monitoring and Observability
• RAG Governance
• RAGOps Integration
• Vector Database Governance
• Production-Ready RAG Case Studies

Module 17

AI Security & Compliance

• AI System Security and Robustness
• Compliance Frameworks
• Audit Logging
• Data Privacy for Secure, Compliant, and Resilient Enterprise AI Systems.
• Enterprise AI Systems Against Adversarial Threats While Ensuring Strict Alignment with Global Compliance Frameworks.
• Secure Deployment
• Audit Governance
• Privacy‑Preserving Practices

Module 18

Capstone

End-to-End Enterprise ModelOps and LLM Infrastructure (CI/CD automation, model registry, monitoring, drift detection, serving optimisation, cost governance, architecture documentation, deployment review)