Topics:

• Programming Foundations

  Advanced Python for MLOps

  Software Engineering Practices

  Linux and Shell Scripting

  Git Advanced Features

  Testing with Pytest

• DevOps Fundamentals

  DevOps Culture and Practices

  CI/CD Concepts

  Infrastructure as Code

  Configuration Management

  Monitoring and Logging

Topics:

• MLOps Fundamentals

  MLOps vs DevOps vs DataOps

  MLOps Lifecycle

  Maturity Models

  Key Challenges

  Team Structure

• MLOps Principles

  Automation Strategies

  Continuous Integration

  Continuous Delivery

  Continuous Training

  Continuous Monitoring

Topics:

• Cloud Platforms

  AWS for MLOps

  Azure ML Services

  GCP Vertex AI

  Multi-Cloud Strategies

  Cost Management

• Containerization

  Docker for ML

  Container Registries

  Docker Compose

  Security Best Practices

  Multi-Stage Builds

Topics:

• Kubernetes Fundamentals

  Pods and Deployments

  Services and Ingress

  ConfigMaps and Secrets

  Persistent Volumes

  RBAC and Security

• ML on Kubernetes

  GPU Scheduling

  Distributed Training

  Kubeflow Platform

  KServe/KFServing

  Custom Operators

Topics:

• IaC Tools

  Terraform Fundamentals

  Ansible Automation

  CloudFormation

  Pulumi

  GitOps Practices

• Infrastructure Management

  Resource Provisioning

  State Management

  Multi-Environment Setup

  Disaster Recovery

  Security Hardening

Topics:

• Data Pipeline Architecture

  Batch vs Streaming

  ETL/ELT Patterns

  Data Quality Checks

  Error Handling

  Pipeline Monitoring

• Data Processing Tools

  Apache Spark

  Apache Beam

  Dask and Ray

  Pandas at Scale

  Data Validation

Topics:

• Feature Pipelines

  Feature Extraction

  Feature Transformation

  Feature Selection

  Automated Feature Engineering

  Feature Validation

• Feature Stores

  Feature Store Architecture

  Feast Implementation

  Online vs Offline Features

  Feature Versioning

  Point-in-Time Correctness

Topics:

• Experiment Tracking

  MLflow Setup

  Weights & Biases

  Neptune.ai

  Metrics Logging

  Artifact Management

• Reproducibility

  Environment Management

  Data Versioning (DVC)

  Code Versioning

  Configuration Management

  Random Seed Control

Topics:

• Training Pipelines

  Pipeline Orchestration

  Airflow for ML

  Prefect and Dagster

  Task Dependencies

  Error Recovery

• Distributed Training

  Data Parallelism

  Model Parallelism

  Horovod Setup

  PyTorch Distributed

  Cost Optimization

Topics:

• Optimization Strategies

  Grid and Random Search

  Bayesian Optimization

  Hyperband and BOHB

  Population-Based Training

  Neural Architecture Search

• Automation Tools

  Optuna Framework

  Ray Tune

  Katib (Kubeflow)

  Azure ML HyperDrive

  Custom Solutions

Topics:

• CI for ML

  Code Quality Checks

  ML-Specific Testing

  Data Validation Tests

  Model Validation Tests

  Security Scanning

• CD for ML

  Deployment Pipelines

  GitHub Actions for ML

  GitLab CI/CD

  Jenkins Pipelines

  ArgoCD

Topics:

• ML Testing

  Unit Testing for ML

  Integration Testing

  Model Testing

  Performance Testing

  A/B Testing Framework

• Validation Pipelines

  Model Quality Gates

  Data Quality Gates

  Business Metrics Validation

  Compliance Checks

  Automated Validation

Topics:

• Model Serialization

  Model Formats (ONNX, SavedModel)

  Containerisation

  Dependency Management

  Version Tagging

  Registry Management

• Deployment Artifacts

  Docker Images

  Helm Charts

  Model Artifacts

  Configuration Files

  Documentation

Topics:

• Deployment Patterns

  Blue-Green Deployment

  Canary Deployment

  Rolling Updates

  Feature Flags

  Shadow Deployment

• Rollout Management

  Progressive Rollouts

  Traffic Splitting

  Rollback Strategies

  Health Checks

  Monitoring Integration

Topics:

• Serving Frameworks

  TensorFlow Serving

  TorchServe

  ONNX Runtime

  Triton Inference Server

  BentoML

• Serving Optimization

  Batching Strategies

  Caching Mechanisms

  Hardware Acceleration

  Load Balancing

  Auto-scaling

Topics:

• LLM Challenges

  Scale Differences

  Cost Considerations

  Latency Requirements

  Memory Constraints

  Safety Concerns

• LLM Infrastructure

  GPU Cluster Management

  Memory Optimization

  Network Requirements

  Storage Solutions

  Vendor Selection

Topics:

• Prompt Management

  Prompt Versioning

  Prompt Templates

  Prompt Registry

  A/B Testing Prompts

  Performance Metrics

• Prompt CI/CD

  Prompt Validation

  Automated Testing

  Deployment Pipelines

  Progressive Rollouts

  Monitoring

Topics:

• Fine-tuning Pipelines

  Data Preparation

  Training Infrastructure

  LoRA/QLoRA Setup

  Instruction Tuning

  RLHF Pipelines

• Distributed LLM Training

  Model Parallelism

  Pipeline Parallelism

  ZeRO Optimization

  DeepSpeed Integration

  FSDP Setup

Topics:

• Serving Infrastructure

  vLLM Deployment

  TGI Setup

  TensorRT-LLM

  Quantization (INT8/INT4)

  Flash Attention

• Optimization Techniques

  KV Cache Management

  Continuous Batching

  Speculative Decoding

  Token Management

  Cost Optimization

Topics:

• RAG Operations

  Vector DB Management

  Embedding Management

  Index Updates

  Retrieval Monitoring

  Context Management

• Agent Systems

  Tool Management

  Memory Systems

  State Management

  Workflow Orchestration

  Error Recovery

Topics:

• Metrics and KPIs

  Model Performance Metrics

  Business Metrics

  System Metrics

  Data Quality Metrics

  Cost Metrics

• Monitoring Stack

  Prometheus Setup

  Grafana Dashboards

  ELK Stack

  Datadog Integration

  Custom Solutions

Topics:

• Model Drift Detection

  Data Drift Monitoring

  Concept Drift

  Feature Drift

  Performance Degradation

  Alert Strategies

• Data Quality Monitoring

  Schema Validation

  Statistical Monitoring

  Anomaly Detection

  Freshness Checks

  Completeness Metrics

Topics:

• Logging and Tracing

  Structured Logging

  Distributed Tracing

  Correlation IDs

  Log Aggregation

  Trace Analysis

• Model Explainability

  SHAP Implementation

  LIME Integration

  Feature Importance

  Model Cards

  Bias Detection

Topics:

• Performance Monitoring

  Latency Tracking

  Throughput Monitoring

  Resource Utilization

  Queue Depths

  Cache Performance

• Optimization Strategies

  Bottleneck Analysis

  Performance Tuning

  Capacity Planning

  Load Testing

  Stress Testing

Topics:

• Alert Management

  Alert Design

  Escalation Policies

  On-Call Rotations

  Runbooks

  Alert Fatigue

• Incident Response

  Incident Detection

  Response Procedures

  Root Cause Analysis

  Post-Mortems

  Continuous Improvement

Topics:

• ML Security Threats

  Model Stealing

  Data Poisoning

  Adversarial Attacks

  Prompt Injection

  Privacy Attacks

• Security Measures

  Access Control

  Encryption

  Secure APIs

  Container Security

  Secrets Management

Topics:

• Regulatory Compliance

  GDPR Implementation

  HIPAA Requirements

  SOC 2 Compliance

  Industry Standards

  Audit Requirements

• Model Governance

  Model Risk Management

  Approval Workflows

  Documentation Standards

  Version Control

  Change Management

Topics:

• Platform Engineering

  Multi-Tenancy

  Resource Management

  Service Catalog

  Self-Service Capabilities

  Developer Experience

• Team Collaboration

  Role-Based Access

  Knowledge Sharing

  Documentation

  Training Programs

  Best Practices

Topics:

• Cost Optimization

  Resource Allocation

  Budget Tracking

  Spot Instance Usage

  Reserved Capacity

  Waste Reduction

• FinOps for ML

  Chargeback Models

  Cost Attribution

  ROI Analysis

  Vendor Management

  Optimization Strategies

Topics:

• Edge MLOps

  Edge Deployment

  Model Optimization

  OTA Updates

  Offline Capabilities

  Edge Monitoring

• Emerging Practices

  Federated Learning Ops

  AutoML Operations

  Green MLOps

  Quantum ML Ops

  Future Trends