MLOps Training in Anna Nagar

• Python programming and object-oriented programming (OOP)
• NumPy, Pandas, and advanced data manipulation
• SQL fundamentals for data extraction
• Linux commands and shell scripting basics
• Git and GitHub version control workflows
• Virtual environments (venv/conda) and dependency management

Beginner → Intermediate Build: Configure environments, manage dependencies, and apply Python with SQL for technical foundations.

• Supervised and unsupervised learning concepts
• Regression and classification algorithms
• Data preprocessing, cleaning, and feature engineering
• Model training, validation, and testing strategies
• Model evaluation metrics and hyperparameter tuning
• Building reproducible Scikit-learn pipelines

Intermediate Build: Train, validate, and optimize ML models with reproducible Scikit-learn pipelines.

• Introduction to MLOps and its business importance
• Key differences between DevOps, DataOps, and MLOps
• The end-to-end machine learning lifecycle
• MLOps architecture patterns and maturity levels
• Principles of reproducibility, scalability, and automation
• Comprehensive model lifecycle management

Intermediate → Advanced Build: Design an MLOps architecture with reproducibility, scalability, and automated lifecycle management.

• Data ingestion, transformation, and validation techniques
• ETL/ELT pipelines and automated data quality checks
• Data leakage prevention strategies
• Dataset and model versioning best practices
• Git, DVC (Data Version Control), and Git LFS
• Data lineage tracking and reproducible workflows

Advanced Build: Create reproducible ETL pipelines with Git, DVC, and lineage tracking for version-controlled workflows.

• MLflow architecture and experiment tracking
• Logging parameters, metrics, and model artifacts
• Experiment comparison and reproducibility validation
• Model packaging, registration, and staging
• Model versioning and environment promotion
• Weights & Biases (W&B) fundamentals for deep learning

Expert Build: Track experiments with MLflow, manage model registries, and integrate W&B for deep learning workflows.

• Designing end-to-end machine learning pipelines
• Pipeline components, dependencies, and orchestration
• Apache Airflow fundamentals for workflow management
• Kubeflow Pipelines introduction for Kubernetes-native ML
• Pipeline scheduling, automation, and resource allocation
• Pipeline testing, error handling, and automated retraining triggers

Advanced Build: Implement automated ML pipelines with Airflow orchestration and Kubeflow retraining triggers.

• Docker architecture, images, and container fundamentals
• Dockerfile creation, optimization, and image management
• Containerizing machine learning applications
• FastAPI and Flask fundamentals for ML serving
• REST API development, documentation, and testing
• Real-time (online) and batch model inference patterns

Advanced Build: Containerize ML models and deploy APIs with FastAPI for real-time and batch inference.

• CI/CD/CT concepts specifically tailored for MLOps
• Automated testing for ML applications (data, model, and code)
• GitHub Actions or Jenkins workflow automation
• Model quality validation and automated deployment gates
• Automated builds and container registry (Docker Hub/ECR) integration
• Release management, blue-green deployments, and rollback strategies

Expert Build: Implement CI/CD pipelines with automated testing, container registry integration, and rollback strategies.

• Kubernetes (K8s) architecture and core components
• Pods, Deployments, Services, ConfigMaps, and Secrets
• Scaling and managing distributed ML workloads
• Cloud storage, networking, and identity management (IAM)
• Hands-on overview of AWS SageMaker, Azure ML, or Google Vertex AI
• Terraform fundamentals for Infrastructure as Code (IaC) automation

Capstone Build: Deploy ML workloads on Kubernetes with cloud-native services and automate infrastructure using Terraform.

• Model performance and prediction monitoring in production
• Data drift and concept drift detection methodologies
• Data quality and continuous model health checks
• Implementing Evidently AI, Prometheus, and Grafana
• Setting up alerts, centralized logging, and incident handling
• Automated retraining, validation, and safe rollback procedures

Advanced Build: Deploy monitoring pipelines with Evidently AI, Prometheus, and Grafana for drift detection and automated retraining.

• Model serving optimization and scalable inference (e.g., vLLM, Ray Serve)
• Feature stores (e.g., Feast) and feature consistency
• Model security, governance, explainability (SHAP/LIME), and compliance
• LLMOps fundamentals and Large Language Model deployment
• RAG pipelines, embeddings, and vector databases (Pinecone, ChromaDB)
• LLM evaluation, tracing (LangSmith/Langfuse), and cost monitoring
• AI agent deployment, monitoring (AgentOps), and human-in-the-loop approval

Expert Build: Deploy scalable LLM pipelines, integrate RAG with vector databases, and monitor AI agents with AgentOps.

• End-to-end automated MLOps pipeline from scratch
• Customer churn prediction with scalable model deployment
• Model drift detection and automated retraining workflow
• Kubernetes-based ML model serving and load balancing
• Production RAG application with comprehensive LLMOps
• AgentOps monitoring, evaluation, and workflow automation
• GitHub portfolio building, resume optimization, mock interviews, and final deployment demonstration

Capstone Build: Deliver enterprise-ready MLOps, LLMOps, and AgentOps projects with a documented portfolio and career preparation.