Introduction
MLOps for Automating ML Lifecycle streamlines the machine learning lifecycle, enabling seamless transitions from model development to deployment and maintenance. This course delves into advanced MLOps practices, focusing on automation, collaboration, and scalability. It equips professionals with the skills to build resilient pipelines, ensure model reproducibility, and optimize ML workflows for production environments.

Prerequisites

1. Strong understanding of machine learning principles and model building.
2. Proficiency in Python and ML libraries like TensorFlow, PyTorch, or Scikit-learn.
3. Familiarity with DevOps concepts (CI/CD, containerization) and tools.
4. Basic experience with cloud platforms like AWS, Azure, or GCP.

Table of Contents

1. Introduction to Advanced MLOps
   1.1 MLOps Beyond Basics: Challenges and Opportunities
   1.2 Understanding the Machine Learning Lifecycle(Ref: MLOps Fundamentals: Introduction to Machine Learning Operations)
   1.3 Evolution of MLOps: From Manual Processes to Full Automation
2. Automating Data Pipelines
   2.1 Data Ingestion: Batch and Streaming Workflows
   2.2 Automating Data Cleaning, Transformation, and Feature Engineering
   2.3 Managing Data Versioning and Lineage with Tools Like DVC
   2.4 Addressing Data Drift and Quality Issues in Production
3. Model Training Automation
   3.1 Building Automated Training Pipelines with Orchestration Tools
   3.2 Hyperparameter Tuning Using Grid Search, Bayesian Optimization, and AutoML
   3.3 Experiment Tracking and Model Selection with MLflow or Weights & Biases
   3.4 Ensuring Reproducibility in Training Workflows
4. CI/CD for Machine Learning
   4.1 Implementing Continuous Integration for ML Code and Models
   4.2 Continuous Deployment Strategies: Blue-Green, Canary, and Shadow Deployments
   4.3 Testing ML Pipelines: Unit, Integration, and System Tests
   4.4 Case Study: Building a CI/CD Pipeline for ML Model Deployment
5. Containerization and Orchestration in MLOps
   5.1 Packaging Models with Docker for Consistent Deployment
   5.2 Orchestrating Pipelines with Kubernetes and Airflow
   5.3 Managing Dependencies and Environments Across Teams
   5.4 Cloud-native Deployment with AWS Sagemaker, Azure ML, and GCP AI Platform
6. Monitoring and Maintenance of MLOps for Automating ML Lifecycle
   6.1 Model Monitoring: Detecting Drift, Decay, and Anomalies
   6.2 Automating Alerts and Retraining Pipelines(Ref: Mastering ASP.NET Core: Advanced Techniques for Web Development)
   6.3 Logging and Metrics: Best Practices for ML Observability
   6.4 Ensuring High Availability and Fault Tolerance for ML Systems
7. Scaling MLOps Pipelines
   7.1 Distributed Training and Serving for Large-Scale Models
   7.2 Scaling Pipelines with Cloud and On-Premise Resources
   7.3 Leveraging Pre-trained Models and Transfer Learning in MLOps
   7.4 Strategies for Cost Optimization and Resource Management
8. Governance, Compliance, and Security of MLOps for Automating ML Lifecycle
   8.1 Data Governance and Privacy Regulations in MLOps
   8.2 Ensuring Model Explainability and Addressing Bias
   8.3 Role-based Access Control (RBAC) for ML Systems
   8.4 Building Secure ML Workflows with Encryption and Secure APIs
9. Integrating Business Needs with MLOps
   9.1 Defining KPIs and Metrics for ML Systems in Production
   9.2 Collaboration Between Data Scientists, Engineers, and Stakeholders
   9.3 Aligning MLOps Practices with Business Goals and ROI
   9.4 Case Study: Scaling MLOps to Support Business Objectives
10. Hands-on Labs and Capstone Projects
    10.1 Automating End-to-End ML Pipelines with Airflow and MLflow
    10.2 Building a Real-time Monitoring Dashboard for Deployed Models
    10.3 CI/CD Workflow for Deploying Multi-cloud Models
    10.4 Final Capstone: Implementing an MLOps Workflow for a Real-world Problem

Conclusion
Mastering MLOps empowers teams to optimize the entire machine learning lifecycle, ensuring efficient, scalable, and secure production systems. By automating workflows and aligning technical processes with business needs, this course prepares you to lead ML deployments that deliver measurable value.

Reference