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WEEK 1
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Introduction to MLOps: Overview of MLOps and its significance: Key challenges in deploying and managing ML models in production, Comparison of traditional software development, DevOps and MLOps, Key components of MLOps, MLOps workflow, Landscape of MLOps tools and technologies.
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WEEK 2
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ML Pipelines & Data Management: Overview of data engineering tools and practices, Data management for ML models, ML pipeline automation. DVC overview.
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WEEK 3
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Data Management - Part 2 : Feature Stores. Motivation, role in ML and Generative AI applications, benefits for MLOps. Feast overview.
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WEEK 4
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CI/CD for ML Models: Use of version control systems like Git for model development, automated testing & validation, model delivery strategies
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WEEK 5
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Machine Learning Model Development: Comparison of development of small models vs large models including LLMs, tracking model training & experimentation using MLFlow, hyperparameter tuning and optimization.
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WEEK 6
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Model Deployment and Serving: Overview of containerization and orchestration technologies and various other cloud-based deployment options, Deployment strategies for different environments (cloud, edge, on-premises), Model serving options
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WEEK 7
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Monitoring and Performance Optimization: Techniques for monitoring model performance in production, Logging and error tracking for ML systems, Performance optimization and scaling strategies
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WEEK 8
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ML Security: Overview of Security considerations for ML, field of MLSecOps, tooling options.
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WEEK 9
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ML Governance: Overview of model explainability and ethical considerations in ML deployments, tracking bias
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WEEK 10
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MLOps for LLMs - Part 1 : Model Versioning for base models and fine-tuned variants, CI/CD specifics
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WEEK 11
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MLOps for LLMs - Part 2 : Accuracy, Performance vs Cost tradeoffs, Observability, Security & Governance (Bias, Toxicity, Explainability)
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WEEK 12
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Closing topics: Advanced topics of Federated learning, edge inferencing; Putting it all together
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