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BSDA5014 - Machine Learning Operations (MLOps)
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BSDA5014 - Machine Learning Operations (MLOps)
657 words
3 min read
| Field | Value |
|---|---|
| Course Code | BSDA5014 |
| Level | Degree Level Course |
| Credits | 4 |
| Type | Pre-requisites: |
| Pre-requisites | BSCS1002 - Programming in Python |
📖 Description
This course aims to give students a comprehensive understanding of Machine Learning Operations (MLOps). MLOps is a paradigm to deploy and maintain machine learning models in production environments reliably and efficiently. The course will cover various aspects of MLOps, including model development, model deployment, monitoring, and optimization. Students will gain hands-on experience with popular MLOps tools and frameworks, enabling them to manage machine learning workflows effectively to deliver robust and scalable ML solutions.
🗓️ Weekly Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to MLOps: Overview of MLOps and its significance: Key challenges in deploying and managing ML models in production, Comparison of traditi |
| Week 2 | ML Pipelines & Data Management: Overview of data engineering tools and practices, Data management for ML models, ML pipeline automation. DVC overview |
| Week 3 | Data Management - Part 2 : Feature Stores. Motivation, role in ML and Generative AI applications, benefits for MLOps. Feast overview. |
| Week 4 | CI/CD for ML Models: Use of version control systems like Git for model development, automated testing & validation, model delivery strategies |
| Week 5 | Machine Learning Model Development: Comparison of development of small models vs large models including LLMs, tracking model training & experimentatio |
| Week 6 | Model Deployment and Serving: Overview of containerization and orchestration technologies and various other cloud-based deployment options, Deployment |
| Week 7 | Monitoring and Performance Optimization: Techniques for monitoring model performance in production, Logging and error tracking for ML systems, Perform |
| Week 8 | ML Security: Overview of Security considerations for ML, field of MLSecOps, tooling options. |
| Week 9 | ML Governance: Overview of model explainability and ethical considerations in ML deployments, tracking bias |
| Week 10 | MLOps for LLMs - Part 1 : Model Versioning for base models and fine-tuned variants, CI/CD specifics |
| Week 11 | MLOps for LLMs - Part 2 : Accuracy, Performance vs Cost tradeoffs, Observability, Security & Governance (Bias, Toxicity, Explainability) |
| Week 12 | Closing topics: Advanced topics of Federated learning, edge inferencing; Putting it all together |
📚 Books & Resources
Prescribed Books
The following are the suggested books for the course:
Building Machine Learning Powered Applications: Going from Idea to Product - Emmanuel Ameisen - O’Reilly publication Reliable Machine Learning: Applying SRE Principles to ML in Production - Chen, Murphy, Parisa - O’Reilly publication Machine Learning Engineering in Action - Ben Wilson - O’Reilly publication Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems - Martin Kleppmann - O’Reilly publication
📝 About the Instructors
Rangarajan Vasudevan
Co-Founder & Chief Data Officer
,
Lentra.ai
Rangarajan Vasudevan is the Co-Founder & CDO of Lentra.ai, India’s fastest growing lending cloud. He did “big data” & “data science” before it was fashionable, building data-native applications across industries and geographies over 15+ years.
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Ranga joined Lentra by way of an acquisition in June 2022 of his company TheDataTeam, creators of Cadenz.ai customer intelligence platform. Prior to founding TheDataTeam, Ranga served as Director, Big Data with Teradata Corporation’s international business unit. Ranga joined Teradata via the acquisition of Aster Data Systems, where he was a founding engineer and co-invented a company-defining, patented, pattern recognition algorithm. He is a recipient of both the Distinguished Engineer (R&D) and Consulting Excellence awards while at Teradata.
Ranga has degrees in Computer Science from the University of Michigan and IIT Madras.
Faculty mail : rangarajan[at]study[dot]iitm[ac]in
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Other courses by the same instructor:
BSDA5001 -
Introduction to Big Data