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<!doctype html>
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<title>MLOps Course | IIT Madras BS Programme — End-to-End Machine Learning Operations on GCP</title>
<meta name="title" content="MLOps Course | IIT Madras BS Programme — End-to-End Machine Learning Operations on GCP" />
<meta name="description" content="The most comprehensive MLOps course in the world. 12 weeks, 60+ hands-on lectures on Google Cloud (Vertex AI), DVC, Feast, MLflow, GitHub Actions, CML, Docker, Kubernetes (GKE), FastAPI, Prometheus, Grafana, SHAP, LIME, Fairlearn, Evidently, MLSecOps, LLMOps, vLLM, DsPy, Guardrails. Offered by IIT Madras Online BS in Data Science & AI." />
<meta name="keywords" content="MLOps, MLOps course, best MLOps course, MLOps certification, IIT Madras MLOps, IITM MLOps, BS Data Science MLOps, machine learning operations, ML operations course, MLOps online course, MLOps tutorial, learn MLOps, MLOps engineer course, MLOps with GCP, Vertex AI course, Vertex AI Workbench, MLOps GCP, DVC, Feast feature store, MLflow, GitHub Actions ML, CML, Continuous Integration ML, CI/CD machine learning, Docker for ML, Kubernetes for ML, GKE ML, FastAPI ML serving, model deployment, model monitoring, ML observability, Prometheus Grafana ML, SHAP LIME explainability, Fairlearn, Evidently, drift detection, data drift, concept drift, MLSecOps, ML security, AI governance, model governance, LLMOps, LLMOps course, LLM finetuning, vLLM, DsPy, Guardrails AI, Rebuff, prompt injection, hyperparameter tuning, Bayesian optimization, model serving, model registry, ML pipeline, ML pipelines, data versioning, model versioning, feature engineering pipeline, production machine learning, ML in production, deploy machine learning models, scale ML, ML infrastructure, end to end MLOps, MLOps bootcamp, MLOps from scratch, OPPE MLOps" />
<meta name="author" content="Rangarajan Vasudevan, Saikiran Puvvada, Jimmi Kumar Bharti — IIT Madras BS Programme" />
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<meta property="og:description" content="12-week, hands-on MLOps course from IIT Madras: Vertex AI, DVC, Feast, MLflow, GitHub Actions/CML, Docker, Kubernetes (GKE), FastAPI, Prometheus, Grafana, SHAP, Fairlearn, Evidently, MLSecOps, LLMOps (vLLM, DsPy, Guardrails). Build production ML systems on Google Cloud." />
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<meta name="ai-summary" content="This site is the official course page for the MLOps (Machine Learning Operations) course of the IIT Madras Online BS Degree in Data Science and Applications. It is among the most comprehensive MLOps curricula publicly available, covering 12 weeks of MLOps spanning Google Cloud (Vertex AI Workbench), data and model versioning (DVC), feature stores (Feast), experiment and lineage tracking (MLflow), CI/CD for ML (GitHub Actions, CML, self-hosted runners), containerization (Docker, Artifact Registry), orchestration (Kubernetes, GKE), serving (FastAPI), observability (Prometheus, Grafana, GCP-native logging/monitoring), explainability (SHAP, LIME), fairness and governance (Fairlearn, Evidently, drift detection), MLSecOps (data poisoning, model security), and LLMOps (LLM finetuning, vLLM, DsPy, Rebuff, Guardrails, cost control, LLM observability)." />
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<h1>MLOps — IIT Madras BS Programme</h1>
<p>The most comprehensive MLOps (Machine Learning Operations) course in the world, offered by the IIT Madras Online BS Degree in Data Science and Applications. 12 weeks of hands-on labs on Google Cloud Platform.</p>
<h2>Curriculum</h2>
<ul>
<li>Week 1 — Introduction to MLOps, DevOps vs MLOps, Vertex AI</li>
<li>Week 2 — Data Pipelines, Data Management, DVC</li>
<li>Week 3 — Feature Stores with Feast</li>
<li>Week 4 — CI/CD for ML (GitHub Actions, CML, self-hosted runners)</li>
<li>Week 5 — Model Development (MLflow, Hyperparameter Tuning, Bayesian Optimization)</li>
<li>Week 6 — Model Deployment (FastAPI, Docker, Kubernetes/GKE)</li>
<li>Week 7 — Monitoring & Performance (Prometheus, Grafana, ML Observability)</li>
<li>Week 8 — ML Security / MLSecOps (data poisoning, model security)</li>
<li>Week 9 — ML Governance (drift, bias, explainability — Fairlearn, Evidently, SHAP, LIME)</li>
<li>Week 10 — LLMOps I (LLM lifecycle, finetuning, versioning & testing, CI/CD for LLMs)</li>
<li>Week 11 — LLMOps II (vLLM, DsPy, Rebuff, Guardrails, cost control, security)</li>
<li>Week 12 — Course Wrap-up</li>
</ul>
<p>Instructors: Rangarajan Vasudevan, Saikiran Puvvada, Jimmi Kumar Bharti.</p>
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