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From training models to running them in production — the full MLOps journey

Follow data scientists and ML engineers as they discover that training a model is only 10% of the work — the other 90% is everything else.

9 chapters ~110 min total

Beginner 2 chapters

Meera trains her first model

What is machine learning really — and what happens after training?

👤 Meera ⏱ 10 min

Ravi loses his best model

Experiment tracking, MLflow, and DVC — keeping track of what actually worked

👤 Ravi ⏱ 10 min

Intermediate 2 chapters

Deploying Meera's chatbot

Model serving with FastAPI and Docker — from notebook to real API

👤 Meera ⏱ 12 min

Vikram teaches the model to see

Feature engineering, data pipelines and why raw data is never model-ready

👤 Vikram ⏱ 11 min

Advanced 3 chapters

The model that went wrong silently

Model monitoring, data drift, and why your model degrades without you knowing

👤 Kavya ⏱ 14 min

Building the full ML pipeline

Kubeflow, Airflow, and the end-to-end MLOps system that runs itself

👤 Ravi & Meera ⏱ 15 min

Meera runs the experiment

A/B testing ML models, statistical significance and shadow deployment

👤 Meera ⏱ 10 min

Expert 2 chapters

Running LLMs in production

LLMOps — cost, latency, evals, and RAG pipelines at scale

👤 Ananya ⏱ 16 min

Rohan cuts the GPU bill by 60 percent

Model quantisation, batching, caching and serving ML cheaply at scale

👤 Rohan ⏱ 12 min

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