Quest 12 of 15
Model deployment & MLOps basics
Ship models: export formats (ONNX, torch.save), FastAPI inference, batch jobs, Docker sketch, and monitoring prediction drift.
Start here
This module builds on earlier parts, but every important idea is explained in context. Ship models: export formats (ONNX, torch.save), FastAPI inference, batch jobs, Docker sketch, and monitoring prediction drift. As you read, connect each concept to the worked example and ask what a person must still decide.
Big idea
Ship models: export formats (ONNX, torch.save), FastAPI inference, batch jobs, Docker sketch, and monitoring prediction drift.
Learn one idea at a time
Read, explore, then mark each idea when you can explain it.
Idea 1 of 8
Export the fitted preprocessing and model together or version them as one release. Loading a model with a different encoder, label map, or feature order creates train-serving skew even when the API remains online.
Live interactive diagrams
Tap nodes, stages, or cards to explore — these diagrams match this module’s ideas.
Train to serve path
Fit preprocess + model; save joblib/torch.
Choose a deep dive
Open the topics you want to explore. The detail stays folded until you need it.
Deep dive 1Train-serve skew kills trust
If serving preprocess differs from training, accuracy collapses silently — use the same Pipeline object.
Deep dive 2Version everything
Tag deployments with git SHA, data version, and metric snapshot for rollback.
Deep dive 3Worked example: Model deployment & MLOps basics
API returns good results in staging but wrong labels in prod. A responsible response is: Diff preprocess code, model version, and sample live requests. Correct debug path. Use this case to separate what the technology contributes from what people contribute. The team should compare the intended outcome with a baseline where applicable, record important assumptions, and keep a clear route to correct or stop the process.
Deep dive 4Local check for Model deployment & MLOps basics
Ask whether the examples, data, and assumptions fit your school, company, or community. Generic demos often miss local names, laws, connectivity, and languages.
Deep dive 5Deploy the pipeline
Same preprocess in train and serve — or metrics lie in production.
One-minute challenge
Connect this lesson to real life
Name one situation where this idea could help, and one thing a person should still check.
Explore a real-world example
Use the arrows to connect the idea to a visible situation.
Photo example
Serve path
Client → API validation → pipeline → model → logged response.
Key terms
Tap a term to flip and read the definition.
Optional further learningFree textbooks and trusted online resources
These sources informed the course structure. Use them to revisit a concept or study it in more depth.
Ready check
Tick each idea only when you could explain it without looking back.
Ready for practice? Dockerise a FastAPI app that loads joblib pipeline and predicts on POST JSON.
Extra context (audience, logistics, curriculum notes)
Built for: Software developers and technical learners — Python and coding required.
Formats: Learn scroll · Practice interactions · Quiz · Optional chat lab
Module 12 — Model deployment & MLOps basics
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