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MLOps track

ML Lifecycle

The full ML lifecycle and the operational discipline MLOps adds.

6 concepts2 labs1 challenges
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Concepts

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Work through the concepts in order, or jump to what you need.

1

The ML Lifecycle

Concept · Hands-on lab included

2

Scoping ML Projects

Concept · Hands-on lab included

3

From Data to Model

Concept · Hands-on lab included

4

Lifecycle Stages in Production

Concept · Hands-on lab included

5

Artifact Management

Concept · Hands-on lab included

6

Automating the Lifecycle

Concept · Hands-on lab included

Practice

Hands-on labs

Apply what you've learned in guided, production-style environments.

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Stage a Model with a Local Registry

30 min · intermediate · Lifecycle Stages in Production

Bundle an Artifact with a Manifest

25 min · intermediate · Artifact Management

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Next modules

Keep your learning streak going with related modules.

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Experiment Tracking

MLflow tracking, run metadata, reproducibility and comparing experiments.

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Dataset Versioning

DVC, data lineage and immutability for training data.

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Model Versioning

Model artifacts, semantic versions and provenance.

DevOpsForge - MLOps · ML Lifecycle
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