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MLOps track

Feature Stores

Online/offline stores, feature freshness and Feast concepts.

6 concepts3 labs1 challenges
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Concepts

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

1

Feature Stores

Concept · Hands-on lab included

2

Offline Feature Stores

Concept · Hands-on lab included

3

Online Feature Servers

Concept · Hands-on lab included

4

Freshness and Consistency

Concept · Hands-on lab included

5

Feature Store Architecture

Concept · Hands-on lab included

6

Preventing Training-Serving Skew

Concept · Hands-on lab included

Practice

Hands-on labs

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

Browse all labs

Materialize and Point-in-Time Join

30 min · intermediate · Offline Feature Stores

Serve Features From a Key-Value Store

25 min · intermediate · Online Feature Servers

Enforce Feature Freshness SLAs

25 min · intermediate · Freshness and Consistency

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Next modules

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The full ML lifecycle and the operational discipline MLOps adds.

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MLflow tracking, run metadata, reproducibility and comparing experiments.

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Dataset Versioning

DVC, data lineage and immutability for training data.

DevOpsForge - MLOps · Feature Stores
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