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AI Engineering track

ML Foundations

What ML engineers assume — models, training, features and evaluation basics.

6 concepts3 labs
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Concepts

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

1

The ML Pipeline in Brief

Concept · Hands-on lab included

2

Features, Labels & Datasets

Concept · Hands-on lab included

3

Train, Validation & Test Splits

Concept · Hands-on lab included

4

Regression & Classification

Concept · Hands-on lab included

5

Baselines & Benchmarks

Concept · Hands-on lab included

6

Evaluation Metrics That Matter

Concept · Hands-on lab included

Practice

Hands-on labs

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

Browse all labs

Profile a Dataset for Leaks

20 min · beginner · Features, Labels & Datasets

Split Without Leaking

25 min · intermediate · Train, Validation & Test Splits

Score a Classifier Honestly

25 min · intermediate · Evaluation Metrics That Matter

Continue

Next modules

Keep your learning streak going with related modules.

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Python

The Python skills AI engineering builds on — typing, async, packaging and testing.

0%

APIs

HTTP, REST, auth, rate limits, SDK patterns and API design for AI systems.

0%

Data Handling

Preparing documents, JSON, embeddings pipelines and data validation for AI.

DevOpsForge - AI Engineering · ML Foundations
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