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MLOps track

ML Cost Optimization

GPU spend, spot instances, auto-scaling and unit economics.

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

ML Cost Optimization

Concept · Hands-on lab included

2

GPU Cost Drivers

Concept · Hands-on lab included

3

Autoscaling and Spot Instances

Concept · Hands-on lab included

4

ML Workload Profiling

Concept · Hands-on lab included

5

Storage and Data Costs

Concept · Hands-on lab included

6

ML Cost Budgeting and Anomaly Detection

Concept · Hands-on lab included

Practice

Hands-on labs

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

Browse all labs

Profile and Rightsize a Workload

40 min · intermediate · GPU Cost Drivers

Elastic Cost Simulator

35 min · intermediate · Autoscaling and Spot Instances

Profile a Training Loop

35 min · intermediate · ML Workload Profiling

Continue

Next modules

Keep your learning streak going with related modules.

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ML Lifecycle

The full ML lifecycle and the operational discipline MLOps adds.

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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.

DevOpsForge - MLOps · ML Cost Optimization
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