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MLOps track

Real-Time Inference

Online serving, latency, autoscaling and online feature lookup.

6 concepts4 labs1 challenges
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Concepts

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

1

Real-Time Inference

Concept · Hands-on lab included

2

Streaming Inference

Concept · Hands-on lab included

3

Online Servers and Engines

Concept · Hands-on lab included

4

Latency and Throughput Tuning

Concept · Hands-on lab included

5

Request Replay and Canaries

Concept · Hands-on lab included

6

Real-Time Governance and Skew

Concept · Hands-on lab included

Practice

Hands-on labs

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

Browse all labs

Score a Stream with State

40 min · intermediate · Streaming Inference

Tune Batching for Latency

35 min · intermediate · Online Servers and Engines

Run a Measured Tuning Experiment

40 min · advanced · Latency and Throughput Tuning

Build a Skew Watchdog

35 min · advanced · Real-Time Governance and Skew

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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 · Real-Time Inference
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