Learning Path Curriculum

MLOps Engineer

Pipelines, tracking, registries and serving — the infrastructure discipline behind machine learning in production.

7 target skills27 interactive tasks live100% Free Sandbox

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Modules & Skills

Curriculum Modules

Master the technical stack through sequence-oriented modules with real hands-on lab tasks.

SKILL 01

Python

4 Tasks

The working language of ML engineering.

0/4 done

Topics Covered (2)

• Environments & Dependencies2 tasks

• Data Wrangling with Pandas2 tasks

Curriculum TrackOpen Skill Lessons ↗

SKILL 02

ML Pipelines

4 Tasks

Data and training pipelines that rerun reliably.

0/4 done

Topics Covered (2)

• Your First Pipeline2 tasks

• The Training Pipeline2 tasks

Curriculum TrackOpen Skill Lessons ↗

SKILL 03

Model Tracking

4 Tasks

Experiments, metrics and reproducibility.

0/4 done

Topics Covered (3)

• Experiment Tracking with MLflow1 task

• The Model Registry1 task

• Model Lineage & Promotion2 tasks

Dedicated Model Tracking & MLflow Hub →Open Skill Lessons ↗

SKILL 04

Model Serving

4 Tasks

Serving models as dependable APIs.

0/4 done

Topics Covered (3)

• High-Throughput Inference with vLLM1 task

• The Serving Stack1 task

• Scaling & Rollouts2 tasks

Dedicated Model Serving & vLLM Hub →Open Skill Lessons ↗

SKILL 05

Monitoring

4 Tasks

Drift, latency and the health of ML systems.

0/4 done

Topics Covered (2)

• Metrics & Drift2 tasks

• Alerting & Retraining2 tasks

Curriculum TrackOpen Skill Lessons ↗

SKILL 06

Feature Stores

4 Tasks

The same features, computed once, served to training and to production alike.

0/4 done

Topics Covered (2)

• Defining Features2 tasks

• Online & Offline Parity2 tasks

Curriculum TrackOpen Skill Lessons ↗

SKILL 07

Data Quality & Labeling

3 Tasks

Contracts that fail the build, and labels that humans can actually agree on.

0/3 done

Topics Covered (2)

• Contracts & Checks2 tasks

• Labeling & Ground Truth1 task

Curriculum TrackOpen Skill Lessons ↗