Free to read ยท Three levels
MLOps: evidence, lifecycle contracts, and reliable delivery
A complete course in operating the full prediction process, from data and experiment contracts to evaluation, delivery, observability, recovery, and retirement.
Start learningIn this course
Learn MLOps as the controlled operation of an evidence-based prediction process. Eighteen developed lessons connect task ownership, data quality, information availability, lineage, reproducible training inputs, model packages, pipeline reuse, acceptance gates, registry identity, delivery, feature parity, bounded exposure, monitoring, feedback, provenance, incident response, and resource-aware lifecycle decisions. Each lesson contains five developed sections, two explained examples, three diagnostic mistakes, and two fully worked exercises.
Every executable example is a tiny deterministic Python standard-library program using supplied synthetic in-memory data. No model download, neural framework, external dataset, orchestration platform, registry, database, server, GPU, cloud resource, credential, or live service is required or used. Platform documentation supports conceptual distinctions; the examples do not claim to execute those products or establish real model quality, distributed guarantees, causal effects, or production performance.
What you will learn
- Define prediction, ownership, data, feature-time, and fallback contracts.
- Trace artifacts, executions, comparable evaluation, and selection history.
- Explain DAG dependencies, cache validity, retry effects, immutable versions, and mutable selection.
- Validate complete inference behavior, controlled exposure, observability, delayed labels, and retraining decisions.
- Separate integrity, origin, access, recovery, resource evidence, and ongoing lifecycle ownership.
Your learning path
Three levels
Prediction and evidence foundations
Task ownership, schema quality, information boundaries, lineage, replay inputs, and complete packages.
- Task ownership and lifecycle contracts
- Data schemas and quality gates
- Splits, availability, and leakage boundaries
- Experiment records and lineage
- Reproducible training boundaries
- Model packages and inference contracts
Pipelines and controlled delivery
Dependencies, reusable evidence, acceptance, version selection, delivery patterns, feature parity, and exposure.
- Pipeline DAGs, caching, and idempotency
- Evaluation slices and acceptance gates
- Registry versions, aliases, and promotion
- Batch and online delivery contracts
- Training-serving skew and feature time
- Rollouts, shadowing, and rollback
Observation, recovery, and lifecycle decisions
Telemetry, delayed outcomes, feedback, provenance, incidents, resource budgets, and retirement.
- Service and model observability
- Drift, label delay, and performance evidence
- Retraining triggers and feedback loops
- Artifact provenance and access boundaries
- Incident response and recovery evidence
- Cost budgets and complete lifecycle review
Who this is for
- Developers, analysts, and ML practitioners who need to make a prediction lifecycle traceable and reviewable.
- Learners with machine-learning foundations preparing to implement platform-specific workflows under explicit contracts.
Before you start
- Python functions, lists, dictionaries, assertions, exceptions, and basic JSON handling.
- Machine-learning concepts including baselines, held-out evaluation, preprocessing, leakage, and model selection.
- Basic software delivery concepts including version identity, testing, failure handling, and service boundaries.