What is MLOps?

• Define MLOps and its differences from plain DevOps
• Map the extra practices ML adds: data/model versioning, drift, registry
• Diagnose what breaks when MLOps practices are missing

• Most ML value fails in operations, not model quality — a great model that nobody can ship, reproduce or monitor is worthless.
• Without versioning you cannot reproduce a result, which breaks audits, debugging and rollback.
• Production data differs from training data and drifts over time; only an operations loop catches it.
• MLOps turns one-off notebook successes into a repeatable business capability.

You can cook a great meal at home (train a model in a notebook), but a restaurant needs recipes, portion control, health inspections, supplier management and a shift that survives staff changes. MLOps is the restaurant: every dish reproducible, every ingredient versioned, every plate quality-checked.

• Version everything that goes into a model: code, data, features, hyperparameters and the trained artifact.
• Automate the train-evaluate-register path so a good candidate becomes a versioned, deployable model without manual babysitting.
• Deploy with gates: test suites, shadow or canary rollout, then switch traffic.
• Monitor live behavior: prediction quality proxies, drift sensors, latency and data quality.
• Close the loop: alerts trigger retraining, retraining produces a new candidate, and the cycle repeats.

• A fintech's fraud model has a monitored AUC-by-hour plus a drift sensor; when either breaches, a ticket auto-opens for review and retraining.
• An e-commerce team ships a two-model recommender twice a week through an automated pipeline with shadow scoring before cutover.
• A clinical risk model logs every prediction and input cohort for audit while alerting on missing-feature spikes.

• Treat a model as an artifact with lineage, not a file — registry plus metadata is non-negotiable.
• Automate the boring path first: training-to-registry should be one button with zero manual steps.
• Monitor a proxy for model quality, because true labels usually arrive late.
• Design for rollback: every promoted model version is instantly repointable.
• Make failures loud and visible: model decay should page someone, not hide in dashboards.

• Automating deployment while leaving training as a manual notebook ritual — a half-operationalized system.
• Versioning code but not data and hyperparameters, so results can't be reproduced.
• Monitoring infrastructure (CPU, memory) but not the model's actual behavior.
• Skipping governance until an audit or incident forces it — retrofitting is painful.

• When a production metric drops, first check data drift, then code changes, then model artifact — in that order.
• Confirm the registry pointer matches what the serving fleet reports; 'wrong version' causes phantom regressions.
• Lineage is your debugger: exact data + code + params reproduce any incident in development.
• Log the inputs and outputs at the API boundary for every prediction; you cannot debug unseen decisions.