MLOps
Taking a trained model from a notebook to something that keeps running in production: versioning data, tracking experiments, building the pipelines that retrain and redeploy it, and watching it once it is live.
What is MLOps?The ML lifecycleMLOps vs. DevOpsReproducibility and the training/serving gapData versioningData pipelines for MLFeature storesData lineageExperiment trackingModel registriesHyperparameter tuningModel evaluation and validationContainerizing modelsEnvironment and dependency managementModel packaging formatsContinuous integration for MLTesting ML systemsContinuous trainingContinuous delivery for modelsWorkflow orchestrationInfrastructure as codeCloud ML platformsDistributed training infrastructureModel serving patternsDeployment strategiesScaling inferenceEdge and on-device deploymentMonitoring model performanceData and concept driftExplainability and interpretabilityML governance and complianceIncident response for ML systems
What MLOps actually solves
- What is MLOps?Why a model that works in a notebook still needs an entire operational discipline before it can be trusted in production.
- The ML lifecycleThe data-to-monitoring loop every ML system runs, and why MLOps treats it as one automated cycle instead of six separate hand-offs.
- MLOps vs. DevOpsWhat actually changes when the thing you're operating has data as a dependency, non-deterministic behavior, and failures that don't throw errors.
- Reproducibility and the training/serving gapWhy a model that trained perfectly can behave differently in production — and the concrete mismatches between training and serving code that cause it.
Data you can trust and reproduce
- Data versioningTracking dataset changes the way Git tracks code, so a training run can be reproduced against the exact data it used.
- Data pipelines for MLBuilding reliable pipelines that feed consistent data into training and inference, and what goes wrong when the two pull from separate paths.
- Feature storesCentralizing engineered features so training and serving read the exact same logic and values, closing one of the top causes of training/serving skew.
- Data lineageTracing where a piece of data or feature came from and what transformed it — essential for debugging predictions and for audit and compliance.
Running and comparing experiments
- Experiment trackingLogging every run's config, metrics, and artifacts so "which setup got 94%?" has a real answer instead of a guess.
- Model registriesWhy "what did we train" and "what is allowed to run in production" need to be two different, tracked lists.
- Hyperparameter tuningGrid search, random search, and Bayesian optimization — systematic ways to search a config space instead of tuning by hand.
- Model evaluation and validationOffline metrics, holdout sets, and the gate a model has to clear between "it trained" and "it's allowed to ship."
Making a model reproducible to ship
- Containerizing modelsPackaging a trained model and its dependencies into a Docker image so it runs identically on your laptop, in CI, and in production.
- Environment and dependency managementPinning library versions with lockfiles and matching CUDA driver versions, the two halves of making training and serving environments actually match.
- Model packaging formatsPortable serialization formats like ONNX let you train in one framework and serve with a different, often faster, runtime.
Automating the path to a deployable model
- Continuous integration for MLCI for ML means testing code, data, and model config on every change — not just running the old test suite against a new commit.
- Testing ML systemsAccuracy isn't a test suite — ML systems need data tests, training-code tests, and behavioral tests on specific known cases.
- Continuous trainingAutomatically retraining when data arrives or performance drops — and the guardrails that stop an automated retrain from shipping a worse model.
- Continuous delivery for modelsAutomating the path from a validated model artifact to a deployable one — CD for software, applied to models.
The infrastructure underneath it all
- Workflow orchestrationWhy multi-step ML pipelines need a scheduler that understands dependencies, retries, and failure — not a cron job and a prayer.
- Infrastructure as codeWhy declaring ML infrastructure in version-controlled files, instead of clicking around a cloud console, is the difference between reproducible and lost.
- Cloud ML platformsWhat managed platforms like SageMaker and Vertex AI actually buy you, and the real tradeoff against building the equivalent on raw infrastructure.
- Distributed training infrastructureWhy training sometimes needs more than one GPU, and what data and model parallelism actually split across machines to make that possible.
Getting a model into production safely
- Model serving patternsBatch, online, and streaming inference are different architectures with different failure modes, not interchangeable ways to run the same model.
- Deployment strategiesCanary, blue-green, and shadow deployments each limit a different kind of risk when rolling out a new model version — not interchangeable safety nets.
- Scaling inferenceAutoscaling, batching, caching, and CPU vs. GPU choices are the levers that keep a model serving fast and cheap once real traffic hits it.
- Edge and on-device deploymentRunning a model on the device itself trades accuracy and model size for no network dependency, lower latency, and better privacy.
Keeping it trustworthy once it is live
- Monitoring model performanceWhy a model needs two separate kinds of monitoring — infra health and whether its predictions are still actually good — not just one.
- Data and concept driftWhy incoming data or the real relationship a model learned can shift after training, silently degrading accuracy a healthy service won't show.
- Explainability and interpretabilitySHAP and LIME — how each technique explains why a model made a specific prediction, for debugging drift and for building trust.
- ML governance and complianceModel cards, approval workflows, and audit trails — the documentation and process layer that makes a production model accountable.
- Incident response for ML systemsDetection, rollback to a known-good model, and postmortems — what to actually do when a production model starts failing anyway.