MLOps Engineer

Read this in one sitting and you already have the map of what production ML actually requires: which of its core practices becomes its own deep-dive step next, so nothing later feels like an unexplained tool.

The containerization and packaging row from step 1 becomes real here: once a model and its dependencies live in one image, the cluster in step 3 and every pipeline and serving tool after it all have the same portable unit to work with.

This is where the learning curve everyone warns you about shows up: pods and controllers reconciling declarative YAML instead of you running commands by hand. Expect to keep this cheat sheet open through the rest of the path, since every deployment, pipeline, and serving tool from here on assumes a cluster underneath it.

Takes the image you built in step 2 and puts it on rails: a commit now triggers build, test, and deploy automatically instead of you running docker push by hand every time.

Turns the cluster and cloud resources from step 3 into version-controlled code instead of a wiki page of manual setup steps, so the environment your pipeline deploys into is exactly reproducible.

Gives the experiment tracking and model registry rows from step 1 an actual home: every training run, metric, and promoted model version now lives somewhere your whole team can find and compare it.

Chains the training, evaluation, and deployment steps you've built so far into one scheduled DAG that reruns itself on new data, instead of you kicking off each stage by hand - the same orchestration muscle the feature pipelines in step 8 lean on.

Solves the mismatch the DAGs from step 7 can create on their own: a feature computed one way in the training pipeline and a slightly different way at request time, which is how models silently underperform in production without anyone touching the code.

Once the feature store in step 8 hands a request the right inputs, this is what turns a slow PyTorch or TensorFlow model into a portable graph that runs fast on whatever hardware is actually available at serving time.

Everything you've automated so far, from the pipeline in step 4 to the registry in step 6, now needs a paper trail: which frameworks like the EU AI Act or NIST's AI RMF actually apply to what you're shipping, and who signs off before it goes live.

Nothing you built earlier catches this on its own: data drift, concept drift, and prediction drift all creep in quietly after launch, and the monitoring here is what tells you before your users do. Expect to revisit this dashboard for the life of every model you ship.

Take this if your team runs on AWS: SageMaker packages the pipeline, registry, and monitoring ideas from earlier sections into one managed platform, trading some of the do-it-yourself control from steps 4 through 7 for less operational overhead.

Aim here if you're serving models at real GPU scale: Triton batches concurrent requests and hands them to whichever backend, the ONNX Runtime from step 9, TensorRT, or PyTorch, actually needs the hardware, squeezing far more throughput out of the same cluster.

Head toward this one if you're in a regulated industry: the governance frameworks from step 10 often demand you can actually explain a specific prediction, not just document that a process exists, and SHAP and LIME are how you do that.

Pulls every earlier step into one running system: the containers from step 2 and cluster from step 3 host it, its pipelines replace the DAGs from step 7, its registry extends what MLflow started in step 6, and its serving layer hands off to the same ONNX and Triton backends from steps 9 and 13. This is the platform a real MLOps team owns.