Learn MLOps: a practical learning path

If you want to learn MLOps, do it in the order production systems depend on each other: Python and Git first, then containers and CI, then tracking, serving and monitoring. Each stage below has a concrete project, because MLOps is a practice, and you only learn it by shipping something and watching it break.

This path assumes you can already train a basic model or are learning that alongside it. It does not require a cloud account or a paid platform; everything below runs on a laptop with free tools, mostly open source. If you want the concept first, read what is MLOps. When you are ready to test yourself, the MLOps interview questions are a good check.

Stage 1: Python you can maintain

MLOps work is mostly software engineering in Python. Notebook-only skills are not enough, because pipelines and services are programs.

• Functions, modules and packages; virtual environments and dependency management; type hints; reading stack traces.
• Testing with pytest: write tests for a data cleaning function before you move on.
• The official Python tutorial covers the language itself, and the scikit-learn user guide covers a model API you will reuse in every later stage.

Project. Take a notebook that trains a model and turn it into a package with a train.py that reads a config file, a predict function, and tests. Delete the notebook's hidden state: running train.py on a clean checkout must reproduce the result.

Stage 2: Git and reproducibility

Version control is the backbone of everything after it. Learn branching, merging, rebasing, reviewing diffs and tagging releases from the free Pro Git book. Then learn what Git is bad at: large files and datasets. DVC stores data pointers in Git and the data itself in separate storage, so a commit can identify both code and data.

Project. Put your project under Git with DVC tracking the dataset. Check out a commit from last week and reproduce that exact model. If you cannot, find out what you forgot to record.

Stage 3: Docker

Containers solve the "works on my machine" problem, and almost every deployment target runs them. Work through the Docker get-started guide, then practice the things that matter for ML: pinning dependencies, building small images, using multi-stage builds, keeping large model files out of the image, and running as a non-root user.

Project. Containerize both training and inference. The training container should run your train.py; the inference container should start a server and respond to requests. Build both from a lockfile.

Stage 4: Continuous integration

CI runs your checks on every change so that broken code does not reach your main branch. GitHub Actions is free for public repositories and enough to learn on. Build a pipeline that installs dependencies, runs linting and tests, builds the container, and runs a smoke test against it. Then add the ML-specific checks: a data validation step (schema, ranges, null rates), a small training run on a sample to catch crashes, and a model test that compares metrics on a fixed evaluation set against a minimum threshold and the current model.

Project. Make a pull request that deliberately degrades the model, for example by dropping a feature. CI should fail and say why.

Stage 5: Experiment tracking and a registry

Now make runs comparable and models traceable. MLflow Tracking records parameters, metrics and artifacts for each run, and its model registry gives versions with lineage back to the run that produced them, plus movable aliases such as "champion". You can run the server locally.

Project. Log every training run, including the Git commit and data version. Register the best model. Then write the serving code so it loads the model by alias instead of by file path, and practice promoting and rolling back by moving the alias.

Stage 6: Pipelines and orchestration

Once training is a script you trust, run it automatically: on a schedule, on new data, or on request. At first a cron job or a CI workflow is enough. When steps depend on each other, learn a workflow orchestrator; Kubeflow Pipelines is one that runs on Kubernetes, and there are others. Concepts transfer between tools: tasks, dependencies, retries, parameters, caching and artifacts passed between steps. Do not start here. Orchestrators add operational weight, and beginners often adopt one before they have anything worth orchestrating.

Project. Build a pipeline with four steps: validate data, train, evaluate, register. It should stop if validation or evaluation fails.

Stage 7: Serving

Learn to serve a model behind an API. FastAPI is a widely used Python framework for this: you define the request and response with typed models, and it validates inputs and generates documentation. Practice:

• loading the model once at startup, not on every request,
• input validation and clear error responses,
• a health endpoint, and logging of request, prediction and model version,
• load testing, to find out your latency and throughput limits.

Then learn how to run it somewhere real. The Kubernetes Deployment concept, and rolling updates in particular, is the foundation under most model serving platforms, including KServe. Study release patterns in model deployment strategies.

Project. Deploy your container to a local Kubernetes cluster (kind or minikube), run two versions of the model, and shift a share of traffic to the new one.

Stage 8: Monitoring and drift

A deployed model without monitoring is a liability. Start with service metrics: Prometheus is a common open-source system for collecting and alerting on them. Then add model-specific signals: distribution of inputs and predictions compared with the training reference, and quality metrics when labels arrive. Evidently is an open-source library for data and model checks you can run on a schedule. Learn the difference between data drift and concept drift, and why a drift alert is a reason to look, not an instruction to retrain. The failures that follow are catalogued in AI model deployment challenges in production.

Project. Simulate drift by feeding your service a shifted dataset. Confirm that your dashboard and alert notice it, and write a short runbook for what a person should do next.

Stage 9: LLM applications

Once the classical loop makes sense, LLM applications are a variation: versioned prompts, retrieval indexes and evaluation sets replace training runs, and traces and cost matter more. Start with LLM monitoring and observability, and build an evaluation set before you build features.

How long it takes, and what to skip

There is no honest universal timeline; it depends on your starting point and how many hours a week you can put in. A better measure is progress by project. If you can complete the stage projects above, you can do the core of the job. Specific things to skip at first: feature stores, multi-cloud setups, and custom platforms. Learn them when a real problem calls for one.

A compact checklist

If you would rather have an engineer set up the pipeline for your team than learn it from scratch, our MLOps services cover it, and you can request a quote to get started.