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Learn MLOps

Take models from a notebook to production, and keep them working there.

Free to start. Built from a single prompt. No credit card.

MLOps is the set of practices for getting machine learning models into production reliably and keeping them healthy afterward. It borrows from DevOps, such as version control, automated testing, containers and continuous delivery, and adds problems unique to ML: tracking experiments, versioning data alongside code, registering models, serving predictions and watching for data drift that slowly erodes accuracy. A model that only runs in one notebook is a prototype; MLOps is what turns it into a service others can depend on. Learnr builds an MLOps course around your current skills, with short lessons that walk through each stage of the lifecycle.

How to learn MLOps, step by step

Turn a notebook into a reproducible project. Move code into scripts or a package, pin dependencies, set random seeds and put everything in Git. If a teammate cannot rerun your training and get the same metrics, nothing after this step will be reliable.
Track every experiment. Use a tracker such as MLflow to log parameters, metrics and artifacts for each run. Comparing runs in a table replaces the folder of files named final_v3 and makes model choices defensible.
Version data and register models. Use a tool like DVC or your platform equivalent to tie each model to the exact data snapshot it saw. Store approved models in a registry with stages so deployment always pulls a known version.
Serve a model behind an API. Wrap the model in a small FastAPI or similar service, validate inputs, and build a Docker image. Make sure the same preprocessing code runs in training and serving to avoid training-serving skew.
Automate with CI/CD. Add tests for data schemas and model quality thresholds, then use GitHub Actions or a similar runner to retrain, evaluate and deploy only when checks pass. Learnr lessons in short daily sessions help you keep the tool landscape organized.
Monitor and close the loop. Log predictions and inputs, compare feature distributions against training data and track business metrics. Decide in advance what level of drift or accuracy loss triggers investigation or retraining.

What you'll learn

Track experiments, parameters and metrics so results are reproducible
Version data, code and models together
Package a model as an API in a container
Automate training and deployment with CI/CD pipelines
Monitor production models for data drift and performance decay

What's inside your MLOps course

A sample path Learnr builds for MLOps. Your real course adapts to your goal and level.

1. The ML lifecycle and why models fail in production
2. Reproducible environments and experiment tracking
3. Data versioning and model registries
4. Packaging and serving models
5. CI/CD and pipeline orchestration
6. Monitoring, drift and retraining

How long does it take to learn MLOps?

If you already train models in Python, learning the core MLOps workflow takes about 2 to 4 months at 5 to 6 hours a week. Prior DevOps experience with Docker and CI shortens that; running production systems at scale is ongoing learning.

What you need before you start

Python, experience training at least a simple model with scikit-learn or a deep learning framework, and Git basics. Familiarity with the command line, Docker and a cloud provider helps.

Common mistakes to avoid

Reimplementing feature preprocessing separately for serving, so production inputs differ subtly from training. Share one preprocessing function or pipeline object.
Deploying a model with no monitoring and assuming it stays accurate. Real-world data shifts, so log inputs and track performance over time.
Adopting a heavy platform before you have one model in production. Start with tracking, a container and a simple pipeline, then add tools as pain appears.

How Learnr builds your course

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MLOps questions, answered

Is MLOps more about data science or engineering?

It sits between them, but leans toward engineering. You need enough ML to understand models and metrics, and enough software and infrastructure skill to build tests, pipelines, services and monitoring.

What tools should I learn first for MLOps?

Git, Docker and one experiment tracker such as MLflow cover a lot. After that, learn a CI system and one orchestration or cloud ML platform your team uses, rather than every tool at once.