DataTalksClub/mlops-zoomcamp

Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort

What is this?

MLOps Zoomcamp is a free, open-source online course that teaches you how to take machine learning models from a notebook and put them into real-world production systems. It's a 9-week program covering the core skills of MLOps (machine learning operations) — everything from tracking experiments and managing models to deploying, monitoring, and maintaining them reliably.

Who is it for?

  • Data scientists who want to move beyond training models to deploying and operating them
  • ML engineers and software engineers looking to add production ML skills
  • Anyone with at least one year of programming experience and basic familiarity with Python, Docker, and the machine learning workflow (e.g., from the related ML Zoomcamp course)

What will you learn?

The course is organized into six modules plus a final project:

  1. Introduction to MLOps – what it is, why it matters, and a running example dataset (NY Taxi)
  2. Experiment Tracking & Model Management – using MLflow to log, save, and manage models
  3. Orchestration & ML Pipelines – automating workflows
  4. Model Deployment – different strategies: web services, streaming (AWS Kinesis/Lambda), and batch scoring
  5. Model Monitoring – tracking performance in production using tools like Prometheus, Evidently, Grafana, and Prefect
  6. Best Practices – testing, linting, CI/CD with GitHub Actions, and infrastructure as code (Terraform)

The final project ties everything together into an end-to-end MLOps pipeline.

How does it work?

The course is pre‑recorded and can be taken in two ways:

  • Live cohort (not currently scheduled): includes graded homework, peer review, a leaderboard, and a certificate of completion. You can register to be notified if a new cohort starts.
  • Self‑paced: start anytime, follow the materials on GitHub, do the homework (self‑checked), and build a project for your portfolio. No certificate is awarded in self-paced mode, but all course content is free.

Prerequisites (from the README)

  • Python
  • Docker
  • Command line basics
  • Machine learning experience (e.g., through ML Zoomcamp)
  • 1+ year of programming experience

Community & support

The course is part of DataTalks.Club, a global community of data enthusiasts. You can:

  • Ask questions and share progress in the dedicated Slack channel (#course-mlops-zoomcamp)
  • Join the broader Slack community for general discussion
  • Follow Telegram announcements for course updates
  • Watch all video lectures on the YouTube playlist

Key features (from the README)

  • 100% free and open‑source
  • 9‑week structured curriculum
  • Hands‑on exercises and homework each module
  • Final project to build a portfolio piece
  • Certificate for learners who complete the final project in a live cohort
  • Optional peer review and leaderboard in live cohorts
  • Self‑paced mode available anytime

All information here is taken directly from the project's README.

Related

  • Project
    EleutherAI/lm-evaluation-harness

    A unified framework to evaluate generative language models on a large number of standard academic benchmarks, serving as the backend for the Open LLM Leaderboard.

  • Project
    ccfos/nightingale

    Nightingale is an open‑source, self‑hosted alerting platform that aggregates alerts from multiple monitoring data sources (Prometheus, VictoriaMetrics, Elasticsearch, Loki, ClickHouse, PostgreSQL, etc.). It provides a UI/API for rule management, team‑based permissions, event processing pipelines, and native notification integrations (Slack, PagerDuty, Discord, Email, Telegram, Jira, …). A built‑in AI assistant can query monitoring data, explain alerts, generate or modify rules, and run remediation scripts via extensible “Skills”. Deployable via Docker Compose, Linux binaries, or Helm on Kubernetes, Nightingale is Apache‑2.0 licensed and backed by a community Slack and GitHub discussions.

  • Project
    confident-ai/deepeval

    DeepEval is an open‑source Python framework that lets you write pytest‑style tests for any LLM‑based app—chatbots, agents, RAG pipelines, or multimodal generators. It ships with dozens of research‑backed metrics (e.g., G‑Eval, JevEval, task‑completion, hallucination, bias) that can run on any model locally, and you can add custom metrics. The library integrates with popular toolkits (LangChain, OpenAI, Anthropic, LlamaIndex, etc.) to automatically trace model calls, enabling both end‑to‑end and step‑level evaluation. Tests run from the CLI, fit into CI/CD, and can be linked to the commercial Confident AI platform for production‑time monitoring. Ideal for developers and teams who need quantitative quality gates for LLM applications.

  • Project
    EthicalML/awesome-production-machine-learning

    A curated list of open-source libraries for deploying, monitoring, versioning, scaling, and securing production machine learning systems, organized by category with monthly updates and a search tool.