base on Free MLOps course from DataTalks.Club # MLOps Zoomcamp <p align="center"> <a href="https://airtable.com/shrCb8y6eTbPKwSTL" target="_blank"> <img src="images/IMG_20230323_134059_927.png"> </a> </p> <p align="center"> <a href="https://airtable.com/shrCb8y6eTbPKwSTL"><img src="https://user-images.githubusercontent.com/875246/185755203-17945fd1-6b64-46f2-8377-1011dcb1a444.png" height="50" /></a> </p> Our MLOps Zoomcamp course - Sign up here: https://airtable.com/shrCb8y6eTbPKwSTL - Register in [DataTalks.Club's Slack](https://datatalks.club/slack.html) - Join the [`#course-mlops-zoomcamp`](https://app.slack.com/client/T01ATQK62F8/C02R98X7DS9) channel - [Tweet about the course!](https://ctt.ac/fH67W) - Start watching course videos! [Course playlist](https://www.youtube.com/playlist?list=PL3MmuxUbc_hIUISrluw_A7wDSmfOhErJK) - [Technical FAQ](https://docs.google.com/document/d/12TlBfhIiKtyBv8RnsoJR6F72bkPDGEvPOItJIxaEzE0/edit) - For announcements, join our [Telegram channel](https://t.me/dtc_courses) ## Taking the course ### 2025 Cohort * **Start**: May 2025 * **Registration link**: https://airtable.com/shrCb8y6eTbPKwSTL * Subscribe to our [public Google Calendar](https://calendar.google.com/calendar/?cid=M3Jzbmg0ZDA2aHVsY2M1ZjcyNDJtODNyMTRAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ) (it works from Desktop only) ### Self-paced mode All the materials of the course are freely available, so that you can take the course at your own pace * Follow the suggested syllabus (see below) week by week * You don't need to fill in the registration form. Just start watching the videos and join Slack * Check [FAQ](https://docs.google.com/document/d/12TlBfhIiKtyBv8RnsoJR6F72bkPDGEvPOItJIxaEzE0/edit) if you have problems * If you can't find a solution to your problem in FAQ, ask for help in Slack ## Overview ### Objective Teach practical aspects of productionizing ML services — from training and experimenting to model deployment and monitoring. ### Target audience Data scientists and ML engineers. Also software engineers and data engineers interested in learning about putting ML in production. ### Pre-requisites * Python * Docker * Being comfortable with command line * Prior exposure to machine learning (at work or from other courses, e.g. from [ML Zoomcamp](https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp)) * Prior programming experience (at least 1+ year) ### Asking for help in Slack The best way to get support is to use [DataTalks.Club's Slack](https://datatalks.club/slack.html). Join the [`#course-mlops-zoomcamp`](https://app.slack.com/client/T01ATQK62F8/C02R98X7DS9) channel. To make discussions in Slack more organized: * Follow [these recommendations](asking-questions.md) when asking for help * Read the [DataTalks.Club community guidelines](https://datatalks.club/slack/guidelines.html) ## Syllabus We encourage [Learning in Public](learning-in-public.md) ### [Module 1: Introduction](01-intro) * What is MLOps * MLOps maturity model * Running example: NY Taxi trips dataset * Why do we need MLOps * Course overview * Environment preparation * Homework [More details](01-intro) ### [Module 2: Experiment tracking and model management](02-experiment-tracking) * Experiment tracking intro * Getting started with MLflow * Experiment tracking with MLflow * Saving and loading models with MLflow * Model registry * MLflow in practice * Homework [More details](02-experiment-tracking) ### [Module 3: Orchestration and ML Pipelines](03-orchestration) * Workflow orchestration * Mage [More details](03-orchestration) ### [Module 4: Model Deployment](04-deployment) * Three ways of model deployment: Online (web and streaming) and offline (batch) * Web service: model deployment with Flask * Streaming: consuming events with AWS Kinesis and Lambda * Batch: scoring data offline * Homework [More details](04-deployment) ### [Module 5: Model Monitoring](05-monitoring) * Monitoring ML-based services * Monitoring web services with Prometheus, Evidently, and Grafana * Monitoring batch jobs with Prefect, MongoDB, and Evidently [More details](05-monitoring) ### [Module 6: Best Practices](06-best-practices) * Testing: unit, integration * Python: linting and formatting * Pre-commit hooks and makefiles * CI/CD (GitHub Actions) * Infrastructure as code (Terraform) * Homework [More details](06-best-practices) ### [Project](07-project/) * End-to-end project with all the things above [More details](07-project/) ## Instructors - Cristian Martinez - Tommy Dang - Alexey Grigorev - Emeli Dral - Sejal Vaidya ## Other courses from DataTalks.Club: - [Machine Learning Zoomcamp - free 4-month course about ML Engineering](http://mlzoomcamp.com) - [Data Engineering Zoomcamp - free 9-week course about Data Engineering](https://github.com/DataTalksClub/data-engineering-zoomcamp/) ## FAQ **I want to start preparing for the course. What can I do?** If you haven't used Flask or Docker * Check [Module 5](https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp/05-deployment) from ML Zoomcamp * The [section about Docker](https://github.com/DataTalksClub/data-engineering-zoomcamp/tree/main/01-docker-terraform/2_docker_sql) from Data Engineering Zoomcamp could also be useful If you have no previous experience with ML * Check [Module 1](https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp/01-intro) from ML Zoomcamp for an overview * [Module 3](https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp/03-classification) will also be helpful if you want to learn Scikit-Learn (we'll use it in this course) * We'll also use XGBoost. You don't have to know it well, but if you want to learn more about it, refer to [module 6](https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp/06-trees) of ML Zoomcamp **I registered but haven't received an invite link. Is it normal?** Yes, we haven't automated it. You'll get a mail from us eventually, don't worry. If you want to make sure you don't miss anything: * Register in [our Slack](https://datatalks.club/slack.html) and join the `#course-mlops-zoomcamp` channel * Subscribe to [our YouTube channel](https://youtube.com/c/datatalksclub) ## Supporters and partners Thanks to the course sponsors for making it possible to run this course <p align="center"> <a href="https://mage.ai/"> <img height="120" src="https://github.com/DataTalksClub/data-engineering-zoomcamp/raw/main/images/mage.svg"> </a> </p> Do you want to support our course and our community? Reach out to [[email protected]]([email protected]) ", Assign "at most 3 tags" to the expected json: {"id":"9998","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"