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https://github.com/DataTalksClub/mlops-zoomcamp

Free MLOps course from DataTalks.Club
https://github.com/DataTalksClub/mlops-zoomcamp

machine-learning mlops model-deployment model-monitoring workflow-orchestration

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Free MLOps course from DataTalks.Club

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README 

        
# MLOps Zoomcamp





  

    

  






  




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



### 2023 Cohort



* **Start**: 15 May 2023 (Monday) at 17:00 CET

* **Registration link**: https://airtable.com/shrCb8y6eTbPKwSTL

* Subscribe to our [public Google Calendar](https://calendar.google.com/calendar/?cid=M3Jzbmg0ZDA2aHVsY2M1ZjcyNDJtODNyMTRAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ) (it works from Desktop only)

* [Cohort folder](cohorts/2023)

* **Deadline Callendar** for [2023 Cohort](https://docs.google.com/spreadsheets/d/e/2PACX-1vRNTwA0Of1lyprYpn2YxU-l0gvNeq-up7g7ITB42nPf2gT9Qd3PTzqTmkjAZjk1s__r7D99CsJfcZEO/pubhtml?gid=0&single=true)



### 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



### [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)



[**Weights and biases workshop**](cohorts/2023/02-experiment-tracking/wandb.md) 



### [Module 3: Orchestration and ML Pipelines](03-orchestration)



* Workflow orchestration

* Prefect 2.0

* Turning a notebook into a pipeline

* Deployment of Prefect flow

* Homework



[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

- Jeff Hale 

- Alexey Grigorev

- Emeli Dral

- Sejal Vaidya



## Other courses from DataTalks.Club:



- [Machine Learning Zoomcamp - free 4-month course about ML Engineering](https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp)

- [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/week_1_basics_n_setup/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)



**Is it going to be live?**



No and yes. There will be two parts:



* Lectures: Pre-recorded, you can watch them when it's convenient for you. 

* Office hours: Live on Mondays (17:00 CET), but recorded, so you can watch later.



**I just joined. Can I still get a certificate?**



* To get a certificate, you need to complete a project

* There will be two attempts to do a project

* First: in July, second: in August

* If you manage to finish all the materials till August, and successfully finish the project, you'll get the certificate



## Supporters and partners



Thanks to the course sponsors for making it possible to create this course