/mlops-zoomcamp

Free MLOps course from DataTalks.Club

Primary LanguageJupyter Notebook

MLOps Zoomcamp

Our MLOps Zoomcamp course

Overview

Objective

Teach practical aspects of productionizing ML services — from collecting requirements 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)
  • Prior programming experience (at least 1+ year)

Timeline

Course start: 16 of May

Asking for help in Slack

The best way to get support is to use DataTalks.Club's Slack. Join the #course-mlops-zoomcamp channel.

To make discussions in Slack more organized:

Syllabus

Module 1: Introduction

  • What is MLOps
  • MLOps maturity model
  • Running example: NY Taxi trips dataset
  • Why do we need MLOps
  • Course overview
  • Environment preparation
  • Homework

More details

Module 2: Experiment tracking and model management

  • 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

Module 3: Orchestration and ML Pipelines

  • Workflow orchestration
  • Prefect 2.0
  • Turning a notebook into a pipeline
  • Deployment of Prefect flow
  • Homework

More details

Module 4: Model 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

Module 5: Model Monitoring

  • Monitoring ML-based services
  • Monitoring web services with Prometheus, Evidently, and Grafana
  • Monitoring batch jobs with Prefect, MongoDB, and Evidently

More details

Module 6: 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

Project

  • End-to-end project with all the things above

More details

Module 7: Processes

  • CRISP-DM, CRISP-ML
  • ML Canvas
  • Data Landscape canvas
  • MLOps Stack Canvas
  • Documentation practices in ML projects (Model Cards Toolkit)

(In October)

Instructors

  • Larysa Visengeriyeva
  • Cristian Martinez
  • Kevin Kho
  • Theofilos Papapanagiotou
  • Alexey Grigorev
  • Emeli Dral
  • Sejal Vaidya

Other courses from DataTalks.Club:

FAQ

I want to start preparing for the course. What can I do?

If you haven't used Flask or Docker

If you have no previous experience with ML

  • Check Module 1 from ML Zoomcamp for an overview
  • Module 3 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 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:

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

Thanks to our friends for spreading the word about the course