MLOps Zoomcamp

Our MLOps Zoomcamp course

• Sign up here: https://airtable.com/shrCb8y6eTbPKwSTL (it's not automated, you will not receive an email immediately after filling in the form)
• Register in DataTalks.Club's Slack
• Join the #course-mlops-zoomcamp channel
• Tweet about the 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 (1+ years of professional experience)

Timeline

Course start: 16 of May

Syllabus

There are five modules in the course and one project at the end. Each module is 1-2 lessons and homework. One lesson is 60-90 minutes long.

This is a draft and will change.

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

Instructors: Alexey Grigorev

Module 2: Processes

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

Instructors: Larysa Visengeriyeva

Module 3: Training

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

Instructors: Cristian Martinez

ML Pipelines

• ML Pipelines: introduction
• Kubeflow Pipelines
• Turning a notebook into a pipeline

Instructors: Theofilos Papapanagiotou

Module 4: Serving

• Batch vs online
• For online: web services vs streaming
• Serving models in Batch mode
• Web services
• Streaming (Kinesis/SQS + AWS Lambda)
• Homework

Instructors: Alexey Grigorev

Module 5: Monitoring

• ML monitoring VS software monitoring
• Data quality monitoring
• Data drift / concept drift
• Batch VS real-time monitoring
• Tools: Evidently, Prometheus and Grafana
• Homework

Instructors: Emeli Dral

Module 6: Best practices

• Devops
• Virtual environments and Docker
• Python: logging, linting
• Testing: unit, integration, regression
• CI/CD (github actions)
• Infrastructure as code (terraform, cloudformation)
• Cookiecutter
• Makefiles
• Homework

Instructors: Alexey Grigorev, Sejal Vaidya

Project

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

Running example

To make it easier to connect different modules together, we’d like to use the same running example throughout the course.

Possible candidates:

• https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page - predict the ride duration or if the driver is going to be tipped or not

Instructors

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

Other courses from DataTalks.Club:

• Machine Learning Zoomcamp - free 4-month course about ML Engineering
• Data Engineering Zoomcamp - free 9-week course about Data Engineering

FAQ

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

If you haven't used Flask or Docker

• Check Module 5 form ML Zoomcamp
• The section about Docker from Data Engineering Zoomcamp could also be useful

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)