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!
Subscribe to the public Google calendar (subscription works form desktop only)

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

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: 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

Module 3: Orchestration and ML Pipelines

ML Pipelines: introduction
Prefect
Turning a notebook into a pipeline
Kubeflow Pipelines
Homework

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

Module 7: Processes

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

Instructors: Larysa Visengeriyeva

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:

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)
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 anythign:

Register in our Slack and join the #course-mlops-zoomcamp channel
Subscribe to our YouTube channel

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, but recorded, so you can watch later.

Partners

Thanks to our friends for spreading the word about the course

hhy5277 / mlops-zoomcamp Goto Github PK

mlops-zoomcamp's Introduction