/mlops-zoomcamp

This repository contains details of MLops zoomcamp organised by DataTalksClub

Primary LanguageJupyter Notebook

mlops-zoomcamp

datatalks.club

This repository tracks my work done as part of the MLops zoomcamp organised by DataTalksClub . The course's complete repository could be accessed on Github and the video playlist is available on Youtube.

Overview

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 May 2022

Course End:

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

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

Module 3: Orchestration and ML Pipelines

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

Module 4: Model Deployment

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

Module 5: Model Monitoring

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

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

Module 7: Processes

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

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.

Instructors

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