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We Build a ML Pipeline After We Deploy

EuroPython Conference via YouTube

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Overview

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This talk explains why and when to build end-to-end machine learning pipelines, covering training and inference, engineering for failures and performance, monitoring, and debugging. It also introduces open-source Python libraries for pipeline work.

Syllabus

Introduction
One stop solution
Agenda
Who am I
What is ML pipeline
Why do we need this pipeline
Why automate it
Reduce the cost of any project
When should we use it
When to scale
Building blocks
Continuous Integration
Continuous Delivery
Automated Pipeline
Continuous Delivery Process
Monitoring
Engineering
Debugging
Top 3 debugging issues
Python libraries
QA time

Taught by

EuroPython Conference

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