We build a ML pipeline after we deploy

Tips and tricks to productionize Data Science projects efficiently

Alyona Galyeva

Data Science Engineering Machine-Learning Python 3

See in schedule

This talk covers the importance of building end-to-end machine learning pipelines from day one.

What you will learn:
- why we need a machine learning pipeline and when to use it;
- ML pipeline building blocks covering training and inference;
- engineering around failures and engineering for performance;
- ML pipelines debugging and monitoring;
- open-source Python libraries to save your time.

For whom:
- data scientists, data analysts, data engineers, machine learning engineers, data product owners, Python developers, working or willing to work with machine learning.

to get the most out of this talk, Data Science, ML, and Python experience is recommended

Type: Talk (30 mins); Python level: Intermediate; Domain level: Intermediate

Alyona Galyeva


Observe - Optimize - Learn - Repeat
Passionate about encouraging others to see different perspectives and constructively break the rules.
I found my joy in building, optimizing and deploying end-to-end AI and Data Engineering Solutions.