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.

Prerequisites:
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

LINKIT

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.