Data Science Engineering Machine-Learning Python 3
See in schedule: Thu, Jul 29, 13:45-14:15 CEST (30 min) Download/View SlidesThis 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
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.
