Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Scaling Python for Machine Learning - Beyond Data Parallelism

GOTO Conferences via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore advanced techniques for scaling Python in machine learning beyond data parallelism in this conference talk from GOTO Chicago 2023. Dive into the world of distributed computing with Holden Karau, an Open Source Engineer at Netflix, as she examines Spark, Dask, and Ray for scaling machine learning models. Learn about distributed tasks, actor models for managing model weights during training, and fault tolerance in various frameworks. Gain insights into the similarities and differences between Dask and Ray distributed tasks, understand task and actor fault tolerance, and discover the relationship between Ray and Netflix. This comprehensive presentation covers topics such as data parallelism refresher, distributed task structures, Spark's capabilities, and actor fault tolerance in Ray and Dask, providing valuable knowledge for scaling Python applications in machine learning contexts.

Syllabus

Intro
Probable relevant biases
Quick refresher on data parallelism
What do distributed tasks look like?
Dask distributed tasks
Ray distributed tasks
How are they different & same?
Task fault tolerance
Does Spark have tasks & actors?
Ray Diagram
Ray actor fault tolerance
What's up with Ray & Netflix?
Dask actor fault tolerance
Outro

Taught by

GOTO Conferences

Reviews

Start your review of Scaling Python for Machine Learning - Beyond Data Parallelism

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.