Practical AI: Machine Learning, Data Science, LLM

From notebooks to Netflix scale with Metaflow

Sep 21, 2021

47:34

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Highlights

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Episode notes

The philosophy behind using Metaflow for solving scale and infrastructure problems

01:55

Is There a Tooling That Can Help With Machine Learning?

03:35

How Far Does MetaFlow Reach?

04:37

How Did Your Perception of Impa Structure Change as You Worked on Netflicks?

02:41

Introduction

3min

Is There a Future in Artificial Neural Networks?

2min

What's the Difference Between the Cloud and the High Level Modelling Libraries?

2min

How Did Your Perception of Impa Structure Change as You Worked on Netflicks?

3min

Data Science

2min

Is There a Tooling That Can Help With Machine Learning?

4min

Is There a Way to Boost Your Productivity?

3min

The Back Story of Meadawflow

4min

How Far Does MetaFlow Reach?

5min

10.

Using Meta Flow in a Workflow?

4min

11.

Metaflo - A Data Scientist's Guide

6min

12.

Python Workflow

2min

13.

Is There a Decentralized Workflow?

4min

14.

Data Science and the Structure Ow to Make Data Scientists More Productive

3min

15.

Change Log Podcasts - Ship It

2min

As you start developing an AI/ML based solution, you quickly figure out that you need to run workflows. Not only that, you might need to run those workflows across various kinds of infrastructure (including GPUs) at scale. Ville Tuulos developed Metaflow while working at Netflix to help data scientists scale their work. In this episode, Ville tells us a bit more about Metaflow, his new book on data science infrastructure, and his approach to helping scale ML/AI work.

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