TrueML Talks #32 | MLOps practices at Outerbounds

Описание: In this episode of #TrueMLtalks, Savin from Outerbounds shares insights into the MLOPs use cases in Netflix. Drawing from his experiences initially with LinkedIn and Netflix, Savin delves into how he used her previous experience to start his journey with Outerbounds.

The discussion with Savin covered a wide array of topics, including:

✨ Savin's Experience in AI/ML
✨ The emergence of ML engineering as a discipline
✨ The Creation of Metaflow
✨ Comparison of Metaflow with other orchestration tools like Airflow
✨ Challenges in ML Operations
✨ Beginning of Outerbounds
✨ Success stories and transformative impacts of Metaflow
✨ Future Aspects of MLOPs

______________________ In this episode of #TrueMLtalks, Savin from Outerbounds shares insights into the MLOPs use cases in Netflix. Drawing from his experiences initially with LinkedIn and Netflix, Savin delves into how he used her previous experience to start his journey with Outerbounds.

The discussion with Savin covered a wide array of topics, including:

✨ Savin's Experience in AI/ML
✨ The emergence of ML engineering as a discipline
✨ The Creation of Metaflow
✨ Comparison of Metaflow with other orchestration tools like Airflow
✨ Challenges in ML Operations
✨ Beginning of Outerbounds
✨ Success stories and transformative impacts of Metaflow
✨ Future Aspects of MLOPs

______________________ In this episode of #TrueMLtalks, Savin from Outerbounds shares insights into the MLOPs use cases in Netflix. Drawing from his experiences initially with LinkedIn and Netflix, Savin delves into how he used her previous experience to start his journey with Outerbounds.

The discussion with Savin covered a wide array of topics, including:

✨ Savin's Experience in AI/ML
✨ The emergence of ML engineering as a discipline
✨ The Creation of Metaflow
✨ Comparison of Metaflow with other orchestration tools like Airflow
✨ Challenges in ML Operations
✨ Beginning of Outerbounds
✨ Success stories and transformative impacts of Metaflow
✨ Future Aspects of MLOPs

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Timestamps
00:40 Introduction
03:30 Discussion on Experience with Metaflow
09:30 Overview of Initial Tools Utilized
11:30 Insights into Data Science at Netflix
18:15 Challenges in Building Metaflow
25:13 Origin and Evolution of Outerbounds
26:50 Criteria for Selecting the Right Tool for Machine Learning
33:57 Examination of Large Language Models (LLMs)
35:50 Success Stories Involving Metaflow
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