Details coming soon.
Panel: MLOps - Production & Delivery for ML Platforms
From the same track
Improve Feature Freshness in Large Scale ML Data Processing
Wednesday Jun 14 / 11:50AM EDT
In many ML use cases, model performance is highly dependent on the quality of the features they are trained and inference on. One of the important dimensions of feature quality is the freshness of the data.

Zhongliang Liang
Engineering Manager @Facebook AI Infra
Platform and Features MLEs, a Scalable and Product-Centric Approach for High Performing Data Products
Wednesday Jun 14 / 04:10PM EDT
In this talk, we would go through the lessons learnt in the last couple of years around organising a Data Science Team and the Machine Learning Engineering efforts at Bumble Inc.

Massimo Belloni
Data Science Manager @Bumble
Building Production AI-Powered Applications with the OpenAI API and Plugins
Wednesday Jun 14 / 02:55PM EDT
We recently introduced Chat Completions into the OpenAI API – which currently powers the GPT-4 and ChatGPT APIs.

Sherwin Wu
Technical Staff @OpenAI

Atty Eleti
Software Engineer @OpenAI
Introducing the Hendrix ML Platform: An Evolution of Spotify’s ML Infrastructure
Wednesday Jun 14 / 10:35AM EDT
The rapid advancement of artificial intelligence and machine learning technology has led to exponential growth in the open-source ML ecosystem.

Divita Vohra
Senior Product Manager @Spotify

Mike Seid
Tech Lead for the ML Platform @Spotify
Unconference: MLOps
Wednesday Jun 14 / 01:40PM EDT
What is an unconference? An unconference is a participant-driven meeting. Attendees come together, bringing their challenges and relying on the experience and know-how of their peers for solutions.