The MLOps landscape is increasingly focused on deploying and managing large-scale models. This shift has revealed new prospects for innovation while concurrently posing distinct challenges. In this track, we delve into these nuances, shedding light on leveraging MLOps principles to maximize the potential of large-scale models and address the inherent complexities.
We will explore an array of strategies, from developing AI-driven applications and optimizing large-scale data processing to revolutionizing machine learning infrastructure. We bring these themes to life by showcasing tangible case studies from industry pioneers, including OpenAI, Meta, Spotify, and Bumble.
Key discussion points will include:
- The crafting of high-functioning AI applications utilizing OpenAI's API and Plugins
- Meta's strategies to invigorate feature freshness in the large-scale ML data processing
- A walkthrough of Spotify's Hendrix ML platform as a progressive step in their ML infrastructure evolution
- Bumble's scalable, product-focused approach to designing platforms and features for high-performing data products
- Strategies for fostering collaboration and communication within MLOps teams tasked with large models
Irrespective of your role as a data scientist, machine learning engineer, or software developer, this track offers actionable insights for integrating large-scale models in your operations. Immerse yourself in our exploration of scalable machine-learning operations and draw from proven strategies to succeed in this rapidly evolving space.
From this track
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.
Senior Product Manager @Spotify
Tech Lead for the ML Platform @Spotify
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.
Engineering Manager @Facebook AI Infra
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.
A Bicycle for the (AI) Mind: GPT-4 + Tools
Wednesday Jun 14 / 02:55PM EDT
OpenAI recently introduced GPT-3.5 Turbo and GPT-4, the latest in its series of language models that also power ChatGPT.
Technical Staff @OpenAI
Software Engineer @OpenAI
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.
Data Science Manager @Bumble
Panel: Navigating the Future: LLM in Production
Wednesday Jun 14 / 05:25PM EDT
Our panel is a conversation that aim to explore the practical and operational challenges of implementing LLMs in production. Each of our panelists will share their experiences and insights within their respective organizations.
Technical Staff @OpenAI
Sr. Engineering Manager @DoorDash
Co-founder & CTO @Openlayer