Track host
About the track
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.
Sessions in this track
Wednesday 14 June. 6 sessions per track, chosen and introduced by the Track Host.
10:35 Salon D Session ML Infrastructure Introducing the Hendrix ML Platform: An Evolution of Spotify’s ML Infrastructure Divita Vohra, Mike Seid The rapid advancement of artificial intelligence and machine learning technology has led to exponential growth in the open-source ML ecosystem. 11:50 Salon D Session Machine Learning Improve Feature Freshness in Large Scale ML Data Processing Zhongliang Liang Engineering Manager @Facebook AI Infra 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. 13:40 Carroll Gardens Unconference Unconference: MLOps 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. 14:55 Salon D Session AI/ML A Bicycle for the (AI) Mind: GPT-4 + Tools Sherwin Wu, Atty Eleti OpenAI recently introduced GPT-3.5 Turbo and GPT-4, the latest in its series of language models that also power ChatGPT. 16:10 Salon D Session MLOps Platform and Features MLEs, a Scalable and Product-Centric Approach for High Performing Data Products Massimo Belloni Data Science Manager @Bumble 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. 17:25 Salon D Panel Panel: Navigating the Future: LLM in Production Sherwin Wu, Hien Luu, Rishab Ramanathan 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.QCon New York 2023 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.