MLOps: Navigating the Terrain of Large-Scale Models

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.