Navigating Vector Databases: Indexing Strategies, GPU, and More

Learnings From Building the ML Platform at DoorDash With Hien Luu

Breaking Down Workflow Orchestration and Pipeline Authoring in MLOps

Going Deep On Model Serving, Deploying LLMs and Anything Production-Deployment

Building Internal ML Platform at Scout24: How to Ship Features that People Actually Need

Building ML Platform at Uber, Feature Stores, Vector Databases, and Real-time Feature Management

Year in Review: LLMs & LLMOps, State of MLOps, and What's Next in 2024

Building MLOps Capabilities at GitLab As a One-Person ML Platform Team With Eduardo Bonet

Learnings From Building the ML Platform at Mailchimp With Mikiko Bazeley

Learnings From Building the ML Platform at Stitch Fix With Stefan Krawczyk

Navigating Organizational Barriers by Doing MLOps with Leanne Kim Fitzpatrick

Tackling MLOps Challenges in Computer Vision with Marcin Tuszyński

What Does GPT-3 Mean For the Future of MLOps? with David Hershey

Managing Data and ML Teams to Deliver Value with Delina Ivanova

Leveraging MLOps Technologies and Principles at Non-ML Companies with Andreas Malekos and Ivan Chon-Hon Chan

Doing MLOps for Clinical Research Studies with Silas Bempong and Abhijit Ramesh

Deploying Conversational AI Products to Production with Jason Flaks

Implementing Vector Search Engines with Kacper Lukawski

ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

Continuous MLOps Pipelines with Itay Ben Haim