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# Facebook
# Vector Search
# Embedding

Facebook Marketplace Machine Learning System Design Review

LLM in Production Round Table

Diego Oppenheimer, David Hershey, Hannes Hapke, James Richards & Rebecca Qian

Popular topics
# Interview
# Presentation
# Case Study
# Open Source
# Model Serving
# Coding Workshop
# Monitoring
# Scaling
# Deployment
# Panel
# Cultural Side
# ML Orchestration
# Feature Stores
# Data Science
# Infrastructure
# Leverage
# ML Platform
# ML Workflow
# Health Care
# Googler
Saahil Jain
Saahil Jain · Mar 14th, 2023

The Future of Search in the Era of Large Language Models

Saahil shares insights into the search engine approach, which includes a focus on a user-friendly interface, third-party apps, and the combination of natural language processing and traditional information retrieval techniques. Saahil highlights the importance of product thinking and the trade-offs between relevance, throughput, and latency when working with large language models. Saahil also discusses the intersection of traditional information retrieval and generative models and the trade-offs in the type of outputs they produce. He suggests occupying users' attention during long wait times and the importance of considering how users engage with websites beyond just performance.
# Large Language Models
# Future of Search
Matt Squire
Matt Squire · Mar 10th, 2023

MLOPs on the Edge and Cheating at Dobble

Deploying ML models to low-powered hardware brings a whole bunch of new challenges. In this talk, Matt walks through how they trained a vision model to play a card game on an Nvidia Jetson Nano and discusses the MLOPs tooling and approaches we used to make it happen.
# MLOps on the Edge
# Dobble
# MLOPs tooling
Jason McCampbell
Jason McCampbell · Mar 10th, 2023

The Challenges of Deploying (many!) ML Models

In order to scale the number of models a team can manage, we need to automate the most common 90% of deployments to allow ops folks to focus on the challenging 10% and automate the monitoring of running models to reduce the per-model effort for data scientists. The challenging 10% of deployments will often be "edge" cases, whether CDN-style cloud-edge, local servers, or running on connected devices.
# Challenges of Deploying ML Models
# Wallaroo
# Edge and ML
As an emerging sub-discipline, MLOps still needs to prove itself inside many enterprises where ML may present significant opportunities. Focused on the needs of the ML and Enterprise Architect this talk discusses some of the key areas which motivate the adoption of an MLOps approach to handling ML solution development and operations.
# Enterprises
# ML Approach Adoption
# ML Solution Development and Operations
Karl Fezer
Karl Fezer · Mar 7th, 2023

Intelligence & MLOps

This conversation explores various topics including biases, defining intelligence, and the future of large language models and MLOps. Karl discusses his paper on defining intelligence and how it relates to the increasing interest in Artificial Intelligence. Karl shares his thoughts on the overlap between foundational models and MLOps, emphasizing the importance of making high-impact tasks more efficient and easier. The conversation touched on philosophical tangents but ultimately circled back to practical applications of these concepts.
# Intelligence
# Artificial Intelligence
# Foundational Models and MLOps
Sam Jenkins
Sam Jenkins · Mar 3rd, 2023

Generics - MLOps Before it was Cool

Ultraleap do a surprising amount of interesting ML – mostly hand tracking from cameras - they had headsets to try and a cool screen ball pit demo thing. They’ve got this interesting homebrew ML platform because they were shipping ML in a product early. It’s got some interesting features and a ton of stuff in it, so they’re having a fun time working out how to replace it, nothing modern quite fits. Maybe partly as they don’t have the normal model serving problem. They’re hiring across a surprising range of things.
# Ultraleap
# Hand-tracking Technology
# Virtual Reality
 Crag Wolfe
Crag Wolfe · Mar 1st, 2023

Let's Talk About Raw Documents

Modern ML pipelines still often need pre-processed documents. This isn't changing anytime soon, in fact, the appetite is growing. is focused on extracting structured data from raw documents (pdf, pptx, html, etc). In the near term, we're more NLP-focused. Check out's open-source libraries!
# Preprocessing API
# NLP-focused
This learning session is led by Elhay Efrat, a Cloud Solution Architect for Startups. Elhay guides attendees through the practical application of Open AI on Azure, discussing how to leverage the platform to improve Machine Learning performance and efficiency in real-world scenarios. Topics are given with an overview of the Machine Learning lifecycle in the real world, comparing cognitive services vs Open AI. The session also covers ML operations on Azure, and how to use Open AI to enhance performance and efficiency in real-world scenarios.
# Open AI
# Azure
# Real-world scenarios
 Alex DeBrie
Alex DeBrie · Feb 23rd, 2023

The Rise of Serverless Databases

For databases, it feels like we're in the middle of a big shift. The first 10-15 years of the cloud were mostly about using the same core infrastructure patterns but in the cloud (SQL Server, MySQL, Postgres, Redis, Elasticsearch). In the last few years, we're finally seeing data infrastructure that is truly built for the cloud. Elastic, scalable, resilient, managed, etc. Early examples were Snowflake + DynamoDB. The most recent ones are all the 'NewSQL' contenders (Cockroach, Yugabyte, Spanner) or the 'serverless' ones (Neon, Planetscale). Also seeing improvements in caching, search, etc. Exciting times!
# Serverless Databases
# Cloud
# Infrastructure Patterns
# Opinionated Databases
 Shalabh Chaudhri
Shalabh Chaudhri · Feb 21st, 2023

The Ops in MLOps - Process and People

Shalabh talks through their newfound appreciation for the MLOps perspective from a customer success standpoint. Shalabh's emphasis on setting realistic expectations and ensuring the delivery of promised value adds is particularly valuable. Generally, this episode provides a unique and insightful perspective on MLOps from the lens of customer success.
# MLOps perspective
# Customer Success
# Realistic Expectations
MLflow Pipelines: Opinionated ML Pipelines in MLflow
Xiangrui Meng
Monzo Machine Learning Case Study
Neal Lathia
More than a Cache: Turning Redis into a Composable, ML Data Platform
Samuel Partee
Driving ML Data Quality with Data Contracts
Andrew Jones