TWIMLcon features an exciting lineup of live podcast interviews, informative sessions and panel discussions, and networking opportunities.

Tuesday, October 4
9:00 am PDT
9:00 am PDT
Site Reliability Engineering for Reliable Machine Learning in Production
Niall Murphy, Todd Underwood
10:00 am PDT
10:00 am PDT
Closing the Loop: Automated Model Retraining and Deployment
Ethan Rosenthal
10:30 am PDT
10:30 am PDT
Using Ray to Scale ML at Shopify
Isaac Vidas
11:00 am PDT
11:00 am PDT
Responsible Machine Learning at WikiMedia
Chris Albon
11:30 am PDT
11:30 am PDT
Why You Need a GitOps-based Machine Learning Model Registry
Dmitry Petrov
12:15 pm PDT
12:15 pm PDT
Real-Time ML: Challenges and Solutions
Chip Huyen
12:45 pm PDT
12:45 pm PDT
Platform First: Scaling Recommendation and Personalization at Reddit
Garrett Hoffman
1:15 pm PDT
1:15 pm PDT
How Spotify is Navigating an Evolving ML Landscape with Hendrix Platform
Jonathan Jin, Maisha Lopa
1:45 pm PDT
1:45 pm PDT
From Principles to Practice: Taking AI From a Concept to Responsible Actuality Within Any Organization
Yoav Schlesinger
2:15 pm PDT
2:15 pm PDT
Build vs Buy in 2022
Srivathsan Canchi, Maisha Lopa, Chris Albon
Wednesday, October 5
9:00 am PDT
9:00 am PDT
You’re not Facebook: Architecting MLOps for B2B Use Cases
Jacopo Tagliabue
10:00 am PDT
10:00 am PDT
Model Quality as a Reliability Problem
Todd Underwood
10:30 am PDT
10:30 am PDT
Bringing Cutting-Edge AI Ideas from Research to Production with PyTorch
Dmytro Dzhulgakov
11:00 am PDT
11:00 am PDT
Understanding the Landscape of the Latest Large Models
Lukas Biewald
11:30 am PDT
11:30 am PDT
ML Infrastructure to Build and Train Scalable Autonomous Driving Systems
Kevin Haas
12:15 pm PDT
12:15 pm PDT
Building Reusable ML Systems at Etsy
Bhumi Patel, Kyle Gallatin
12:45 pm PDT
12:45 pm PDT
Data Labeling for Modern Machine Learning
Shayan Mohanty
1:15 pm PDT
1:15 pm PDT
How To Create a Research-Centric Machine Learning Platform
Zachary Carrico
1:45 pm PDT
1:45 pm PDT
Infrastructure for Rigorous ML Model Testing
Mohamed Elgendy
2:15 pm PDT
2:15 pm PDT
Culture and Organizational Strategies for ML Success
Adrian Cartier, Rosa Català, Miriam Friedel
Thursday, October 6
9:00 am PDT
9:00 am PDT
The Great MLOps Debate: End-to-End ML Platforms vs Specialized Tools
Bindu Reddy, Noah Gift, Demetrios Brinkmann, Shayan Mohanty, Dan Jeffries
10:00 am PDT
10:00 am PDT
Build Better Models Faster with Weights & Biases
Thomas Capelle
11:00 am PDT
11:00 am PDT
Codifying, Standardizing, and Serving Production-Ready ML Models with Open-Source Tool MLEM
Mikhail Sveshnikov
12:00 pm PDT
12:00 pm PDT
Training and Deploying Deep Learning Recommender Systems with Merlin
Ronay Ak, Benedikt Schifferer
1:30 pm PDT
1:30 pm PDT
Ship Performant Models Confidently with Labelbox
Karen Yang, Maxime Voisin
2:30 pm PDT
2:30 pm PDT
Team Teardown: Evolving a Mature Platform Organization at LinkedIn
Zheng Li, Jenelle Bray, Tanton Gibbs
Friday, October 7
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