MLOps World | GenAI Summit

What’s the difference between a model that runs on your laptop and one that serves traffic? We’re thankful to have Runpod pushing this conversation this year. They are a Silver sponsor for MLOps World | GenAI Summit Austin. 📅 November 16 to 19 mlopsworld.com/

Are you PR code reviews cued up around the block? How did DraftKings Inc. address this? MLOps World Austin Wednesday, November 18, 12:05 p.m. mlopsworld.com/

Ankit Goyal is Principal Staff Engineer at LinkedIn, technical lead for Machine Learning Infrastructure and lead architect for the company’s AI Training Platform. He’s speaking at MLOps World | GenAI Summit. mlopsworld.com/

AI-assisted development tools can now quickly produce working software that once took months to handcraft. But it has limitations. AI-Assisted Software Engineering track, MLOps World Austin, Thursday, November 19, 9:30 a.m., workshop day. mlopsworld.com/

Amir Feizpour is one of Canada’s biggest leaders in the AI community! The order he teaches is “evidence first”. He’s sharing the lessons learned for attendees at MLOps World | GenAI Summit, November 19, workshop day. mlopsworld.com/

Why does an agent that works in development die in production? Most times, it’s the infrastructure. This is part of our Speakers Corner, MLOps World | GenAI Summit Austin Tuesday, November 17, 1:15 p.m. mlopsworld.com/

How can you make a general model measurably better at a specific job? Come join us, and come meet Sophia! MLOps World Austin Tuesday, November 17, 2:10 p.m. mlopsworld.com/

A stronger model cannot recover information it never receives. Kshetrajna Raghavan (Shopify) built the redesigned system that achieved ~75x lower inference cost and close to 2× improvement on selected quality metrics. MLOps World Austin, November 17 mlopsworld.com/

The catch with conversion has always been the retraining bill: with standard approaches it took 33× the token budget ScaleOp reaches the same quality at 3–18× instead Ravi Satya Durga Prasad Yenugula (2K Games) MLOps World Austin, November 18, 2:10 p.m. mlopsworld.com/

80% accuracy is a production target, not a ceiling. The remaining 20% is where your guardrails earn their keep. Suneet Malhotra ( @MotoSolutions ) redesigned the QA pipeline around Claude Code. MLOps World Austin, November 16, virtual session day mlopsworld.com/

Union.ai supports customers running 12 million or more containers on a control plane that holds no customer data. That was not the goal. Niels Bantilan will cover this architecture. They’re a Silver sponsor for MLOps World Austin mlopsworld.com/

Data engineering’s bottleneck has moved. Infrastructure is no longer the main bottleneck. Meaning and clarity in the data are. When machines take actions, semantic ambiguity becomes catastrophic. Joe Reis · MLOps World Austin, November 18, 2:10 p.m. mlopsworld.com/

Instacart cut feature store latency in half by running fewer nodes. Using Dragonfly’s multi-threaded engine, hundreds of nodes became roughly 100. This is part of our 7th annual MLOps World | GenAI Summit. Hope you can join us!

Nalin Dadhich, Senior Machine Learning Engineer, NVIDIA, built the synthetic data that took Cosmos 3 to #1. → Big Ideas, Innovations and Creative Explorations track. MLOps World Austin, Tuesday November 17, 2:10 p.m. mlopsworld.com

“Alternating between inference sampling and gradient updates creates severe low GPU duty cycles (~40%) during LLM reinforcement learning pipelines.” → Hardware and Chips track. MLOps World Austin, Wednesday November 18, 11:30 a.m

“A leaderboard win is not a product.” Kalpesh Sutaria (Director, NVIDIA) worked on Nemotron Retriever, which ranks #1 on RTEB but still wasn’t runnable for customer production. → Hardware and Chips track. MLOps World Austin mlopsworld.com

In their own words, cost came up more than anything else. @CoreWeave sits right in the middle of these discussions. They are our Platinum sponsor for MLOps World Austin. 📅 November 16 to 19 → Details and registration mlopsworld.com

A multi-agent pipeline that worked, but was slow and expensive. Parallelizing the independent roles cut runtime by roughly 40%. A per-role comparison of model tiers cut per-run cost by roughly 40% more. 📅 November 17 and 18, mlopsworld.com

A multi-agent pipeline that worked, but was slow and expensive. Parallelizing the independent roles cut runtime by roughly 40%. A per-role comparison of model tiers cut per-run cost by roughly 40% more. 📅 November 17 and 18, Austin. mlopsworld.com