Panel: Building High-Performance ML Teams

apply()2021 - 30 minutes

As Machine Learning moves to production, ML teams have to evolve into high-performing engineering teams. Data science is still a central role, but no longer sufficient. We now need new functions (e.g. MLOps Engineers) and new processes to bridge the gap between traditional data science and the world of software engineering. In this panel discussion, we’ll discuss how high-performing ML teams are organized to build and deploy production-quality ML models with engineering best practices.


Neal Lathia

Associate Director, Machine Learning

Monzo Bank

Neal is the Director of Machine Learning at Monzo Bank in the United Kingdom, where he leads a team of Machine Learning Scientists who build and deploy ML models across several different parts of the bank – ranging from financial crime through to customer service. This journey started with a couple of Data Scientists spending part of their time training machine learning models and has grown to having full-time Machine Learning Scientists who train and deploy their machine learning models across our analytics and production stacks, supported by in-house tooling and systems.

Janakiram MSV

Principal Analyst

Janakiram and Associates

Janakiram MSV is an internationally recognized analyst, advisor and an architect in the field of Microservices, IoT, Edge Computing, and AI.

He was the founder and CTO of Get Cloud Ready Consulting, a niche Cloud Migration and Cloud Operations firm that got acquired by Aditi Technologies.

Through his speaking, writing, and analysis, he helps businesses take advantage of emerging technologies.

Janakiram is a regular contributor at Forbes and The New Stack. His articles are published at Business Insider, Computer Weekly, TechRepublic, and YourStory.

He is currently an advisor to Bay Area startups from the container storage, edge computing, and machine learning domains.

Janakiram is an adjunct faculty at the International Institute of Information Technology (IIIT-H) where he teaches Big Data, Cloud Computing, Containers, and DevOps to the students enrolled for the Master’s course.

Kevin Stumpf

Co-Founder and CTO

Tecton

Kevin co-founded Tecton where he leads a world-class engineering team that is building a next-generation feature store for operational Machine Learning. Kevin and his co-founders built deep expertise in operational ML platforms while at Uber, where they created the Michelangelo platform that enabled Uber to scale from 0 to 1000’s of ML-driven applications in just a few years.

Prior to Uber, Kevin founded Dispatcher, with the vision to build the Uber for long-haul trucking. Kevin holds an MBA from Stanford University and a Bachelor’s Degree in Computer and Management Sciences from the University of Hagen. Outside of work, Kevin is a passionate long-distance endurance athlete.

Alex Williams

Founder and Editor in Chief

The New Stack

Alex Williams is founder and publisher of The New Stack, a content platform for the people who build and manage software the world relies on. He was an editor at ReadWriteWeb and TechCrunch before leaving in 2014 to start The New Stack. Alex hosts The New Stack Makers pancake and podcast breakfast, and livestreams from tech events around the globe with the generous support of The New Stack’s sponsors in the cloud native ecosystem.

Lex Beattie

Machine Learning Engagement Lead

Spotify

 Lex Beattie is the Machine Learning Engagement Lead at Spotify. Since creating the ML Engagements team, she has helped over 40 different teams across Spotify understand ML best practices, productionize ML workflows and implement impactful ML in their products. Lex is also a Ph.D. candidate at the University of Oklahoma, focusing on feature importance and interpretability in deep neural networks. Beyond her passion for all things ML, she enjoys exploring the great outdoors in Montana with her German Wirehaired Pointer, Bridger.