On Demand

Find out what's top of mind for AI & Machine Learning experts from leading enterprise brands

          • Understand your customers
          • Large-scale personalization
          • Rapid innovation and adoption

Presentations from leaders at:

Learn from industry leaders and skip the learning curve.
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A Masterclass in Delivering High-Velocity ML at Scale

With over 30 sessions, TWIMLcon On Demand offers a masterclass in how to design and build teams and platforms for delivering machine learning in a high-velocity, repeatable, consistent and predictable manner—at scale:


A masterclass in delivering enterprise ML & AI.
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Build your ML Operationalization Knowledge Through Case Studies, How-Tos, Perspective Talks, Panels and Workshops


How Spotify Does ML at Scale

Aman Khan Spotify

Aman Khan

Product Manager

John Baer Spotify

Josh Baer

Machine Learning Platform Product Leader

When Good Models Go Bad: The Damage Caused by Wayward Models and How to Prevent it

Todd Underwood

Josh Baer

Engineering Director

Machine Learning is Going Real-Time: Are You Ready?

Chip Huyen

Chip Huyen

Machine Learning Engineer at Snorkel AI, Adjunct Lecturer at Stanford

Cost Transparency and Model ROI at Intuit

Ian Sebanja

Ian Sebanja

Machine Learning Platform

Srivathsan Canchi

Srivathsan Canchi

Head of Engineering, ML Platform Team

Driving Platform Adoption Success at Spotify

James Kirk

James Kirk

Staff Machine Learning Engineer, Listening Experiences

Lex Beattie

Lex Beattie

Machine Learning Engagement Lead

Maisha Lopa

Maisha Lopa

Senior Product Manager, Machine Learning Platform

Sam N

Samuel Ngahane

Staff ML Software Engineer

Maya Hristakeva

Maya Hristakeva

Machine Learning Engineering Manager

Build Smarter, Innovate Faster and Avoid Costly Mistakes

TWIMLcon sessions—presented by data scientists, engineers and leaders from teams at the forefront of operationalizing machine learning—can help you and your team save time and money by learning:

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Packages start at $499 $399

Faisal Siddiqi Netflix

Faisal Siddiqi

Director of Engineering, Personalization Infrastructure


Faisal is the Director of Engineering for Personalization Infrastructure at Netflix, where he runs multiple teams delivering large-scale ML infrastructure to support the company’s personalization research and systems. The teams he runs currently support model development, model tools and data, and model serving. In this keynote conversation, Faisal and Sam discuss lessons learned building Netflix’s large-scale personalization infrastructure.
solmaz in ruffled shirt

Solmaz Shahalizadeh

Vice President, Commerce Intelligence
Solmaz is the Vice President of Data Science & Engineering at Shopify. During her time there, she has implemented the company’s first ML products, built their financial data warehouse, led multiple cross-functional teams, and played a critical role in their IPO. In this keynote interview, Sam and Solmaz discuss how she’s helped the company scale its use of machine learning and how that has helped power the company’s growth.

Chris Albon

Director of Machine Learning
Chris Albon joined the Wikimedia Foundation in 2020 as director of machine learning with a mandate to empower the teams producing models for Wikipedia to move more quickly. In this keynote interview, Sam and Chris discuss ML use cases at Wikimedia, the evolution of the organization’s ML infrastructure, their use of Kubernetes and Kubeflow to support the ML workflow, and their ultimate plans to make this infrastructure available to the broader Wikimedia community.

Ya Xu

Head of Data Science
Ya Xu leads the LinkedIn data science practice, consisting of hundreds of researchers distributed across the USA (Sunnyvale, Mountain View, San Francisco, New York), India and Dublin. The team touches every aspect of the organization, helping to inform decisions about new product features, business investments, surfacing economic insights for policy makers, and much more. In this keynote interview, Sam and Ya explore her career transition from a platform-oriented role to one focused on data science products, the horizontal tools and capabilities her team maintains, and some of the ways her team is innovating in areas like experimentation, privacy and fairness.

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Exclusive: Level-Up as an AI/ML Leader With On-Demand Access to TWIMLcon's Half-Day Executive Summit

Fran Bell in Collared jacket outdoors

Franziska Bell

VP, Head of Data and Analytics


Franziska Bell is vice president and head of data and analytics at bp. She is a career data professional and data science leader with experience in multiple industries and companies, including serving as the head of data science platforms at Uber. In this keynote interview, Franziska and Sam explore best practices for successfully launching and deploying ML & AI solutions for the enterprise and strategies for mitigating risks that lead to ML project failures.
Executive Summit Panels

Building the Business Case for ML Platforms

Divya Jain

Divya Jain

Director of ML Platform Adobe

Justin Norman

Justin Norman

Vice President Data Science & Analytics Yelp

Kirk Borne

Kirk Borne

Principal Data Scientist and Executive Advisor Booz Allen Hamilton

Why ML Projects Fail and How to Ensure Their Success

Andy Minteer

Andy Minteer

Senior Director, Digital Transformation - Head AI Products Walmart

Adrien Cartier

Kirk Borne

VP of Data Science


Jurgen Weichenberger

Data Science Senior Principal & Global AI Lead, Resources Assenture

Building Teams and Culture that Support ML Innovation

Ziad Ashgar

Ziad Asghar

Vice President, Product Management Qualcomm

Pardis Noorzad

Pardis Noorzad

Noorzad, Head of Data Science Carbon Health

Ameen Kazerouni

Ameen Kazerouni

Chief Analytics Officer Orangetheory Fitness

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Executive Summit Pass today!

Memorable Insights

“If you’re serious about your data, you want to invest in your platforms.”
Solmaz Shahalizadeh
VP of Commerce Intelligence, Shopify
Ameen Kazerouni
“The currency of an analytics team is trust, not data.”
Ameen Kazerouni
Chief Analytics Officer, Orangetheory Fitness
“It is our obligation to bring our customers on the journey with us. We need to be in the mindset that we are enabling people to do their jobs."
Jurgen Weichenberger
Data Science Senior Principal & Global AI Lead, Resources, Accenture
“Now it’s about productization and treating models less like a crystal chandelier and more like a disposable coffee cup. If you find a better one, use it and throw away the old one.”
Chris Albon
Director of Machine Learning at WikiMedia
“This is the greatest technology of our lifetime, now it’s about getting the tools to be able to do it at scale”
Diego Oppenheimer
CEO of Algorithmia
Justin Norman
“The goal is to produce an ML system that functions reliably and that can replicate that at scale.”
Justin Norman
VP Data Science & Analytics, Yelp


TWIMLcon: AI Platforms is brought to you by Sam Charrington and the team behind the TWIML AI Podcast.

The conference has its roots in a series of podcast interviews and ebooks on the topic of AI Platforms published going back to the fall of 2018. The series – which featured interviews with ML Platforms and Infrastructure engineers and leaders from Facebook, Airbnb, LinkedIn, OpenAI, Shell and Comcast – resonated strongly with listeners and remains one of our most popular series to this day.

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