TWIMLcon: AI Platforms

The 2019 Conference for platforms, tools, technologies, and practices necessary to enable and scale machine learning and AI in the enterprise.

Conference sessions now available for Free On Demand viewing!

Discover how leading organizations deliver high-velocity ML and AI at scale

50+ Worldclass ML and MLOPS Practitioners

Learn from 50+ world-class data science, ML and MLOps practitioners and leaders—from companies like Netflix, Shopify, LinkedIn, Spotify, Google, Walmart, iRobot, Adobe, Intuit, Yelp, Salesforce, Prosus, Qualcomm, and more.

50+ Hours of keynotes and presentations

Access 20+ hours of professionally produced keynotes, presentations, demos, and panels discussing the most important issues for practitioners and leaders building and deploying AI and ML at scale.

Learn and develop a plan for deploying AI at scale

Develop a practical blueprint for delivering machine learning efficiently and at scale.

Advance Your Skills and Accelerate Your Team!

Discover the state-of-the-art the practices, platforms, tools, and technologies for scaling and accelerating enterprise ML and AI.

With On-Demand video access you will learn:
Overcoming the barriers to getting machine learning and deep learning models into production
How to apply MLOps and DevOps to the machine learning workflow in your enterprise
The latest approaches to platform technologies for accelerating and scaling machine learning and deep learning in the enterprise
Experiences and lessons-learned in delivering platform and infrastructure support for data management, experiment management, and model deployment
Platform deployment stories from leading companies like Google, Facebook, Airbnb, and more
Organizational and cultural best practices for success

Memorable Conference Insights

TWIMLcon 2019 keynote speakers and panelists have amazing insight for how to deploy and scale AI and ML/OPS for your organization.

If you’re serious about your data, you want to invest in your platforms.

Franziska Bell, Ph.D. Director of Data Science Platforms, Uber

The goal is to produce an ML system that functions reliably and that can replicate that at scale.

Khari Johnson Senior AI Staff Writer, VentureBeat

This is the greatest technology of our lifetime, now it’s about getting the tools to be able to do it at scale

Eric Colson Chief Algorithms Officer Emeritus, Stitch Fix

The currency of an analytics team is trust, not data.

Ameen Kazerouni Chief Analytics Officer, Orangetheory Fitness

Keynote Speakers

This years keynote speakers hold positions at the top data science, ML/OPS tech companies in the world.

Andrew Ng

Founder

Landing AI

Deepak Agarwal

VP of Artificial Intelligence

Franziska Bell

Director of Data Science, Platforms

Uber

Featured Speakers

Experience the best of TWIMLcon 2019 On Demand, and learn from over 50 world-class speakers.

Seth Weidman

Data Scientist

Facebook

Ameen Kazerouni

Chief Analytics Officer

Orangetheory Fitness

Featured Sessions

Experience the best of TWIMLcon 2019 On Demand and get access to over 25 hours of video sessions.

Chaos and Pain in Machine Learning, and the ‘DevOps for ML Manifesto’

Perspective

Most AI/ML projects start shipping models into production, where they...

Increasing the Impact of AI Through Better Software

Technology

The state of artificial intelligence is continuing to advance rapidly,...

Model Versioning: Why, When, and How

Technology

Models are the new code: while machine learning models are increasingly being used to make critical product and business decisions, the process of developing and deploying ML models remain ad-hoc. In this talk, we draw upon our experience with ModelDB and Verta to present best practices and tools for model versioning and ensure high quality of deployed ML models.

Operationalizing Machine Learning at Levi’s

Case Study

In this talk, we will cover the journey we at Levi's undertook to go from a fully outsourced model to over a dozen internally-built Machine Learning models deployed in production that are ROI positive and that solve real business problems, in about two years, as well as challenges we've faced, key design methodologies and more

Productionizing Machine Learning Models at Scale with Kubernetes

Case Study

Productionizing machine learning models in an organization is difficult. The goal of this presentation is to discuss how Kubernetes can be leveraged to train, deploy, and monitor models in production settings, as well as lessons learned from using Kubernetes to productionize machine learning workloads at 2U.

Removing Unfair Bias in the Machine Learning Pipeline

Technology

Machine learning models are increasingly being used to make critical decisions that impact people’s lives. Learn how to measure bias in your data sets & models, and how to apply the fairness algorithms to reduce bias across the machine learning pipeline.

About Sam Charrington and the TWIML AI Podcast

The TWIML AI Podcast is hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Sam’s research is focused on the business and consumer application of machine learning and AI, bringing AI-powered products to market, and AI-enabled and -enabling technology platforms.