Bridging the ML Gap: How to Get Your Team from Prepare to Deploy

Featuring 

As companies invest more heavily in AI/ML projects, teams will need to move rapidly from experimentation into production. But even with new advances in technology and processes, many teams struggle to get their projects into the later stages of the Machine Learning Lifecycle.

Join us as representatives from the AI Infrastructure Alliance (AIIA), Pachyderm, Superb AI, and WhyLabs discuss the big trends in MLOps, useful strategies for accelerating model development, and best practices all teams can incorporate.

Lee Baker - General Secretary at the AI Infrastructure Alliance

Lee is the General Secretary at the AI Infrastructure Alliance. Based out of the UK, he is responsible for the proceedings and growth of the AI Infrastructure Alliance (AIIA) as well as working at Pachyderm to deploy programs and initiatives that enable customer-facing teams to execute the core aspects of their jobs more effectively. When not shuttling his 3 children around, he can most often be found cycling, running and swimming around England's South Coast.

Danny Leybzon - MLOps Architect at WhyLabs

Danny D. Leybzon has worn many hats, all of them related to data. He studied computational statistics at UCLA, before becoming first an analyst and then a product manager at a big data platform named Qubole. He went on to be the primary field engineer for data science and machine learning at Imply, before taking on his current role as MLOps Architect at WhyLabs. He has worked to evangelize machine learning best practices, talking on subjects such as distributed deep learning, productionizing machine learning models, automated machine learning, and lately has been talking about AI observability and data logging. When Danny's not researching, practicing, or talking about data science, he's usually doing one of his numerous outside hobbies: rock climbing, backcountry backpacking, skiing, etc.

James Le - Data Advocate at Superb AI

James Le currently runs Data Relations at Superb AI, a Series A ML data management startup. As part of his role, James executes content and partnership initiatives - while working cross-functionally with growth, product, customer success, sales, marketing, and community functions to drive Go-To-Market strategy.

Before joining Superb AI, he completed his Computer Science Master's degree at RIT, where his research thesis lies at the intersection of deep learning and recommendation systems. Outside of work, he is highly active in the broader data and ML community - writing data-centric blog posts, hosting a data-focused podcast, and organizing in-person community events.

Jimmy Whitaker - Data Science Evangelist at Pachyderm

Jimmy Whitaker is the Data Science Evangelist at Pachyderm. He focuses on creating a great data science experience and sharing best practices for how to use Pachyderm. When he isn’t at work, he’s either playing music or trying to learn something new, because “You suddenly understand something you’ve understood all your life, but in a new way.”

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