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Two Tools to get you 90% Operational ML, The Future of ML and Data Platforms

Posted Feb 03
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SPEAKERS
David Aronchick
David Aronchick
David Aronchick
Program Manager, Azure Innovations @ Azure

David leads works in the Azure Innovation Office on Machine Learning. This means he spends most of my time helping humans to convince machines to be smarter. He am only moderately successful at this.

Previously, he led product management for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded the Kubeflow project. He has also worked at Microsoft, Amazon and Chef and co-founded three startups.

When not spending too much time in service of electrons, he can be found on a mountain (on skis), traveling the world (via restaurants) or participating in kid activities, of which there are a lot more than he remembers than when he was that age.

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David leads works in the Azure Innovation Office on Machine Learning. This means he spends most of my time helping humans to convince machines to be smarter. He am only moderately successful at this.

Previously, he led product management for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded the Kubeflow project. He has also worked at Microsoft, Amazon and Chef and co-founded three startups.

When not spending too much time in service of electrons, he can be found on a mountain (on skis), traveling the world (via restaurants) or participating in kid activities, of which there are a lot more than he remembers than when he was that age.

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Michael Del Balso
Michael Del Balso
Michael Del Balso
CEO & Co-founder @ Tecton

Mike is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. Previous to that, he worked on Google Maps. He holds a BSc in Electrical and Computer Engineering summa cum laude from the University of Toronto.

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Mike is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. Previous to that, he worked on Google Maps. He holds a BSc in Electrical and Computer Engineering summa cum laude from the University of Toronto.

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Willem Pienaar
Willem Pienaar
Willem Pienaar
Co-creator @ Feast

Willem is currently a tech lead at Tecton where he leads the development of Feast, an open-source feature store for machine learning. Previously he led the ML platform team at Gojek, the Southeast Asian decacorn, which supports a wide variety of models and handles over 100 million orders every month. His main focus areas are building data and ML platforms, allowing organizations to scale machine learning and drive decision making. In a previous life, Willem founded and sold a networking startup.

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Willem is currently a tech lead at Tecton where he leads the development of Feast, an open-source feature store for machine learning. Previously he led the ML platform team at Gojek, the Southeast Asian decacorn, which supports a wide variety of models and handles over 100 million orders every month. His main focus areas are building data and ML platforms, allowing organizations to scale machine learning and drive decision making. In a previous life, Willem founded and sold a networking startup.

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SUMMARY

The MLOps tooling landscape is confusing. There’s a complicated patchwork of products and open-source software that each cover some subset of the infrastructure requirements to get ML to production. In this session - we’ll focus on the two most important platforms: model management platforms and feature stores. Model management platforms such as Kubeflow help you get models to production quickly and reliably. Feature stores help you easily build, use, and deploy features. Together, they cover requirements to get models and data to production - the two most important components of any ML project. In this panel discussion, we’ll be joined by David Aronchick (Co-Founder of Kubeflow), Mike Del Balso (Co-Founder of Tecton) and Willem Pienaar (Creator of Feast). These experts will share their perspective on the challenges of Operational ML and how to build the ideal infrastructure stack for MLOps. They’ll talk about the importance of managing models and data with the same engineering efficiency and best practices that we’ve been applying to application code. They’ll discuss the role of Kubeflow, Feast and Tecton, and share their views on the future of MLOps tooling., The MLOps tooling landscape is confusing. There’s a complicated patchwork of products and open-source software that each cover some subset of the infrastructure requirements to get ML to production. In this session - we’ll focus on the two most important platforms: model management platforms and feature stores. Model management platforms such as Kubeflow help you get models to production quickly and reliably. Feature stores help you easily build, use, and deploy features. Together, they cover requirements to get models and data to production - the two most important components of any ML project. In this panel discussion, we’ll be joined by David Aronchick (Co-Founder of Kubeflow), Mike Del Balso (Co-Founder of Tecton) and Willem Pienaar (Creator of Feast). These experts will share their perspective on the challenges of Operational ML and how to build the ideal infrastructure stack for MLOps. They’ll talk about the importance of managing models and data with the same engineering efficiency and best practices that we’ve been applying to application code. They’ll discuss the role of Kubeflow, Feast and Tecton, and share their views on the future of MLOps tooling., The MLOps tooling landscape is confusing. There’s a complicated patchwork of products and open-source software that each cover some subset of the infrastructure requirements to get ML to production. In this session - we’ll focus on the two most important platforms: model management platforms and feature stores. Model management platforms such as Kubeflow help you get models to production quickly and reliably. Feature stores help you easily build, use, and deploy features. Together, they cover requirements to get models and data to production - the two most important components of any ML project. In this panel discussion, we’ll be joined by David Aronchick (Co-Founder of Kubeflow), Mike Del Balso (Co-Founder of Tecton) and Willem Pienaar (Creator of Feast). These experts will share their perspective on the challenges of Operational ML and how to build the ideal infrastructure stack for MLOps. They’ll talk about the importance of managing models and data with the same engineering efficiency and best practices that we’ve been applying to application code. They’ll discuss the role of Kubeflow, Feast and Tecton, and share their views on the future of MLOps tooling., Machine learning, data analytics, and software engineering are converging as data-intensive systems become more ubiquitous. Erik Bernhardsson, ex-CTO at Better and former Spotify machine learning lead, and Mike Del Balso, CEO at Tecton and former Uber machine learning lead and co-creator of Michelangelo sit down to chat with us today. These two jammed with us about building machine learning platform systems and teams, the modern operational data stack and how it allows more machine learning applications to thrive, and how to successfully take advantage of data in the process of building products and companies.

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