ABOUT THE TALK: While giving a talk to a group of up-and-coming data scientists, a question that surprised Dean Pleban was: "When you say “production”, what exactly do you mean?"

In this talk, Dean defines what production actually means. I’ll present a first-principles, step-by-step approach to thinking about deploying a model to production. He will talk about challenges you might face in each step, and provides further reading if you want to dive deeper into each one.

ABOUT THE SPEAKER: Dean Pleban has a background combining physics and computer science. He’s worked on quantum optics and communication, computer vision, software development and design. He’s currently CEO at DagsHub, where he builds products that enable data scientists to work together and get their models to production, using popular open source tools. He’s also the host of the MLOps Podcast, where he speaks with industry experts about ML in production.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

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ABOUT THE TALK As machine learning models are becoming more common in production, organizations are recognizing the significance of continuous validation, and are integrating automated testing into their CI/CD pipelines to ensure that their models remain relevant and are trustworthy. However, with constantly changing data and black-box logic, testing these models can be a daunting task.

In this talk, we explore the common pitfalls of ML models and best practices for testing them. We demonstrate how to use the deepchecks open source package to validate models and data during the research and CI/CD phases.

ABOUT THE SPEAKER Shir Chorev is the co-founder and CTO of Deepchecks, an MLOps startup for continuous validation of ML models and data. Previously, Shir worked at the Prime Minister’s Office and at Unit 8200, conducting and leading research in various Machine Learning and Cybersecurity related challenges.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

ABOUT THE TALK: Quality ML at scale is only possible when we can reproduce a specific iteration of the ML experiment–and this is where data is key.

In this talk, you will learn how to use a data versioning engine to intuitively and easily version your ML experiments and reproduce any specific iteration of the experiment.

This talk will demo through a live code example: -Creating a basic ML experimentation framework with lakeFS (on Jupyter notebook) -Reproducing ML components from a specific iteration of an experiment Building intuitive, zero-maintenance experiments infrastructure -All with common data engineering stacks & open source tooling.

ABOUT THE SPEAKER: Vino Duraisamy is a developer advocate at lakeFS, an open-source platform that delivers git-like experience to object store based data lakes. She has previously worked at NetApp (on data management applications for NetApp data centers), on data teams of Nike and Apple, where she worked mainly on batch processing workloads as a data engineer, built custom NLP models as an ML engineer and even touched upon MLOps a bit for model deployments.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

ABOUT THE TALK:
Our traditional testing and CI methods for Data Science are not working, but we can't just give up on providing guardrails.

As engineers, how do you solve ML testing?

In this talk, Emily Curtain discusses: - abstracting, decoupling, and separating concerns - keeping pytest only where it belongs - substituting testing for observability in appropriate places - applying data reliability practices and thereby solving some problems at the source - by honoring Data Scientists' mental models, and ways of working

ABOUT THE SPEAKER: Emily Curtin is a Staff MLOps Engineer at Intuit Mailchimp. She leads a crazy good team focused on helping Data Scientists do higher quality work faster and more intuitively.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

ABOUT THE TALK: Learn about "fun-sized companies" (SMB's, small startups, etc) and how to build a fully fledged MLOps platform from scratch using the best OSS tools out there in under a day.

This talk covers: -The main problems MLOps tries to solve -The most common tools being used & their drawbacks -OSS projects & tools that have been developed in the past 2-3 years and how do they solve some of the pain points of the prior tools -The realistic roadmap for companies that are forever “not-Google” scale but want to continue improving their data and ML maturity

ABOUT THE SPEAKER: Mikiko Bazeley is Head of MLOps at Featureform, a Virtual Feature Store. He has previously taken on engineer, data scientist, and data analyst roles for companies including Mailchimp (Intuit), Teladoc, Sunrun, Autodesk along with a handful of early stage startups.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai