Selecting Tools for Developing, Monitoring and Maintaining ML Models


Date:  Wednesday, May 24

Start Time: 10:50 am

End Time: 11:20 am

With the boom in tools for developing, monitoring and maintaining ML models, data science teams have many options to choose from. Proprietary tools provided by cloud service providers are enticing, but teams may fear being locked in—and may worry that these tools are too costly or missing important features when compared with alternatives from specialized providers. Fortunately, most proprietary, fee-based tools have an open-source component that can be integrated into a home-grown solution at low cost. This can be a good starting point, enabling teams to get started with modern tools without making big investments and leaving the door open to evolve tool selection over time. In this talk, we’ll present a step-by-step process for creating an MLOps tool set that enables you to deliver maximum value as a data scientist. We’ll share how we built pipelines for model development and put them into production using open-source projects.

Track

Session Speakers

  • Parshad Patel is a Data Scientist at Yummly (a Whirlpool subsidiary), a company with a mission to be the smartest and most helpful food platform in existence. He has over a decade of diverse experience using applied machine learning to solve computer vision and general ML problems for security robots and consumer products. He also has a deep interest in hardware and has built start-ups to tackle waste management and retrofitting defense systems. He has successfully led and built several machine learning projects, including hardware building, labeling, pipeline building, model training, deployment and monitoring. He currently leads development of end-to-end machine learning pipelines, MLOps and edge-based models to enhance user experiences with Yummly’s Smart Thermometer. Parshad holds a master’s degree in Robotics from Carnegie Mellon University.


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Selecting Tools for Developing, Monitoring and Maintaining ML Models