#MLOps World Keynote Clips 04: #MLOps Problem (and Solution) Space!

In this fourth and final clip, we dive into the actual framework for solving the biggest problems facing ML practitioners who seek to create business value through AI product development today.

From product, to infrastructure, to process, to people problems, solutions do exist!

Talk deets!
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"Business-Value Best Practices for MLOps: Building #DataScience into #DigitalProducts"

Abstract:

The most valuable tech companies in the world have successfully leveraged network effects and data to create unparalleled business value and competitive advantage. Yet today, it still remains difficult to replicate their success. Why? While being able to operationalize ML in production environments is incredibly important, other barriers to value creation remain. Integrating ML into existing digital product paradigms requires a holistic approach to AI-first product management, both from technical and business perspectives. In this talk, a framework for AI-first product management is presented that considers product, infrastructure, process, and people problems in turn. These problems correspond to answerable questions about the why, what, how, and who of MLOps. Best practices for digital product portfolios and pipelines are discussed through the lens of maximizing the business value of MLOps for any organization in 2022.

What You'll Learn:

Actionable recommendations about MLOps, especially about what not to do. Maybe even some guidance or additional confidence about where to allocate limited resources to maximize value from MLOps initiatives.

https://mlopsworld.com/VANCOUVER/
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Tunes!
Kevin MacLeod, Cool Blast - Disco Ultralounge: https://youtu.be/wsghgqJgBv4

Get in touch with feedback or ideas for more lessons!
Unautomatablog: unautomatable.substack.com
Twitter: twitter.com/GregOnLock
LinkedIn: linkedin.com/in/gregloughnane

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