Executive Briefing: Why machine-learned models crash and burn in production and what to do about it
Much progress has been made over the past decade on process and tooling for managing large-scale, multitier cloud apps and APIs, but there is far less common knowledge on best practices for managing machine-learned models (classifiers, forecasters, etc.), especially beyond the modeling, optimization, and deployment process once these models are in production.
A key mindset shift required to address these issues is understanding that model development is different than software development in fundamental ways. David Talby shares real-world case studies showing why this is true and explains what you can do about it, covering key best practices that executives, solution architects, and delivery teams must take into account when committing to successfully deliver and operate data science-intensive systems in the real world.
- Concept drift (Machine-learned models begin degrading as soon as they’re deployed and must adapt to a changing environment.)
- Locality and limited reuse and generalization of models
- A/B testing challenges, which make it very hard in practice to know which model will perform better in production
- Semisupervised and adversarial learning scenarios, which require modeling and optimizing models only once they’re in production
- The impact of all of the above on product planning, staffing, and client expectation management
What you'll learn
- Understand why model development is different than software development and why this matters
David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe; and at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.
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