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’s 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 mind-set shift required to address these issues is understanding that model development is different than software development in fundamental ways. David Talby outlines real-world case studies showing why this is true and 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. You’ll explore 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; and the impact of all of these 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 led business operations for Bing Shopping in the US and Europe with Microsoft’s Bing Group and built and ran distributed teams that helped scale Amazon’s financial systems with Amazon in both Seattle and the UK. David holds a PhD in computer science and master’s degrees in both computer science and business administration.
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