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Data in DepthAuthor: Mountain Point
Data in Depth explores the world of advanced analytics, business intelligence, and machine learning within the context of the manufacturing industry. In each episode, we talk with industry leaders and analytics experts to help manufacturers gain a 360-degree view of the shop floor, their business processes, and their customers. We dig into the concepts of descriptive, prescriptive, and predictive analytics to help solve modern manufacturing problems. From MRP to quality control, from field service to customer experience, our conversations are designed to spur innovative, data-driven thinking for those working to build the factories of the future. Language: en-us Genres: Business, Management, Technology Contact email: Get it Feed URL: Get it iTunes ID: Get it |
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Demystifying Serverless Machine Learning
Episode 12
Monday, 29 June, 2020
In this episode, we sat down with Carl Osipov with CounterFactual.AI and the author of Serverless Machine Learning In Action. Carl shared some real-world use cases for serverless machine learning and identified strategies to get the most from a machine learning investment. “One of the things that happens at the beginning of a machine learning project — and this is a well-known problem for data scientists and machine learning practitioners — is spending way too much time cleaning up their data sets and focusing on things like data quality instead of actually building out machine learning solutions. I think, as practitioners, machine learning developers and engineers have created a set of techniques over the past few years to help formalize and accelerate this process. But it’s still a concern, especially if you think about scenarios that are common to manufacturing where different data silos have to come together for a machine learning system. This also happens in the scenarios where manufacturers acquire companies and then integrate data and use that data for machine learning systems. What happens is that if companies don’t actually have a rigorous approach for transitioning their machine learning systems code into operations, they find themselves in a situation where data scientists and machine learning engineers actually end up doing a lot of operations involved in putting machine learning systems into production. So what I’m describing here is what I call an ML ops trap. This machine learning operations trap, where these highly compensated practitioners are essentially spending their time working on something that’s not their core competency.” Connect with Carl on LinkedIn.
