So you want to include a machine learning component in your IT systems? The process is a little more involved than clicking through an AI tutorial on your laptop. It’s not just the first working model you run that you need to consider; you also need to think about things like integration, scaling, and testing. What’s more, postlaunch, you’ll want to continuously adapt your model to respond to the changing environment.
ThoughtWorks pioneered continuous delivery—a set of tools and processes that ensure that software under development can be reliably released to production at any time and with high frequency.
Danilo Sato and Christoph Windheuser demonstrate how to apply continuous delivery to machine learning—what’s known as continuous intelligence. In a live scenario, you’ll change a machine learning model in a development environment, test its new performance, and, depending on the outcome, automatically deploy the new model into a production environment. The tech stack for this scenario will be Python, DVC (Data Science Version Control), and GoCD.
Danilo Sato is a principal consultant at ThoughtWorks with more than 17 years of experience in many areas of architecture and engineering: software, data, infrastructure, and machine learning. Balancing strategy with execution, Danilo helps clients refine their technology strategy while adopting practices to reduce the time between having an idea, implementing it, and running it in production using the cloud, DevOps, and continuous delivery. He is the author of DevOps in Practice: Reliable and Automated Software Delivery, is a member of ThoughtWorks’ Technology Advisory Board and Office of the CTO, and is an experienced international conference speaker.
Christoph Windheuser is the global head of intelligent empowerment at ThoughtWorks, where he’s responsible for the company’s positioning on data management, machine learning, and artificial intelligence. Previously, he held a number of positions in the IT industry at companies like SAP and Capgemini. Christoph studied computer science in Bonn (Germany), Pittsburgh (USA), and Paris (France), and he holds a PhD in speech recognition with artificial neural networks.
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