Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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Continuous intelligence: Moving machine learning into production reliably

Danilo Sato (ThoughtWorks), Christoph Windheuser (ThoughtWorks)
9:00–12:30 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
 Location: Capital Suite 14
Secondary topics:  Model lifecycle management
Average rating: ****.
(4.31, 13 ratings)
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Who is this presentation for?

  • Data scientists, machine learning engineers, and anyone that needs to deploy ML/AI models to production reliably

Level

Intermediate

Prerequisite knowledge

  • Basic knowledge of machine learning

Materials or downloads needed in advance

  • A laptop (Linux, macOS, or Windows) with a working Docker environment installed
  • A GitHub account
  • A local working Python 3 installation with pip

What you'll learn

  • Learn how to apply continuous delivery principles and tools to machine learning
  • Create your own deployment pipelines to test, integrate, and take versioned ML models to production reliably

Description

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.

Photo of Danilo Sato

Danilo Sato

ThoughtWorks

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.

Photo of Christoph Windheuser

Christoph Windheuser

ThoughtWorks

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.