Data Science Best Practices: Continuous Delivery for Machine Learning
Data Science Best Practices: Continuous Delivery for Machine Learning


Machine learning is usually taught from tutorials using small, clean datasets put into data-frames and orchestrated with Jupyter notebooks; all done in one, in-memory, local environment. This is a fine style for presenting a new topic and teaching the main ideas, but unfortunately, these patterns are not conducive to the delivery of real production applications at scale. Real industrial situations involve multiple environments and data sets from databases or other data stores rather than file-based input. They interact with live production systems and must be coordinated with software delivery teams and product owners. They must be production quality, with good design, well-tested and maintainable. This often results in data scientists having to choose between the environment that they are used to, and one that is suitable for delivery to production; and an awkward migration from one to the other. In this workshop, we show how to maintain data science productivity as well as collaborate effectively and deliver value continuously and seamlessly. We demonstrate and guide the participants through CI/CD practices for machine learning and a new pattern of working that avoids most of the pitfalls of the typical approach.

Participants will learn how to utilize new patterns of repeatable continuous model development to collaborate effectively and deliver value continuously and seamlessly in industrial data science projects using Continuous Integration (CI) and Continuous Delivery (CD) practices.

● Github;
● Docker;
● Jenkins;
● Jupyter;
● Python;
● DVC;
● MLFlow;
● Kibana;
● ElasticSearch;


David Johnston is a Principal Data Scientist and founding data scientist of the ThoughtWorks Data Science & Engineering practice. David has over 25 years of experience working with data, data processing pipelines, algorithms, optimization and statistical and machine learning models. David has a Ph.D. in physics and worked previously as a researcher at top universities, NASA and US government labs in the field of cosmology. Since leaving academia he has specialized in helping clients apply these techniques in their business environments with a focus on end-to-end delivery of valuable data-driven products and creating working, maintainable production systems. David is a frequent writer and speaker on data science, artificial intelligence and the importance of applying quality software development best practices toward data science-driven applications.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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