Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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Continuous intelligence: Keeping your AI application in production

Arif Wider (ThoughtWorks), Emily Gorcenski (ThoughtWorks)
16:3517:15 Wednesday, 1 May 2019
Secondary topics:  Model lifecycle management
Average rating: ***..
(3.90, 10 ratings)

Who is this presentation for?

  • Data engineers, data scientists, and architects



Prerequisite knowledge

  • A basic understanding of software engineering principles and software testing

What you'll learn

  • Understand continuous delivery concepts within a data science context
  • Explore the challenges of applying CI/CD in data science
  • Discover use cases and benefits for continuous intelligence


It’s already challenging to transition a machine learning model or AI system from the research space to production, and maintaining that system alongside ever-changing data is an even greater challenge. In software engineering, continuous delivery practices have been developed to ensure that developers can adapt, maintain, and update software and systems cheaply and quickly, enabling release cycles on the scale of hours or days instead of weeks or months.

Nevertheless, in the data science world, continuous delivery is rarely applied holistically—due in part to different workflows: data scientists regularly work on whole sets of hypotheses, whereas software engineers work more linearly even when evaluating multiple implementation alternatives. Therefore, existing software engineering practices cannot be applied as is to machine learning projects.

Arif Wider and Emily Gorcenski explore continuous delivery (CD) for AI/ML along with case studies for applying CD principles to data science workflows. Join in to learn how they drew on their expertise to adapt practices and tools to allow for continuous intelligence—the practice of delivering AI applications continuously.

Photo of Arif Wider

Arif Wider


Arif Wider is a lead consultant and developer at ThoughtWorks Germany, where he enjoys building scalable applications, teaches Scala, and consults at the intersection of data science and software engineering. Previously, he was a researcher with a focus on data synchronization, bidirectional transformations, and domain-specific languages.

Photo of Emily Gorcenski

Emily Gorcenski


Emily Gorcenski is lead data scientist at ThoughtWorks. Emily has over 10 years of experience in scientific computing and engineering research and development. Her background is in mathematical analysis, with a focus on probability theory and numerical analysis. She’s currently working in Python development, though she also has experience with C#/.Net, Unity3D, SQL, and MATLAB as well as statistics and experimental design. Previously, she was principal investigator in a number of clinical research projects.