Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: Automation in data science and big data sessions

As the use of machine learning and analytics become more widespread, we’re beginning to see tools that allow data scientists and data engineers to scale and tackle many more problems and maintain more systems. This includes automation tools for the many stages involved in data science including data preparation, feature engineering, model selection and hyperparameter tuning, as well as in data engineering and data operations.

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12:0512:45 Wednesday, 1 May 2019
Peter Billen (Accenture)
Peter Billen explains how to use metadata to automate delivery and operations of a data platform. By injecting automation into the delivery processes, you shorten the time to market while improving the quality of the initial user experience. Typical examples include data profiling and prototyping, test automation, continuous delivery and deployment, and automated code creation. Read more.
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16:3517:15 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Shivnath Babu (Unravel Data Systems | Duke University), Alkis Simitsis (Micro Focus)
Cost and resource provisioning are critical components of the big data stack. Shivnath Babu and Alkis Simitsis detail how to build a Magic 8 Ball for the big data stack—a decomposable time series model for optimal cost and resource allocation that offers enterprises a glimpse into their future needs and enabling effective and cost-efficient project and operational planning. Read more.
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16:3517:15 Wednesday, 1 May 2019
Data Engineering and Architecture, Expo Hall
Location: Expo Hall 2 (Capital Hall N24)
Constantin Muraru (Adobe), Dan Popescu (Adobe)
With the current crop of cloud providers, obtaining servers to run your real-time application has never been easier. But what happens though when you wish to deploy your (web) applications frequently, on hundreds or even thousands of servers, in a fast, reliable way, with minimal human intervention? Constantin Muraru and Dan Popescu tell you how to tackle this challenge. Read more.
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14:5515:35 Thursday, 2 May 2019
Sonal Goyal (Nube)
Enterprise data on customers, vendors, and products is often siloed and represented differently in diverse systems, hurting analytics, compliance, regulatory reporting, and 360 views. Traditional rule-based MDM systems with legacy architectures struggle to unify this growing data. Sonal Goyal offers an overview of a modern master data application using Spark, Cassandra, ML, and Elastic. Read more.