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)
In this session we will explain how to use metadata to automate delivery and operations of a data platform. By injecting automation into the delivery processes we 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, 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. A magic 8-ball for the big data stack would give an enterprise a glimpse into its future needs and would enable effective and cost-efficient project and operational planning. This talk covers how to build that magic 8-ball, a decomposable time-series model, for optimal cost and resource allocation for the big data stack. Read more.
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16:3517:15 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 7
Constantin Muraru (Adobe), Dan Popescu (Adobe)
Obtaining servers to run your realtime application has never been easier. Cloud providers have removed the cumbersome process of provisioning new hardware, to suite your needs. What happens though when you wish to deploy your (web) applications frequently, on hundreds or even thousands of servers in a fast and reliable way with minimal human intervention? This session addresses this precise topic. Read more.
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14:5515:35 Thursday, 2 May 2019
Sonal Goyal (Nube)
Enterprise data on customers, vendors, products etc is 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. This talk covers a modern master data application using Spark, Cassandra, ML and Elastic. Read more.