Engineering Tomorrow’s Decisions: Building Predictive Analytics Projects with Open Source
Predictive analytics is used in various organisations to transform existing data into future decisions. Churn prevention, customer segmentation or risk modelling apply across various industries, from financial services to retail, telecommunications to public sectors. End users benefit from better experiences and increased productivity, whereas companies stay competitive in the market, ultimately improving microeconomics.
At the same time, data scientists need to stay up to date and develop systems that can handle a growing volume of data and machine learning models with ever-improving performance. They look for solutions that can handle more than one application and ensure the scalability and reproducibility of the projects.
This presentation will cover how to build predictive analytics applications at scale, including ways to take advantage of the continuously growing volumes of data. This talk will feature the main use cases of predictive analytics and how to roll them into your business, ensuring you leave the session geared with knowledge on how to apply predictive analytics within your company using open source tooling such as Spark or Kubeflow.
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