Building a Scalable Big Data Machine Learning System with Spark and CDAP
THE RISE OF THE INTERNET has led to the rise of e-commerce websites.
With the mushrooming of the number of e-commerce sites today there is surge in demand by customers. In order to provide the most relevant recommendations to the visitors, ecommerce websites regularly rely on recommendation engines, which are complex data applications fueled by data ingestion, data preparation and machine learning processes. In addition to building machine learning models, recommendation engines require the ingestion and storage of user data in real-time, the training and storing of the models and their results, as well as the monitoring and improvement of the model performance.
This webinar showcases the ways and means to use the Cask Data Application Platform (CDAP), a unified integration platform for big data, to operationalize Machine Learning models for your ecommerce website by simplifying data collection, testing and continuous improvement.
Agenda
- CDAP Application Platform.
- Operationalize Machine Learning models for your ecommerce website.
- Show & Tell of how to develop a Cask Solution.
- Live Demo.
- Summary of lessons learned and takeaways.
Speaker:
Russ Savage
About the Speaker:
Russ Savage is leading the application engineering team at Cask, focusing on building end to end big data applications using the Cask Data Application Platform (CDAP). He believes that the true value of Hadoop and other big data technologies is only unlocked when they are used to solve problems and provide value to the business. He previously worked at Elastic as a solutions architect.
Date: March 21
Time: 11 am to 11:30 am PST
Technologies Showcased:
Cask Data Application Platform (CDAP)
Machine Learning Models
BigData
Spark
Team
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