Before you can even get started building large-scale data analytic systems, you need to start with one crucial element: data. Collecting data, especially collecting lots of data, is harder than it seems. Data ingested with the wrong data model can be worse than no data at all, and a data collection system that is too slow can bring an entire platform grinding to a halt. Don't panic! Scalable, non-destructive data collection is possible. This talk will focus on strategies for data collection based on real world experience building large scale machine learning systems. It will introduce ideas from the emerging paradigm of reactive machine learning that are based on older ideas about immutable facts and pervasive, intrinsic uncertainty.
YOU MAY ALSO LIKE:
- Fast Track to Machine Learning with Louis Dorard (in London on 21st - 23rd May 2018)
- Brian Sletten's Data Science with R Workshop (in London on 2nd - 4th July 2018)
- Infiniteconf 2018 - The conference on Big Data and Fast Data (in London on 5th - 6th July 2018)
- Blockchain by Brian Sletten (in London on 9th - 10th July 2018)
Collecting Uncertain Data the Reactive Way
Jeff Smith builds large-scale machine learning systems using Scala and Spark. For the past decade, he has been working on data science applications at various startups in New York, San Francisco, and Hong Kong. He is a frequent blogger and the author of an upcoming book from Manning on how to build reactive machine learning systems using Scala, Akka, and Spark.