SkillsCast coming soon.
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:
- Lightning Talks 2 (SkillsCast recorded in December 2015)
- Fast Track to Machine Learning with Louis Dorard (in London on 15th - 17th July 2019)
- Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
- Infiniteconf 2019 - The conference on Big Data and AI (in London on 4th - 5th July 2019)
- ProgNET London 2019 (in London on 11th - 13th September 2019)
- Causal inference and the data-fusion problem (in London on 18th June 2019)
- The wonders of IBM: Watson, artificial intelligence, Swift coding and databases! (in London on 19th June 2019)
- Accessing Snap ML (SkillsCast recorded in June 2019)
- Snap Machine Learning (SkillsCast recorded in June 2019)
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.