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)
- Brian Sletten's Data Science with Python Workshop (in London on 18th - 20th November 2019)
- Fast Track to Machine Learning with Louis Dorard (in London on 2nd - 4th December 2019)
- Scala eXchange London 2019 (in London on 12th - 13th December 2019)
- Practical ML 2020 (in London on 2nd - 3rd July 2020)
- Conditional Random Fields: Probabilistic Models for Segmenting and Labelling Sequence Data. (in London on 28th October 2019)
- A Guide to the Market Promise of Automagic AI-Enabled Detection and Response (in London on 29th October 2019)
- How AI can be used to enable assisted living for the ageing population (SkillsCast recorded in October 2019)
- The importance of DataOps (SkillsCast recorded in October 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.