Machine Learning (ML)–and its subset Deep Learning (DL)–have evolved in the last decade to take an often hidden role in everyday system infrastructures. From self-driving cars to real- time credit card fraud detection to real-time personalization, organizations are using ML to improve customer interactions with systems that can train themselves–using algorithms and historical data–to actively manage complex scenarios without being explicitly programmed.
Yet in isolation, even the best ML algorithm will have limited usefulness to businesses. To create advanced offerings to set your business apart from your competitors, streaming and Fast Data applications must be able to process, learn from, and respond to a never ending stream of data.
This webinar by Emre Velipasaoglu, Principal Data Scientist at Lightbend, is for busy Architects and Managers looking to get a handle on what ML is really all about, the ideal use cases for ML and how getting it right can benefit your streaming and Fast Data application architectures. At the end of this presentation, you will have learned about:
If you are a developer or architect looking to learn more, consider our Fast Data Platform Technical Overview or Fast Data Architectures for Streaming Applications, by Dean Wampler (VP of Fast Data Engineering at Lightbend). If you are a team lead or manager, review our Reactive Launch engagement and read some of our streaming and Fast Data success stories from enterprises like Credit Karma, Weight Watchers, Zalando, Swisscom and Intel:
As always, if you'd like to get in contact with a Lightbend representative, you can schedule a brief, 20-min introductory conversation with our team here:
