Microservices for Data Science and Deep Learning
Please log in to watch this conference skillscast.
You will learn about using language agnostic micro services approach to handle data and provide interactive analytics in a Deep Learning / AI startup.
You will explore use cases and an example of a distributed queue based architecture for micro services.
Andrew and John will be demonstrating two different 'frameworks' for microservices in a Big Data architecture (with examples):
Kafka Consumers as microservices - image processing
Spark Jobs as microservices - clustering algorithms + data visualisation
Andrew and John will also share their experiences of using microservices at Tractable.
YOU MAY ALSO LIKE:
- Domain Models in Practice: DDD, CQRS & Event Sourcing with Marco Heimeshoff (Online Course on 2nd - 6th November 2020)
- FullStack Event Sourcing and Applied DDD with Roman Sachse (Online Course on 25th - 27th November 2020)
- London Calling (Online Meetup on 24th September 2020)
- Strategic Domain-Driven Design Tools For Non‑DDD People (Online Meetup on 29th October 2020)
- Debugging Containers on Kubernetes with "kubectl debug" (SkillsCast recorded in July 2020)
- Applied Domain-Driven Design — Full-Stack Event Sourcing (SkillsCast recorded in July 2020)
Thanks to our sponsors
Microservices for Data Science and Deep Learning
John Bradshaw
John is studying for a PhD at the University of Cambridge and the Max Planck Institute for Intelligent Systems, Tübingen in Machine Learning. Prior to that he worked as an engineer at Tractable implementing scalable Machine Learning algorithms.
Andrew Jefferson
Andrew is a software engineer specialising in realtime data systems. Andrew has worked at YC Startups and at Apple on applications ranging from Ticketing to Genetics. Currently building data systems for training and exploiting Deep Neural Networks for Computer Vision.