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:
- Real-time Systems with Spark Streaming and Kafka (in London on 30th September - 1st October 2019)
- Fast Track to Android Architecture (in London on 21st - 23rd October 2019)
- Clojure eXchange 2019 (in London on 2nd - 3rd December 2019)
- Scala eXchange London 2019 (in London on 12th - 13th December 2019)
- Keynote by Luca Mezzalira on Demystifying Micro-Frontends (in London on 23rd September 2019)
- Resilience and Chaos Engineering at Hotels.com (in London on 23rd September 2019)
- Revving up with Reinforcement Learning (SkillsCast recorded in September 2019)
- The importance of Data Visualisation (SkillsCast recorded in September 2019)
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.
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:
- Real-time Systems with Spark Streaming and Kafka (in London on 30th September - 1st October 2019)
- Fast Track to Android Architecture (in London on 21st - 23rd October 2019)
- Clojure eXchange 2019 (in London on 2nd - 3rd December 2019)
- Scala eXchange London 2019 (in London on 12th - 13th December 2019)
- Keynote by Luca Mezzalira on Demystifying Micro-Frontends (in London on 23rd September 2019)
- Resilience and Chaos Engineering at Hotels.com (in London on 23rd September 2019)
- Revving up with Reinforcement Learning (SkillsCast recorded in September 2019)
- The importance of Data Visualisation (SkillsCast recorded in September 2019)
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.