Distributed deep learning
Please log in to watch this conference skillscast.
After a brief introduction to the nature of distributed systems, from distributed databases, through distributed state, to distributed computation, you will learn how to use Akka Cluster and Akka Persistence to implement such distributed systems. Once the data is safely stored in a journal, it is important to be able to perform deeper analysis. To do so, Jan will show you how to access the data in the journal in a distributed computation program running in Apache Spark. With the mechanics done, Jan will explain how to use (deep) neural networks that can be very easily trained to recognize patterns in the ingested data.
You will learn the advantages and traps of designing distributed domains, data and computation: systems that may become the next generation of financial systems, bringing elasticity, resilience and responsiveness. You will look in particular at systems that consume data from IoT / wearables, and that perform immediate and batch analyses.
YOU MAY ALSO LIKE:
- Production Reactive Systems in Scala with Jan Machacek! (SkillsCast recorded in April 2017)
- Awesome CI/CD Data Pipelines for Distributed Data-Sources (SkillsCast recorded in May 2019)
- Validating Big Data Jobs - Stopping Failures before Production (w/ Spark, BEAM, & friends!) (SkillsCast recorded in December 2018)
Thank you to our sponsors and partners
Platinum
Gold
Silver
Bronze
Partners
Distributed deep learning
Jan Machacek
Jan Machacek is a passionate technologist with hands-on experience of the practical aspects of software delivery (architecture, quality, CI, CD), the project management approaches (applying the principles of agile project management), and mentoring and motivating engineering & business teams.