Online machine learning in streaming applications
Who is this presentation for?
- Machine learning (ML) engineers, data scientists, data engineers, data architects, and IoT experts
Applications such as smart homes, smart monitoring of industrial environments, augmented reality in retail, and autoconnected cars are driving a new era in online ML, where ML algorithms have been moved to the edge instead of the cloud. These applications are constrained in terms of resources like power, CPU, memory, etc. and responsiveness. Data flows in the system and the application needs to interact with the surrounding environment in a given time window.
Stavros Kontopoulos and Debasish Ghosh explore the foundations of the algorithmic aspects (Hoeffding Adaptive Trees, classic sketch data structures, and drift detection algorithms) of these applications and dive into the details of how they can be implemented and deployed efficiently in production. They evaluate production concerns like performance (latency, memory footprint, etc.), techniques for updating models being served in a running pipeline and future trends like feature space representation and sampling, and tools to use for the actual implementation and deployment of these algorithms.
The concepts they detail can also be applied in a cloud setting, so many of the practical aspects they cover are universal and will benefit any practitioner of ML. The main focus is cutting-edge applications and technologies, and you don’t want to miss a glance in the future.
- A basic understanding of ML, streaming, tools for writing streaming applications like Apache Spark, Apache Flink, and tools for application deployment and orchestration like Kubernetes
What you'll learn
- Gain an understanding of online ML, combining ML with streaming, and tools and best practices for deploying online ML-based streaming applications
Stavros Kontopoulos is a senior engineer on the data systems team at Lightbend. He implement Lightbend’s fast data strategy. Previously, he worked building software solutions that scale in different verticals like telecoms and marketing. His interests among include distributed system design, streaming technologies, and NoSQL databases.
Debasish Ghosh is principal software engineer at Lightbend. Passionate about technology and open source, he loves functional programming and has been trying to learn math and machine learning. Debasish is an occasional speaker in technology conferences worldwide, including the likes of QCon, Philly ETE, Code Mesh, Scala World, Functional Conf, and GOTO. He’s the author of DSLs In Action and Functional & Reactive Domain Modeling. Debasish is a senior member of ACM. He’s also a father, husband, avid reader, and Seinfeld fanboy who loves spending time with his beautiful family.
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