Applied Machine Learning in Real-Time with Distributed, Scalable, In-Memory Technology
Machine learning (ML) brings exciting new opportunities, but applying the technology in production workloads has been cumbersome, time consuming, and error prone. In parallel, data generation patterns have evolved, generating streams of discrete events that require high-speed processing at extremely low response latencies. Enabling these capabilities requires a scalable application of high-performance stream processing, distributed application of ML technology, and dynamically scalable hardware resources.
Machine Learning Inference at Scale with Python and Stream Processing
In this talk we will show you how to write a low-latency, high throughput distributed stream processing pipeline (in Java), using a model developed in Python.
Tech Talk: Machine Learning at Scale Using Distributed Stream Processing
In this talk, Marko will show one approach which allows you to write a low-latency, auto-parallelized and distributed stream processing pipeline in Java that seamlessly integrates with a data scientist’s work taken in almost unchanged form from their Python development environment. The talk includes a live demo using the command line and going through some Python and Java code snippets.