What Is Machine Learning Inference?
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.
Key Considerations for Optimal Machine Learning Deployments
Machine learning (ML) is being used almost everywhere, but the ubiquity has not been equated with simplicity. If you solely consider the operationalization aspect of ML, you know that deploying your models into production, especially in real-time environments, can be inefficient and time-consuming. Common approaches may not perform and scale to the levels needed. These challenges are especially true for businesses that have not properly planned out their data science initiatives.
Operationalizing Machine Learning with Java Microservices and Stream Processing
Are you ready to take your algorithms to the next steps and get them working on real-world data in real-time? We will walk through an architecture for taking a machine learning model into deployment for inference within an open source platform designed for extremely high throughput and low latency.
Fraud Detection with In-Memory Machine Learning
This paper discusses the role of machine learning in fraud detection, and why improved fraud detection models are required today.