John DesJardins, CTO N. America & Community Advocate at Hazelcast
Make Your Data Science Actionable: Real-Time Machine Learning Inference With Stream Processing
Are you ready to take your machine learning algorithms and make them operational within your business in real time?
Join us for an on-demand webinar titled, “Make Your Data Science Actionable, Real-Time Machine Learning Inference With Stream Processing”.
In this webinar we will walk through an architecture for taking a machine learning model from training into deployment for inference within an open source platform for real-time stream processing.
We will also cover:
- The typical workflow from data exploration to model training through to real-time model inference (aka scoring) on streaming data.
- Important considerations to ensure maximum flexibility for deployments that need the flexibility to run in Cloud-Native, Microservices and Edge/Fog architectures.
- Live demonstration of a working example of a machine learning model used on streaming data within Hazelcast Jet.
Riaz Mohammed, Senior Solutions Architect at Hazelcast
Riaz has 15+ years’ experience in developing high throughput & low latency trading systems, surveillance & monitoring systems mainly in the Finance sector. He has been part of core development teams delivering global sales and trading platforms in Tier 1 Investment Banks.