Make your Data Science Actionable with Stream Processing

Make your Data Science Actionable with Stream Processing

Are you ready to take your machine learning algorithms and make them operational within your business in real time? 
In this talk 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.
•    How the technologies like IMDG and Streaming Engine combine to provide performance at scale.


Live demonstration of a working example of a machine learning model used on streaming data within Hazelcast Jet. The talk is aimed at Architects and Developers alike who are keen to explore  ways to inject intelligence from Machine learning to existing or new platforms in real-time

Schedule:

Room:

Edward 1-4

Tracks:

Architecture
Speakers
Profile picture for user mohari
Riaz
Mohammed
Senior Solutions Architect
at
Hazelcast
Riaz has 18+ years’ experience in the IT, designing and developing solutions mainly in the Investment Banking sector. Having worked for the like of Deutsche Bank, JP Morgan Cazenove, Nomura, Software AG and currently at Hazelcast he is proficient in High Level design and Low level hands-on programming. Working with Enterprise Software companies to provide solutions in various industry sectors incl Investment, Commercial & Retail Banking , logistics, Govt etc he has developed a broader knowledge of the needs of various industries and how technology is evolving to meet those needs.

Slides & Recordings