Want AI? It’s Going to Require a Healthy Dose of Real-Time Data Streaming
AI and machine learning are going to shake up how companies operate, and streaming data amplifies the leverage that can be gained from these powerful tools.
Another Avenue for Digital Twins: Behavioral Modeling for Banks
Financial services organizations are using digital twins and machine learning algorithms to identify behavioral characteristics including individual preferences, risk tolerance, and financial goals.
Human-Centered Design to Amplify Care
While AI and machine learning can help, front-line employees are ultimately the ones who can deliver that empathy and care customers desire today.
Real-time Analytics News for the Week Ending September 16
In this week's real-time analytics news: MLCommons released its latest machine learning benchmark testing results.
Using Photonic Neurons to Improve Neural Networks
Photonic neural networks represent a promising technology that could revolutionize the way businesses approach machine learning and artificial intelligence systems.
Futureproofing Your Architecture for Payments Fraud Detection
Join us for this Hazelcast webinar which delves into the challenge of combating new fraudulent tactics and explore how machine learning (ML) has become a key strategy in minimizing prediction errors.
Using Smaller ML Models To Train Large Language Models
A new research project by MIT has developed a way to train large language models on smaller machine learning algorithms.
Enabling Financial Services Real-time Time-Series Analytics
To keep up with the speed, scale, and complexity of financial data requires active analytics that unifies streaming, historical, and location analytics with machine learning on a single platform.
Real-time Analytics News for Week Ending March 4
In this week's real-time analytics news: TigerGraph’s new version of TigerGraph Cloud includes new security, advanced AI, and machine learning capabilities.
MIT Researchers Create Explanation Taxonomy to ML Models
For non-technical staff, the lack of understanding and obfuscation by some developers is one of the lead causes of under-utilization of machine learning services in the real world.