This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
Businesses are developing new methods to identify fraud with in-memory machine learning models for predictive analytics. By moving these models to in-memory technologies, data scientists are able to increase both the speed and accuracy of model development and performance.
In this paper, we will introduce machine learning and explain its role in fraud detection. We will cover the challenges that data science teams face in deploying their models. Then we will explore the business implications of succumbing to the status quo, along with the technology implications to deliver in-memory machine learning for fraud detection in the real world.