In this week's real-time analytics news: A paper by Arm, Intel, and NVIDIA describes a specification to provide a common interchangeable format for both artificial intelligence (AI) training and inference.
Claypot AI founder, Chip Huyen, designs machine-learning systems that combine streaming data and batch processing to accommodate multiple data types.
In this week's real-time analytics news: Canonical announced Charmed Kubeflow 1.6, an end-to-end MLOps platform with optimized complex model training capabilities.
Those hesitant to fully automate should consider partial automation strategies that, while keeping a human involved, can still deliver significant benefits.
A dizzying range of tools and services has grown out of the need to speed up and scale data analytics. However, one skill that has often been overlooked is humble SQL, the data scientist's best friend.
In this week's real-time analytics news: Teradata announced a complete cloud analytics and data platform, now with next-generation, cloud-native deployment and expanded analytics capabilities.
A hybrid cloud approach lets businesses essentially pick and choose the various aspects of their analytics and ML workloads that they want to keep on-premises and which they want to run in the cloud.
In this week's real-time analytics news: The Linux Foundation and the World Bank released a free, online quantum computing training course to provide an understanding of how the technology could be used for complex decision making.
The benefits of moving data analytics to the cloud can disappear if businesses don’t have the necessary expertise to manage the cloud’s complexities. Here are some best practices to consider to avoid challenges and maximize ROI.
Data that is ready for machine learning will be observable, supported by real-time infrastructure, and primarily processed with streaming technologies.