Machine Learning | News, how-tos, features, reviews, and videos
This issue showcases practical AI deployments, implementation strategies, and real-world considerations such as for data management and AI governance that IT and business leaders alike should know before plunging into AI.
Do you need to distribute a heavy Python workload across multiple CPUs or a compute cluster? These seven frameworks are up to the task.
Python developers are uniquely positioned to succeed in the AI era, with a little help from upskilling.
Recent advances in AI, such as foundation models, make it possible for smaller companies to build custom models to make predictions, reduce uncertainty, and gain business advantage.
Spring’s ambitious new enterprise AI framework provides a familiar entry point for Java developers getting started with generative AI. It also leverages Java’s strengths as a time-tested enterprise integration platform. Let’s jump i
Torchao is a PyTorch native library that makes machine learning models faster and smaller for training or inference by leveraging low-bit dtypes, sparsity, and quantization.
AI agents created via Agentforce differ from previous Salesforce-based agents in their use of Atlas, a reasoning engine designed to help these bots think like human beings.
Integration with PyTorch and ExecuTorch brings Arm computing performance to the machine learning stack for developers.
The tools provide advanced data intelligence, data quality, and data modeling capabilities aimed at helping customers ensure the AI readiness of their data, the company said.
Rival software providers, Zendesk and ServiceNow, have already introduced similar capabilities.