Building natural voice conversations with AI agents requires complex infrastructure and lots of code from engineering teams. Text-based agent interactions follow a turn-based pattern: a

Building custom foundation models requires coordinating multiple assets across the development lifecycle such as data assets, compute infrastructure, model architecture and frameworks, lineage, and production

A user can conduct machine learning (ML) data experiments in data environments, such as Snowflake, using the Snowpark library. However, tracking these experiments across diverse

Picture this: Your enterprise has just deployed its first generative AI application. The initial results are promising, but as you plan to scale across departments,

This post is co-written with Vikram Bansal from Tata Power, and Gaurav Kankaria, Omkar Dhavalikar from Oneture. The global adoption of solar energy is rapidly

Media and entertainment, advertising, education, and enterprise training content combines visual, audio, and motion elements to tell stories and convey information, making it far more

Foundation model training has reached an inflection point where traditional checkpoint-based recovery methods are becoming a bottleneck to efficiency and cost-effectiveness. As models grow to

Modern AI infrastructure serves multiple concurrent workloads on the same cluster, from foundation model (FM) pre-training and fine-tuning to production inference and evaluation. In this

Large Language Model (LLM) agents have revolutionized how we approach complex, multi-step tasks by combining the reasoning capabilities of foundation models with specialized tools and

Enterprise organizations are rapidly moving beyond generative AI experiments to production deployments and complex agentic AI solutions, facing new challenges in scaling, security, governance, and