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 deployments. Data scientists create and refine training datasets, develop custom evaluators to assess model...
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 environments can be challenging due to the difficulty in maintaining a central repository to...
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, critical questions emerge. How will you enforce consistent security, prevent model bias, and maintain...
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 increasing as organizations and individuals transition to renewable energy sources. India is on the brink...
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 complex than text where individual words have clear meanings. This creates unique challenges for...
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 trillions of parameters and training clusters expand to thousands of AI accelerators, even minor...
