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Kaggle’s AI Agents Intensive with Google brought learners together in a no-cost course to build and deploy the next frontier of AI. ​ 

Learn how Gemini 3 powers Google Search with Generative UI, Nano Banana, and interactive graphics. ​ 

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 quality and safety, and iterate through fine-tuning configurations to optimize performance. As these workflows scale across …

Tracking and managing assets used in AI development with Amazon SageMaker AI  Read More »

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 monitor experiment metadata, parameters, hyperparameters, models, results, and other pertinent information. In this post, we demonstrate …

Track machine learning experiments with MLflow on Amazon SageMaker using Snowflake integration Read More »

Gemini 3 Flash offers frontier intelligence built for speed at a fraction of the cost. ​ 

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 control as AI applications multiply? It turns out you’re not alone. A McKinsey survey spanning 750+ …

Governance by design: The essential guide for successful AI scaling Read More »

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 of a solar energy revolution, with a national goal to empower 10 million households with …

How Tata Power CoE built a scalable AI-powered solar panel inspection solution with Amazon SageMaker AI and Amazon Bedrock Read More »

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 AI systems that need to understand video content. Video content is multidimensional, combining visual elements (scenes, …

Unlocking video understanding with TwelveLabs Marengo on Amazon Bedrock Read 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 trillions of parameters and training clusters expand to thousands of AI accelerators, even minor disruptions can result in significant costs and delays. In this post, we introduce checkpointless training on …

Checkpointless training on Amazon SageMaker HyperPod: Production-scale training with faster fault recovery Read More »

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 shared environment, the demands for AI accelerators fluctuates continuously as inference workloads scale with traffic patterns, and experiments complete and release resources. Despite this dynamic availability of AI accelerators, traditional …

Adaptive infrastructure for foundation model training with elastic training on SageMaker HyperPod Read More »