Studio

In: Artificial Intelligence

Amazon SageMaker Unified Studio is a single integrated development environment (IDE) that brings together your data tools for analytics and AI. As part of the next generation of Amazon SageMaker, it contains integrated tooling for building data pipelines, sharing datasets, monitoring data governance, running SQL analytics, building artificial intelligence andContinue Reading

In: Artificial Intelligence

AWS supports trusted identity propagation, a feature that allows AWS services to securely propagate a user’s identity across service boundaries. With trusted identity propagation, you have fine-grained access controls based on a physical user’s identity rather than relying on IAM roles. This integration allows for the implementation of access controlContinue Reading

In: Artificial Intelligence

Amazon SageMaker Unified Studio represents the evolution towards unifying the entire data, analytics, and artificial intelligence and machine learning (AI/ML) lifecycle within a single, governed environment. As organizations adopt SageMaker Unified Studio to unify their data, analytics, and AI workflows, they encounter new challenges around scaling, automation, isolation, multi-tenancy, andContinue Reading

In: Artificial Intelligence

In Part 1 of our series, we established the architectural foundation for an enterprise artificial intelligence and machine learning (AI/ML) configuration with Amazon SageMaker Unified Studio projects. We explored the multi-account structure, project organization, multi-tenancy approaches, and repository strategies needed to create a governed AI development environment. In this post,Continue Reading

In: Artificial Intelligence

Organizations today face a critical challenge: managing an ever-increasing volume of tasks and information across multiple systems. Although traditional task management tools help organize work, they often fall short in delivering the intelligence needed for truly efficient operations. Today, we’re excited to announce the integration of Asana AI Studio withContinue Reading

In: Artificial Intelligence

AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted development tools, custom extensions, and debugging tools while accessing compute resourcesContinue Reading

In: Artificial Intelligence

Enterprises adopting advanced AI solutions recognize that robust security and precise access control are essential for protecting valuable data, maintaining compliance, and preserving user trust. As organizations expand AI usage across teams and applications, they require granular permissions to safeguard sensitive information and manage who can access powerful models. AmazonContinue Reading

In: Artificial Intelligence

Although rapid generative AI advancements are revolutionizing organizational natural language processing tasks, developers and data scientists face significant challenges customizing these large models. These hurdles include managing complex workflows, efficiently preparing large datasets for fine-tuning, implementing sophisticated fine-tuning techniques while optimizing computational resources, consistently tracking model performance, and achieving reliable,Continue Reading

In: Artificial Intelligence

Organizations face the challenge to manage data, multiple artificial intelligence and machine learning (AI/ML) tools, and workflows across different environments, impacting productivity and governance. A unified development environment consolidates data processing, model development, and AI application deployment into a single system. This integration streamlines workflows, enhances collaboration, and accelerates AIContinue Reading

In: Artificial Intelligence

Modern generative AI model providers require unprecedented computational scale, with pre-training often involving thousands of accelerators running continuously for days, and sometimes months. Foundation Models (FMs) demand distributed training clusters — coordinated groups of accelerated compute instances, using frameworks like PyTorch — to parallelize workloads across hundreds of accelerators (likeContinue Reading