AI

Scale visual production using Stability AI Image Services in Amazon Bedrock

This post was written with Alex Gnibus of Stability AI. Stability AI Image Services are now available in Amazon Bedrock, offering ready-to-use media editing capabilities delivered through the Amazon Bedrock API. These image editing tools expand on the capabilities of Stability AI’s Stable Diffusion 3.5 models (SD3.5) and Stable Image Core and Ultra models, which

Scale visual production using Stability AI Image Services in Amazon Bedrock Read More »

Prompting for precision with Stability AI Image Services in Amazon Bedrock

Amazon Bedrock now offers Stability AI Image Services: 9 tools that improve how businesses create and modify images. The technology extends Stable Diffusion and Stable Image models to give you precise control over image creation and editing. Clear prompts are critical—they provide art direction to the AI system. Strong prompts control specific elements like tone,

Prompting for precision with Stability AI Image Services in Amazon Bedrock Read More »

Monitor Amazon Bedrock batch inference using Amazon CloudWatch metrics

As organizations scale their use of generative AI, many workloads require cost-efficient, bulk processing rather than real-time responses. Amazon Bedrock batch inference addresses this need by enabling large datasets to be processed in bulk with predictable performance—at 50% lower cost than on-demand inference. This makes it ideal for tasks such as historical data analysis, large-scale

Monitor Amazon Bedrock batch inference using Amazon CloudWatch metrics Read More »

Use AWS Deep Learning Containers with Amazon SageMaker AI managed MLflow

Organizations building custom machine learning (ML) models often have specialized requirements that standard platforms can’t accommodate. For example, healthcare companies need specific environments to protect patient data while meeting HIPAA compliance, financial institutions require specific hardware configurations to optimize proprietary trading algorithms, and research teams need flexibility to experiment with cutting-edge techniques using custom frameworks.

Use AWS Deep Learning Containers with Amazon SageMaker AI managed MLflow Read More »