"The cloud provider is introducing serverless model customization in SageMaker, which allows developers to start building a model without needing to think about compute resources or infrastructure, according to Ankur Mehrotra, general manager of AI platforms at AWS, in an interview with TechCrunch. To access these serverless model-building capabilities, developers can either follow a self-guided point-and-click path or an agent-led experience where they can prompt SageMaker using natural language. The agent-led feature is launching in preview."
""If you're a healthcare customer and you wanted a model to be able to understand certain medical terminology better, you can simply point SageMaker AI, if you have labeled data, then select the technique and then off SageMaker goes, and [it] fine tunes the model," Mehrotra said. This capability is available for customizing Amazon's own Nova models and certain open source models (those with publicly available model weights), including DeepSeek and Meta's Llama."
AWS added new capabilities in Amazon Bedrock and Amazon SageMaker AI to simplify building and fine-tuning custom large language models for enterprise customers. Serverless model customization in SageMaker lets developers start building models without managing compute resources or infrastructure. Developers can use a self-guided point-and-click workflow or an agent-led natural language interface (preview). Serverless customization supports Amazon's Nova models and open-source models with public weights, including DeepSeek and Meta's Llama. Bedrock now offers Reinforcement Fine-Tuning that runs customization automatically using a chosen reward function or a preset workflow. AWS also launched Nova Forge, offering custom Nova models for $100,000 per year.
Read at TechCrunch
Unable to calculate read time
Collection
[
|
...
]