Organizations face a challenging trade-off when adapting AI models to their specific business needs: settle for generic models that produce average results, or tackle the complexity and expense of advanced model customization. Traditional approaches force a choice between poor performance with smaller models or the high costs of deploying larger model variants and managing complex…

Today, I’m happy to announce new serverless customization in Amazon SageMaker AI for popular AI models, such as Amazon Nova, DeepSeek, GPT-OSS, Llama, and Qwen. The new customization capability provides an easy-to-use interface for the latest fine-tuning techniques like reinforcement learning, so you can accelerate the AI model customization process from months to days. With…

Today, we’re announcing two new AI model training features within Amazon SageMaker HyperPod: checkpointless training, an approach that mitigates the need for traditional checkpoint-based recovery by enabling peer-to-peer state recovery, and elastic training, enabling AI workloads to automatically scale based on resource availability. Checkpointless training – Checkpointless training eliminates disruptive checkpoint-restart cycles, maintaining forward training…