Category: Amazon SageMaker

Q Developer empowers non-ML experts to build ML models using natural language, enabling organizations to innovate faster with reduced time to market.

Unlock efficient large model training with SageMaker HyperPod flexible training plans – find optimal compute resources and complete training within timelines and budgets.

Enable priority-based resource allocation, fair-share utilization, and automated task preemption for optimal compute utilization across teams.

Simplify data replication and ingestion from applications such as Salesforce, SAP, ServiceNow, and Zendesk, to Amazon SageMaker Lakehouse and Amazon Redshift.

Unifying data silos, Amazon SageMaker Lakehouse seamlessly integrates S3 data lakes and Redshift warehouses, enabling unified analytics and AI/ML on a single data copy through open Apache Iceberg APIs and fine-grained access controls.

Effortlessly analyze operational data in Amazon SageMaker Lakehouse, freeing developers from building custom pipelines and enabling seamless insights extraction.

Manage data and AI assets through a unified catalog, granular access controls, and a consistent policy enforcement. Establish trust via automation – boost productivity and innovation for data teams.

Unify data engineering, analytics, and generative AI in a streamlined studio with enhanced capabilities of Amazon SageMaker.