III UNIT
Cloud-based Machine Learning and
Deep Learning
,AWS(SAGEMAKER)
• Bringing together widely adopted AWS machine
learning (ML) and analytics capabilities, the next generation of
Amazon SageMaker delivers an integrated experience for
analytics and AI with unified access to all your data.
• Collaborate and build faster from a unified studio using
familiar AWS tools for model development in SageMaker
AI (including HyperPod, JumpStart, and MLOPs).
,Continue…
• Access all your data whether it’s stored in data lakes, data
warehouses, or third-party or federated data sources, with
governance built in to meet enterprise security needs.
, HyperPod
• It removes the undifferentiated heavy lifting involved in building generative AI
models.
• It helps quickly scale model development tasks such as training, fine-tuning, or
inference across a cluster of hundreds or thousands of AI accelerators.
• SageMaker HyperPod enables centralized governance across all your model
development tasks, giving you full visibility and control over how different tasks
are prioritized and how compute resources are allocated to each task, helping
you maximize GPU and AWS Trainium (purpose-built for high-performance,
cost-efficient AI at scale) utilization of your cluster and accelerate innovation.
Cloud-based Machine Learning and
Deep Learning
,AWS(SAGEMAKER)
• Bringing together widely adopted AWS machine
learning (ML) and analytics capabilities, the next generation of
Amazon SageMaker delivers an integrated experience for
analytics and AI with unified access to all your data.
• Collaborate and build faster from a unified studio using
familiar AWS tools for model development in SageMaker
AI (including HyperPod, JumpStart, and MLOPs).
,Continue…
• Access all your data whether it’s stored in data lakes, data
warehouses, or third-party or federated data sources, with
governance built in to meet enterprise security needs.
, HyperPod
• It removes the undifferentiated heavy lifting involved in building generative AI
models.
• It helps quickly scale model development tasks such as training, fine-tuning, or
inference across a cluster of hundreds or thousands of AI accelerators.
• SageMaker HyperPod enables centralized governance across all your model
development tasks, giving you full visibility and control over how different tasks
are prioritized and how compute resources are allocated to each task, helping
you maximize GPU and AWS Trainium (purpose-built for high-performance,
cost-efficient AI at scale) utilization of your cluster and accelerate innovation.
