SageMaker simplifies building, training, and deploying ML models, and QuickSight enables you to create interactive dashboards and visualizations without specialized skills or complex data engineering. As fully managed services, SageMaker and QuickSight handle infrastructure scaling, maintenance, and automation. This frees you to focus on optimizing ML models and gaining insights from data visualizations, rather than managing underlying complexities.

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This Guidance uses AWS Identity and Access Management (IAM)to support least privilege access and role separation, reducing the risk of unauthorized access or actions. Additionally, Amazon S3 bucket policies support bucket access control, encryption at rest, version and object locking, and access logging. These policies enable you to make sure that only authorized roles, users, or services can read or write data to the buckets. You can also use the log data for security analysis, auditing, and compliance purposes. Finally, QuickSight supports row-level security, enabling you to control access to specific data rows based on user or group membership, preventing unauthorized data exposure.

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SageMaker provides automatic scaling, fault tolerance, and model validation features to support reliable and accurate ML model deployment. It automatically distributes training data and models across multiple Availability Zones (AZs) and enables automated retraining to maintain model accuracy over time. Additionally, Amazon S3 provides reliable and durable data storage capabilities through its built-in high-availability features. For example, it replicates data across multiple AZs and redundant storage facilities, minimizing data loss and supporting consistent data access.

Read the Reliability whitepaper

SageMaker offers optimized hardware instances, distributed training, and automatic model tuning to improve the performance and efficiency of your ML workloads. For example, you can choose optimal instance types, and your workload scales based on demand, resulting in efficient utilization of compute and memory resources. Additionally, Amazon S3 provides highly scalable and low-latency data storage, enabling efficient data ingestion and retrieval for training and deploying ML models.

Read the Performance Efficiency whitepaper

Amazon S3 offers lifecycle policies and storage classes that optimize storage and costs. For example, Amazon S3 Intelligent-Tiering automatically moves objects to the most cost-effective storage tier based on access patterns. Additionally, SageMaker offers per-second billing for training instances, so you only pay for the resources you use, and it scales automatically to optimize costs. And as a managed service, it removes the overhead of provisioning and managing your own ML infrastructure.

Read the Cost Optimization whitepaper

Amazon Bedrock provides a sustainable foundation for running data-intensive workloads. By scaling automatically, it optimizes resource usage and avoids the environmental impact of overprovisioned or inefficient infrastructure. Additionally, Amazon S3 provides highly durable and secure object storage that protects data integrity, minimizing the need for redundant backups. It also supports efficient data storage and management practices, such as Amazon S3 Lifecycle policies like Amazon S3 Intelligent-Tiering and object expiration. Using these management capabilities, you can minimize the storage resources required for your workloads and reduce the energy consumption associated with storing and processing data.

Read the Sustainability whitepaper