Amazon SageMaker introduces a new notebook experience that provides data and AI teams a high-performance, serverless programming environment for analytics and machine learning (ML) jobs. This helps customers quickly get started working with data without pre-provisioning data processing infrastructure. The new notebook gives data engineers, analysts, and data scientists one place to perform SQL queries, execute Python code, process large-scale data jobs, run ML workloads and create visualizations. A built-in AI agent accelerates development by generating code and SQL statements from natural language prompts while it guides users through their tasks. The notebook is backed by Amazon Athena for Apache Spark to deliver high-performance results, scaling from interactive SQL queries to petabyte-scale data processing. It’s available in the new one-click onboarding experience for Amazon SageMaker Unified Studio.
Data engineers, analysts, and data scientists can flexibly combine SQL, Python, and natural language within a single interactive workspace. This removes the need to switch between different tools based on your workload. For example, you can start with SQL queries to explore your data, use Python for advanced analytics or to build ML models, or use natural language prompts to generate code automatically using the built-in AI agent. To get started, sign in to the console, find SageMaker, open SageMaker Unified Studio, and go to “Notebooks” in the navigation.
You can use the SageMaker notebook feature in the following Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Singapore), and Asia Pacific (Sydney).
To learn more, read the AWS News Blog or see SageMaker documentation.
Categories: general:products/amazon-sagemaker,marketing:marchitecture/analytics
Source: Amazon Web Services
