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Fine-tune multimodal models for vision and text use cases on Amazon SageMaker JumpStart

AWS Machine Learning Blog

In the metadata.jsonl file, each example is a dictionary that contains three keys named file_name , prompt , and completion. prompt defines the text input prompt and completion defines the text completion corresponding to the input prompt. jpg", "prompt": "what is the contact person name mentioned in letter?", "completion": "P.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Create a KMS key in the dev account and give access to the prod account Complete the following steps to create a KMS key in the dev account: On the AWS KMS console, choose Customer managed keys in the navigation pane. Choose Create key. For Key type , select Symmetric.

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AI-powered code suggestions and security scans in Amazon SageMaker notebooks using Amazon CodeWhisperer and Amazon CodeGuru

AWS Machine Learning Blog

To get started, complete the following steps: On the File menu, choose New and Terminal. Use CodeWhisperer in Studio After we complete the installation steps, we can use CodeWhisperer by opening a new notebook or Python file. Let’s test it out in a Python file. On the File menu, choose New and Python File.

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16 Companies Leading the Way in AI and Data Science

ODSC - Open Data Science

Going from Data to Insights LexisNexis At HPCC Systems® from LexisNexis® Risk Solutions you’ll find “a consistent data-centric programming language, two processing platforms, and a single, complete end-to-end architecture for efficient processing.” These tools are designed to help companies derive insights from big data.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

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Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml Launch SageMaker Studio Complete the following steps to launch your SageMaker Studio domain: On the SageMaker console, choose Domains in the navigation pane.

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Boost productivity on Amazon SageMaker Studio: Introducing JupyterLab Spaces and generative AI tools

AWS Machine Learning Blog

Complete the following steps to edit an existing space: On the space details page, choose Stop space. EFS mounts provide a solid alternative for sharing Python environments like conda or virtualenv across multiple workspaces. To start using Amazon CodeWhisperer, make sure that the Resume Auto-Suggestions feature is activated.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code. or later image versions.