Remove 2012 Remove Best practices Remove Big data
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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

On the JSON tab, modify the policy as follows: { "Version": "2012-10-17", "Statement": [ { "Sid": "eniperms", "Effect": "Allow", "Action": [ "ec2:CreateNetworkInterface", "ec2:DescribeNetworkInterfaces", "ec2:DeleteNetworkInterface", "ec2:*VpcEndpoint*" ], "Resource": "*" } ] } Choose Next. You’re redirected to the IAM console. With an M.Sc.

APIs 136
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Set up cross-account Amazon S3 access for Amazon SageMaker notebooks in VPC-only mode using Amazon S3 Access Points

AWS Machine Learning

Kesaraju Sai Sandeep is a Cloud Engineer specializing in Big Data Services at AWS. Don’t change or edit any Block Public Access settings for this access point (all public access should be blocked). You can define the actions as per your requirements or use case.

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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

Policy 3 – Attach AWSLambda_FullAccess , which is an AWS managed policy that grants full access to Lambda, Lambda console features, and other related AWS services.

Scripts 120
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Define customized permissions in minutes with Amazon SageMaker Role Manager

AWS Machine Learning

To allow your data scientists to assume their given persona via the console, they require a console role to get to the Studio environment. He is passionate about building secure and scalable AI/ML and big data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes.

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Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

As you scale your models, projects, and teams, as a best practice we recommend that you adopt a multi-account strategy that provides project and team isolation for ML model development and deployment. They provide a fact sheet of the model that is important for model governance. For more information, refer to Configure the AWS CLI.

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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

He regularly speaks at AI and machine learning conferences across the world including O’Reilly AI, Open Data Science Conference, and Big Data Spain. mlflow/runs/search/", "arn:aws:execute-api: : : / /POST/api/2.0/mlflow/experiments/search", mlflow/runs/search/", "arn:aws:execute-api: : : / /POST/api/2.0/mlflow/experiments/search",

APIs 77
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Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication

AWS Machine Learning

Above solution created the following Auth Policy for the Lambda that generated Pre-Signed URL for accessing SageMaker Studio. { "Version": "2012-10-17", "Statement": [ { "Condition": { "IpAddress": { "aws:VpcSourceIp": "10.16.0.0/16" Pre-Signed URL Lambda Auth Policy. Ram Vittal is a machine learning solutions architect at AWS.

APIs 87