Remove 2012 Remove Automation Remove Big Data
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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

Batch predictions with model monitoring – The inference pipeline built with Amazon SageMaker Pipelines runs on a scheduled basis to generate predictions along with model monitoring using SageMaker Model Monitor to detect data drift. After it’s trained, the model is registered into the central model registry to be approved by a model approver.

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16 Companies Leading the Way in AI and Data Science

ODSC - Open Data Science

These tools are designed to help companies derive insights from big data. To deliver on their commitment to enhancing human ingenuity, SAS’s ML toolkit focuses on automation and more to provide smarter decision-making. SAS One of the most experienced AI leaders, SAS delivers AI solutions to enhance human ingenuity.

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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Flipboard

You can implement comprehensive tests, governance, security guardrails, and CI/CD automation to produce custom app images. Implement an automated image authoring process As already mentioned, you can use the Studio Image Build CLI to implement an automated CI/CD process of app image creation and deployment with CodeBuild and sm-docker CLI.

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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

Flipboard

Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. For this post, as part of the environment set up we create a new S3 bucket with the name aws-gen-ai-glue-metadata-. The following is an example policy.

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

AWS Machine Learning Blog

To automate the infrastructure deployment, we use the AWS Cloud Development Kit (AWS CDK). For details on adding automation via lifecycle configurations, refer to Customize Amazon SageMaker Studio using Lifecycle Configurations. mlflow/runs/search/", "arn:aws:execute-api: : : / /POST/api/2.0/mlflow/experiments/search",

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Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning Blog

In addition to data engineers and data scientists, there have been inclusions of operational processes to automate & streamline the ML lifecycle. Through automation, that model card is shared with ML Prod account in read-only mode. For more information, refer to Configure the AWS CLI.

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Onboard users to Amazon SageMaker Studio with Active Directory group-specific IAM roles

AWS Machine Learning Blog

We provide the following sample Lambda function that you can copy and modify to meet your needs for automating the creation of the Studio user profile. We demonstrated an end-to-end solution architecture that organizations can adopt to automate and scale their onboarding process to meet their agility, security, and compliance needs.

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