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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

Run the script init-script.bash : chmod u+x init-script.bash./init-script.bash init-script.bash This script prompts you for the following: The Amazon Bedrock knowledge base ID to associate with your Google Chat app (refer to the prerequisites section). The script deploys the AWS CDK project in your account.

APIs 123
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Deploy a Slack gateway for Amazon Bedrock

AWS Machine Learning

The Slack application sends the event to Amazon API Gateway , which is used in the event subscription. API Gateway forwards the event to an AWS Lambda function. Toggle Enable Events on. The event subscription should get automatically verified. Choose Save Changes. The integration is now complete.

APIs 127
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Host the Spark UI on Amazon SageMaker Studio

AWS Machine Learning

Amazon SageMaker offers several ways to run distributed data processing jobs with Apache Spark, a popular distributed computing framework for big data processing. install-scripts chmod +x install-history-server.sh./install-history-server.sh cd amazon-sagemaker-spark-ui-0.1.0/install-scripts install-history-server.sh

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MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

AWS Machine Learning

Batch inference The SageMaker batch inference pipeline runs on a schedule (via EventBridge) or based on an S3 event trigger as well. The batch inference pipeline includes steps for checking data quality against a baseline created by the training pipeline, as well as model quality (model performance) if ground truth labels are available.

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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 6 – Attach CloudWatchEventsFullAccess , which is an AWS managed policy that grants full access to CloudWatch Events. Under Advanced Project Options , for Definition , select Pipeline script from SCM. For Script Path , enter Jenkinsfile. For SCM , choose Git. For Repository URL , enter the forked GitHub repository URL.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

Collaboration across teams – Shared features allow disparate teams like fraud, marketing, and sales to collaborate on building ML models using the same reliable data instead of creating siloed features. Audit trail for compliance – Administrators can monitor feature usage by all accounts centrally using CloudTrail event logs.

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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning

To create these packages, run the following script found in the root directory: /build_mlops_pkg.sh When training is complete, choose the System tab to see the training time durations on your edge servers and aggregation events. He entered the big data space in 2013 and continues to explore that area.