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Enabling generative AI self-service using Amazon Lex, Amazon Bedrock, and ServiceNow

AWS Machine Learning Blog

Application Auto Scaling is enabled on AWS Lambda to automatically scale Lambda according to user interactions. Prerequisites The following prerequisites need to be completed before building the solution. Download the CloudFormation template and upload it in the Specify template Choose Next. Download a sample article.

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Amazon SageMaker Domain in VPC only mode to support SageMaker Studio with auto shutdown Lifecycle Configuration and SageMaker Canvas with Terraform

AWS Machine Learning Blog

IaC ensures that customer infrastructure and services are consistent, scalable, and reproducible while following best practices in the area of development operations (DevOps). Later, the auto-shutdown script will run the s3 cp command to download the extension file from the S3 bucket on Jupyter Server start-ups.

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How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps

AWS Machine Learning Blog

When training is complete (through the Lambda step), the deployed model is updated to the SageMaker endpoint. When the preprocessing batch was complete, the training/test data needed for training was partitioned based on runtime and stored in Amazon S3. We load tested it with Locust using five g4dn.2xlarge

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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. Download and save the publicly available UCI Mammography Mass dataset to the S3 bucket you created earlier in the dev account.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. The entire model can be downloaded to your source code’s runtime with a single line of code.

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Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS

AWS Machine Learning Blog

It manages the availability and scalability of the Kubernetes control plane, and it provides compute node auto scaling and lifecycle management support to help you run highly available container applications. Training Now that our data preparation is complete, we’re ready to train our model with the created dataset.

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Unearth insights from audio transcripts generated by Amazon Transcribe using Amazon Bedrock

AWS Machine Learning Blog

For the purpose of this notebook, we downloaded the MP4 file for the recording and stored it in an Amazon Simple Storage Service (Amazon S3) bucket. time.sleep(10) The transcription job will take a few minutes to complete. Transcribe audio with Amazon Transcribe In this case, we use an AWS re:Invent 2023 technical talk as a sample.