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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

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts 104
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Build a custom UI for Amazon Q Business

AWS Machine Learning

Prerequisites For this walkthrough, you should have the following prerequisites: An AWS account set up. An IAM role in the account with sufficient permissions to create the necessary resources. If you have administrator access to the account, no additional action is required. You can also find the script on the GitHub repo.

APIs 99
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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

default_bucket() upload _path = f"training data/fhe train.csv" boto3.Session().resource("s3").Bucket To see more information about natively supported frameworks and script mode, refer to Use Machine Learning Frameworks, Python, and R with Amazon SageMaker. resource("s3").Bucket Bucket (bucket).Object Object (upload path).upload

Scripts 100
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Reduce cost and development time with Amazon SageMaker Pipelines local mode

AWS Machine Learning

Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. One of the main drivers for new innovations and applications in ML is the availability and amount of data along with cheaper compute options. Register model step (model package). Fail step (run failed).

Scripts 76
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21 Business Analysts & Call Center Leaders Reveal the Optimal Role of the Business Analyst in Call Center Operations

Callminer

For instance, a call center business analyst might recommend implementing an interaction analytics solution for a collections and accounts receivables management (ARM) firm to ensure that call center agents meet compliance requirements for debt collection. Carol Tompkins is the Business Development Consultant at AccountsPortal.

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Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

One important aspect of this foundation is to organize their AWS environment following a multi-account strategy. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs.

APIs 74
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Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition

AWS Machine Learning

Prerequisites For this walkthrough, you should have the following prerequisites: An AWS account. Two components need to be configured in our inference script : model loading and model serving. On top, he likes thinking big with customers to innovate and invent new ideas for them. iterdir(): if p_file.suffix == ".pth":

APIs 66