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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

Under Advanced Project Options , for Definition , select Pipeline script from SCM. For Script Path , enter Jenkinsfile. upload_file("pipelines/train/scripts/raw_preprocess.py","mammography-severity-model/scripts/raw_preprocess.py") s3_client.Bucket(default_bucket).upload_file("pipelines/train/scripts/evaluate_model.py","mammography-severity-model/scripts/evaluate_model.py")

Scripts 119
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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

AWS Machine Learning

We use the custom terminology dictionary to compile frequently used terms within video transcription scripts. If you want to learn more about this use case or have a consultative session with the Mission team to review your specific generative AI use case, feel free to request one through AWS Marketplace. Here’s an example.

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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 He entered the big data space in 2013 and continues to explore that area. Her specialization is machine learning, and she is actively working on designing solutions using various AWS ML, big data, and analytics offerings.

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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 114
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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. Build your pipeline.

Scripts 79
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Optimize for sustainability with Amazon CodeWhisperer

AWS Machine Learning

Amazon CodeWhisperer currently supports Python, Java, JavaScript, TypeScript, C#, Go, Rust, PHP, Ruby, Kotlin, C, C++, Shell scripting, SQL, and Scala. times more energy efficient than the median of surveyed US enterprise data centers and up to 5 times more energy efficient than the average European enterprise data center.

Analytics 117
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Build a custom UI for Amazon Q Business

AWS Machine Learning

You can also find the script on the GitHub repo. He helps organizations in achieving specific business outcomes by using data and AI, and accelerating their AWS Cloud adoption journey. He has extensive experience across big data, data science, and IoT, across consulting and industrials.

APIs 126