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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

If you have a different format, you can potentially use Llama convert scripts or Mistral convert scripts to convert your model to a supported format. models demonstrate state-of-the-art performance on a wide range of industry benchmarks and introduce features to help you build a new generation of AI experiences. from sagemaker.s3

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How Accenture is using Amazon CodeWhisperer to improve developer productivity

AWS Machine Learning

They were able to create a preprocessing data class just by typing “class to create preprocessing script for ML data.” Writing the preprocessing script took only a couple of minutes, and CodeWhisperer was able to generate entire code blocks. Writing boilerplate code Developers were able to use CodeWhisperer to complete prerequisites.

Scripts 91
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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

With the increasing use of artificial intelligence (AI) and machine learning (ML) for a vast majority of industries (ranging from healthcare to insurance, from manufacturing to marketing), the primary focus shifts to efficiency when building and training models at scale. The steps are as follows: Open AWS Cloud9 on the console.

Scripts 81
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Continuous integration and continuous delivery (CI/CD) pipeline – Using the customer’s GitHub repository enabled code versioning and automated scripts to launch pipeline deployment whenever new versions of the code are committed. About the Authors Stephen Randolph is a Senior Partner Solutions Architect at Amazon Web Services (AWS).

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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

Create a healthcare folder in the bucket you named via your AWS CDK script. He has over 8 years of industry experience from startups to large-scale enterprises, from IoT Research Engineer, Data Scientist, to Data & AI Architect. Then upload flow-healthcarediabetesunclean.csv to the folder and let the automation happen!

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Bring SageMaker Autopilot into your MLOps processes using a custom SageMaker Project

AWS Machine Learning

You can also add your own Python scripts and transformations to customize workflows. You can access the testing script from the local path of the code repository that we cloned earlier. We use Data Wrangler to perform preprocessing on the dataset before submitting the data to Autopilot. Choose the file browser icon view the path.