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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 122
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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

It enables different business units within an organization to create, share, and govern their own data assets, promoting self-service analytics and reducing the time required to convert data experiments into production-ready applications. This approach was not only time-consuming but also prone to errors and difficult to scale.

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11 Contact Center Technologies to Boost Customer Satisfaction

TechSee

Recognizing that continuously adding quality agents simply does not add up financially, more and more companies are turning to technology in order to scale quality support. Founded in 2015, TechSee is a technology and technical support company that specializes in visual technology and augmented reality. ” 2. Coveo.

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Fine-tune and deploy a summarizer model using the Hugging Face Amazon SageMaker containers bringing your own script

AWS Machine Learning

Build your training script for the Hugging Face SageMaker estimator. script to use with Script Mode and pass hyperparameters for training. Thanks to our custom inference script hosted in a SageMaker endpoint, we can generate several summaries for this review with different text generation parameters. If we use an ml.g4dn.16xlarge

Scripts 101
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

By establishing robust oversight, organizations can build trust, meet regulatory requirements, and help ensure ethical use of AI technologies. He is passionate about building secure and scalable AI/ML and big data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes.

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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket. 1 with the following additions: The Snowflake Connector for Python to download the data from the Snowflake table to the training instance.

Scripts 131
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How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

AWS Machine Learning

Batch transform The batch transform pipeline consists of the following steps: The pipeline implements a data preparation step that retrieves data from a PrestoDB instance (using a data preprocessing script ) and stores the batch data in Amazon Simple Storage Service (Amazon S3).

Scripts 120