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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

A Business or Enterprise Google Workspace account with access to Google Chat. 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).

APIs 121
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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

The data mesh architecture aims to increase the return on investments in data teams, processes, and technology, ultimately driving business value through innovative analytics and ML projects across the enterprise. This approach was not only time-consuming but also prone to errors and difficult to scale.

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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 128
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

from time import gmtime, strftime experiment_suffix = strftime('%d-%H-%M-%S', gmtime()) experiment_name = f"credit-risk-model-experiment-{experiment_suffix}" The processing script creates a new MLflow active experiment by calling the mlflow.set_experiment() method with the experiment name above. fit_transform(y).

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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 119
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Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning

You can then iterate on preprocessing, training, and evaluation scripts, as well as configuration choices. framework/createmodel/ – This directory contains a Python script that creates a SageMaker model object based on model artifacts from a SageMaker Pipelines training step. script is used by pipeline_service.py The model_unit.py

Scripts 116
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21 Business Analysts & Call Center Leaders Reveal the Optimal Role of the Business Analyst in Call Center Operations

Callminer

Learn more about how speech analytics can benefit your call center operation by downloading our white paper, 10 Ways Speech Analytics Empowers the Entire Enterprise. They can assess how current scripts are performing and change them as needed. There would be no operations without customers. Jesse Silkoff.