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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 123
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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. However, as data volumes and complexity continue to grow, effective data governance becomes a critical challenge.

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning

Large language models (LLMs) have captured the imagination and attention of developers, scientists, technologists, entrepreneurs, and executives across several industries. We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw.

Scripts 125
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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

This file includes the necessary AWS and PrestoDB credentials to connect to the PrestoDB instance, information on the training hyperparameters and SQL queries that are run at training, and inference steps to read data from PrestoDB. For more information on processing jobs, see Process data.

Scripts 121
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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 122
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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table.

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Contact Center Trends 2024: Our Predictions

Fonolo

We live in an era of big data, AI, and automation, and the trends that matter in CX this year begin with the abilities – and pain points – ushered in by this technology. For example, big data makes things like hyper-personalized customer service possible, but it also puts enormous stress on data security.