Big data How To Scripts 
AWS Machine Learning
OCTOBER 31, 2024
The solution integrates large language models (LLMs) with your organization’s data and provides an intelligent chat assistant that understands conversation context and provides relevant, interactive responses directly within the Google Chat interface. In the following sections, we explain how to deploy this architecture.
AWS Machine Learning
NOVEMBER 22, 2024
This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product. To view this series from the beginning, start with Part 1.
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AWS Machine Learning
NOVEMBER 14, 2024
In this post, we will show you how to use this new cross-account model sharing feature to build your own centralized model governance capability, which is often needed for centralized model approval, deployment, auditing, and monitoring workflows. The following code snippet shows how to create a tracking server.
AWS Machine Learning
JULY 29, 2022
In this post, we walk you through an example of how to build and deploy a custom Hugging Face text summarizer on SageMaker. Build your training script for the Hugging Face SageMaker estimator. script to use with Script Mode and pass hyperparameters for training. return tokenized_dataset. If we use an ml.g4dn.16xlarge
AWS Machine Learning
OCTOBER 5, 2023
In this post, we walk through how to fine-tune Llama 2 on AWS Trainium , a purpose-built accelerator for LLM training, to reduce training times and costs. 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.
AWS Machine Learning
JUNE 17, 2024
This post focuses on how to achieve flexibility in using your data source of choice and integrate it seamlessly with Amazon SageMaker Processing jobs. With SageMaker Processing jobs, you can use a simplified, managed experience to run data preprocessing or postprocessing and model evaluation workloads on the SageMaker platform.
AWS Machine Learning
FEBRUARY 29, 2024
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
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