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

AWS Machine Learning

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.

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

AWS Machine Learning

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. Run the script init-script.bash : chmod u+x init-script.bash./init-script.bash

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

AWS Machine Learning

With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. It also provides common ML algorithms that are optimized to run efficiently against extremely large data in a distributed environment.

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Prepare image data with Amazon SageMaker Data Wrangler

AWS Machine Learning

The rapid adoption of smart phones and other mobile platforms has generated an enormous amount of image data. According to Gartner , unstructured data now represents 80–90% of all new enterprise data, but just 18% of organizations are taking advantage of this data.

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Perform generative AI-powered data prep and no-code ML over any size of data using Amazon SageMaker Canvas

AWS Machine Learning

Amazon SageMaker Canvas now empowers enterprises to harness the full potential of their data by enabling support of petabyte-scale datasets. Organizations often struggle to extract meaningful insights and value from their ever-growing volume of data. On the Data flows tab, choose Tabular on the Import and prepare dropdown menu.

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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

Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data without the need of any prior ML experience. Build your training script for the Hugging Face SageMaker estimator. return tokenized_dataset. to(device).

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

AWS Machine Learning

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. For example, to use the RedPajama dataset, use the following command: wget [link] python nemo/scripts/nlp_language_modeling/preprocess_data_for_megatron.py

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