APIs Big data Document Scripts 
AWS Machine Learning
FEBRUARY 29, 2024
Knowledge Bases for Amazon Bedrock automates synchronization of your data with your vector store, including diffing the data when it’s updated, document loading, and chunking, as well as semantic embedding. RAG is a popular technique that combines the use of private data with large language models (LLMs).
AWS Machine Learning
JULY 29, 2022
Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to build, train, and deploy machine learning (ML) models quickly. The SageMaker Python SDK provides open-source APIs and containers to train and deploy models on SageMaker, using several different ML and deep learning frameworks.
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AWS Machine Learning
DECEMBER 21, 2022
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.
AWS Machine Learning
MARCH 23, 2023
Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture. To learn more about real-time endpoint architectural best practices, refer to Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker.
AWS Machine Learning
JULY 25, 2022
For detailed instructions on how to use the DGL-KE, refer to Training knowledge graph embeddings at scale with the Deep Graph Library and DGL-KE Documentation. For production, we wanted to invoke the model as a simple API call. Packaging the solution as a scalable workflow. We used SageMaker notebooks to develop and debug our code.
AWS Machine Learning
SEPTEMBER 9, 2022
In the artificial intelligence (AI) space, athenahealth uses data science and machine learning (ML) to accelerate business processes and provide recommendations, predictions, and insights across multiple services. Each project maintained detailed documentation that outlined how each script was used to build the final model.
AWS Machine Learning
JULY 21, 2022
As a result, this experimentation phase can produce multiple models, each created from their own inputs (datasets, training scripts, and hyperparameters) and producing their own outputs (model artifacts and evaluation metrics). At the start, the process is full of uncertainty and is highly iterative.
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