Computer Vision Data Ingestion Metadata 
AWS Machine Learning Blog
OCTOBER 6, 2023
A feature store maintains user profile data. A media metadata store keeps the promotion movie list up to date. A language model takes the current movie list and user profile data, and outputs the top three recommended movies for each user, written in their preferred tone.
AWS Machine Learning Blog
MAY 4, 2023
Each dataset group can have up to three datasets, one of each dataset type: target time series (TTS), related time series (RTS), and item metadata. A dataset is a collection of files that contain data that is relevant for a forecasting task. DatasetGroupFrequencyTTS The frequency of data collection for the TTS dataset.
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AWS Machine Learning Blog
JANUARY 6, 2023
In this post, we illustrate how to handle OOC by utilizing the power of the IMDb dataset (the premier source of global entertainment metadata) and knowledge graphs. Creates a Lambda function to process and load movie metadata and embeddings to OpenSearch Service indexes ( **-ReadFromOpenSearchLambda-** ).
AWS Machine Learning Blog
OCTOBER 2, 2024
Additionally, you can enable model invocation logging to collect invocation logs, full request response data, and metadata for all Amazon Bedrock model API invocations in your AWS account. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.
The MLOps Blog
JUNE 27, 2023
Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.
AWS Machine Learning Blog
JUNE 27, 2023
Earth.com’s leadership team recognized the vast potential of EarthSnap and set out to create an application that utilizes the latest deep learning (DL) architectures for computer vision (CV). The initial solution also required the support of a technical third party, to release new models swiftly and efficiently.
The MLOps Blog
MARCH 21, 2023
To make that possible, your data scientists would need to store enough details about the environment the model was created in and the related metadata so that the model could be recreated with the same or similar outcomes. Your ML platform must have versioning in-built because code and data mostly make up the ML system.
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