APIs Big data Scripts Training 
AWS Machine Learning
AUGUST 3, 2023
Training ML algorithms for pose estimation requires a lot of expertise and custom training data. Therefore, we present two options: one that doesn’t require any ML expertise and uses Amazon Rekognition, and another that uses Amazon SageMaker to train and deploy a custom ML model.
AWS Machine Learning
FEBRUARY 29, 2024
The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.
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AWS Machine Learning
DECEMBER 21, 2022
As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring. The offline store is primarily used for batch predictions and model training.
AWS Machine Learning
MARCH 23, 2023
Although this example shows how to perform this for inference operations, you can extend the solution to training and other ML steps. Endpoints are deployed with a couple clicks or lines of code using SageMaker, which simplifies the process for developers and ML experts to build and train ML and deep learning models in the cloud.
AWS Machine Learning
FEBRUARY 13, 2024
Features are inputs to ML models used during training and inference. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. Teams can discover and directly consume features created by others instead of duplicating them in each account.
AWS Machine Learning
OCTOBER 4, 2022
Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. Model training and tuning. In the data preparation step, data is loaded, massaged, and transformed into the type of inputs, or features, the ML model expects. SageMaker Pipelines.
AWS Machine Learning
JANUARY 26, 2024
Plan for rollback and recovery from production security events and service disruptions such as prompt injection, training data poisoning, model denial of service, and model theft early on, and define the mitigations you will use as you define application requirements.
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