APIs Consulting Scripts Training 
AWS Machine Learning
APRIL 29, 2024
In recent years, large language models (LLMs) have gained attention for their effectiveness, leading various industries to adapt general LLMs to their data for improved results, making efficient training and hardware availability crucial. In this post, we show you how efficient we make our continual pre-training by using Trainium chips.
AWS Machine Learning
JANUARY 5, 2024
The AWS portfolio of ML services includes a robust set of services that you can use to accelerate the development, training, and deployment of machine learning applications. Collaboration – Data scientists each worked on their own local Jupyter notebooks to create and train ML models.
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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. Both requirements are hard and costly to obtain.
COPC
OCTOBER 26, 2023
Understanding AI Terminology Generative AI: These are algorithms trained to predict data sequences based on training information. LLMs are unique because they have been trained on enormous data sets. This training gives them capabilities beyond any AI we have seen before.
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.
AWS Machine Learning
SEPTEMBER 28, 2022
Data scientists often train their models locally and look for a proper hosting service to deploy their models. Unfortunately, there’s no one set mechanism or guide to deploying pre-trained models to the cloud. In this post, we look at deploying trained models to Amazon SageMaker hosting to reduce your deployment time.
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.
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