AWS Machine Learning Blog

SEPTEMBER 29, 2023

Planet and AWS’s partnership on geospatial ML SageMaker geospatial capabilities empower data scientists and ML engineers to build, train, and deploy models using geospatial data. This example uses the Python client to identify and download imagery needed for the analysis. Xiong Zhou is a Senior Applied Scientist at AWS.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

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AWS Machine Learning Blog

OCTOBER 4, 2024

You can download the generated images directly from the UI or check the image in your S3 bucket. About the Authors Akarsha Sehwag is a Data Scientist and ML Engineer in AWS Professional Services with over 5 years of experience building ML based solutions.

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Data Scientist Generative AI Machine Learning ML 100

AWS Machine Learning Blog

FEBRUARY 12, 2025

SageMaker AI starts and manages all the necessary Amazon Elastic Compute Cloud (Amazon EC2) instances for us, supplies the appropriate containers, downloads data from our S3 bucket to the container and uploads and runs the specified training script, in our case fine_tune_llm.py. Moran Beladev is a Senior ML Manager at Booking.com.

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AWS Machine Learning Blog

MAY 5, 2023

Voxel51 is the company behind FiftyOne, the open-source toolkit for building high-quality datasets and computer vision models. Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets.

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AWS Machine Learning Blog

SEPTEMBER 25, 2024

You can now use state-of-the-art model architectures, such as language models, computer vision models, and more, without having to build them from scratch. Single-image input You can set up vision-based reasoning tasks with Llama 3.2 With SageMaker JumpStart, you can deploy models in a secure environment. models today.

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Software Engineer Software Development ML Python 125

AWS Machine Learning Blog

OCTOBER 22, 2024

Data scientists and machine learning (ML) engineers use pipelines for tasks such as continuous fine-tuning of large language models (LLMs) and scheduled notebook job workflows. Download the pipeline definition as a JSON file to your local environment by choosing Export at the bottom of the visual editor.

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AWS Machine Learning Blog

FEBRUARY 9, 2023

Rather than downloading the data to a local machine for inferences, SageMaker does all the heavy lifting for you. SageMaker automatically downloads and preprocesses the satellite image data for the EOJ, making it ready for inference. This land cover segmentation model can be run with a simple API call.

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The MLOps Blog

MAY 10, 2023

2 For dynamic models, such as those with variable-length inputs or outputs, which are frequent in natural language processing (NLP) and computer vision, PyTorch offers improved support. To save the model using ONNX, you need to have onnx and onnxruntime packages downloaded in your system. In this example, I’ll use the Neptune.

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The MLOps Blog

JUNE 27, 2023

MLOps workflows for computer vision and ML teams Use-case-centric annotations. Data storage and versioning You need data storage and versioning tools to maintain data integrity, enable collaboration, facilitate the reproducibility of experiments and analyses, and ensure accurate ML model development and deployment.

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