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Getting Started with Docker for Machine Learning

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Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. Download the RPM (Red Hat Package Management system) file for Docker Desktop ( Note: This link may change in the future.

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LLM experimentation at scale using Amazon SageMaker Pipelines and MLflow

AWS Machine Learning Blog

It can also be done at scale, as explained in Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services. Fine-tuning an LLM can be a complex workflow for data scientists and machine learning (ML) engineers to operationalize. In this example, we download the data from a Hugging Face dataset.

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Llama 4 family of models from Meta are now available in SageMaker JumpStart

AWS Machine Learning Blog

download_file(s3_bucket, f"{key_prefix}/{key_filename}", key_filename) # Define image names heat_map = "heatmap_semantic_similarity_search.png" # Download and display the heatmap image download_from_s3(key_filenames=[heat_map]) def img_to_base64(image_path): with open(image_path, "rb") as f: img = f.read() enc_img = base64.b64encode(img).decode('utf-8')

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Getting Used to Docker for Machine Learning

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Getting Used to Docker for Machine Learning Introduction Docker is a powerful addition to any development environment, and this especially rings true for ML Engineers or enthusiasts who want to get started with experimentation without having to go through the hassle of setting up several drivers, packages, and more. the image).

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LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker

AWS Machine Learning Blog

The concept of a compound AI system enables data scientists and ML engineers to design sophisticated generative AI systems consisting of multiple models and components. Clone the GitHub repository and follow the steps explained in the README. These components can include multiple calls to models, retrievers, or external tools.

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Automatically redact PII for machine learning using Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

Amazon SageMaker provides purpose-built tools for ML teams to automate and standardize processes across the ML lifecycle. Download the SageMaker Data Wrangler flow. Download the SageMaker Data Wrangler flow You first need to retrieve the SageMaker Data Wrangler flow file from GitHub and upload it to SageMaker Studio.

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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning Blog

The randomization process was adequately explained to patients, and they understood the rationale behind blinding, which is to prevent bias in the results (Transcript 2). You can download a sample file and review the contents. Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice.

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