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Exploring summarization options for Healthcare with Amazon SageMaker

AWS Machine Learning

In today’s rapidly evolving healthcare landscape, doctors are faced with vast amounts of clinical data from various sources, such as caregiver notes, electronic health records, and imaging reports. In a healthcare setting, this would mean giving the model some data including phrases and terminology pertaining specifically to patient care.

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Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

AWS Machine Learning

Customers are now pre-training and fine-tuning LLMs ranging from 1 billion to over 175 billion parameters to optimize model performance for applications across industries, from healthcare to finance and marketing. For more information on how to enable SMP with your existing PyTorch FSDP training scripts, refer to Get started with SMP.

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Everything You Need to Know About Auto Attendant

Hodusoft

In this blog post, we will discuss everything about auto attendants starting from what they are, how they work, the pros and cons of using auto attendants, how to set up an auto attendant, and how to include scripts. Pros of Using Auto Attendant Cons of Auto Attendant Auto Attendant Scripts – What to Record? Read on to know more.

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Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together

AWS Machine Learning

Media organizations can generate image captions or video scripts automatically. As enterprise customers rely on Claude across industries like healthcare, finance, and legal research, reducing hallucinations is essential for safety and performance. Claude 3 Opus shows an estimated 2x gain in accuracy over Claude 2.1

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Revolutionizing large language model training with Arcee and AWS Trainium

AWS Machine Learning

Now you can launch a training job to submit a model training script as a slurm job. Finally, convert the saved checkpoints back to a standard format for subsequent use, employing scripts for seamless conversion. Malikeh Ehghaghi is an Applied NLP Research Engineer at Arcee. Create and launch ParallelCluster in the VPC.

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Build Streamlit apps in Amazon SageMaker Studio

AWS Machine Learning

Next the script will install packages iproute and jq , which will be used in the following step. Next the script will install packages iproute and jq , which will be used in the following step. Note that while developing, it might be helpful to automatically rerun the script when app.py sh setup.sh sh setup.sh

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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

Amazon SageMaker Feature Store is a purpose-built feature management solution that helps data scientists and ML engineers securely store, discover, and share curated data used in training and prediction workflows. Amazon Athena is a serverless SQL query engine that natively supports Iceberg management procedures. AWS Glue Job setup.

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