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Evaluating Long-Context Question & Answer Systems

Eugene Yan

Open-ended questions: Queries on broad themes or interpretative topics rarely have a single definitive answer, especially for large documents or corpora. Definitions: These assess a model’s ability to explain domain-specific content based on the document. or “What is the legal clause mentioned in Section 2.1?”

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Efficiently build and tune custom log anomaly detection models with Amazon SageMaker

AWS Machine Learning Blog

The SageMaker Python SDK provides the ScriptProcessor class, which you can use to run your custom processing script in a SageMaker processing step. SageMaker provides the PySparkProcessor class within the SageMaker Python SDK for running Spark jobs. slim-buster RUN pip3 install pandas==0.25.3 scikit-learn==0.21.3

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Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI

AWS Machine Learning Blog

Ray is an open source framework that makes it straightforward to create, deploy, and optimize distributed Python jobs. At its core, Ray offers a unified programming model that allows developers to seamlessly scale their applications from a single machine to a distributed cluster. Ray clusters and Kubernetes clusters pair well together.

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Build agentic AI solutions with DeepSeek-R1, CrewAI, and Amazon SageMaker AI

Flipboard

These services support single GPU to HyperPods (cluster of GPUs) for training and include built-in FMOps tools for tracking, debugging, and deployment. Having access to a JupyterLab IDE with Python 3.9, To get started, complete the following steps: Install the latest version of the sagemaker-python-sdk using pip. 3.10, or 3.11

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How Druva used Amazon Bedrock to address foundation model complexity when building Dru, Druva’s backup AI copilot

AWS Machine Learning Blog

Generate and run data transformation Python code. Stream 3: Generate and run data transformation Python code Next, we took the response from the API call and transformed it to answer the user question. The request arrives at the microservice on our existing Amazon Elastic Container Service (Amazon ECS) cluster.

Python 110
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Cloud Pak for Data 4.6 Code Experience with VS Code Integration

IBM Data Science in Practice

VS Code desktop integration lets data scientists use a familiar IDE to run and debug code that runs on the Cloud Pak for Data cluster. We show how the new Watson Studio extension for VS Code makes it easy to connect to Python runtime environments within Cloud Pak for Data projects. New in Cloud Pak for Data 4.6,

Python 130
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Scale your machine learning workloads on Amazon ECS powered by AWS Trainium instances

AWS Machine Learning Blog

With containers, scaling on a cluster becomes much easier. Solution overview We walk you through the following high-level steps: Provision an ECS cluster of Trn1 instances with AWS CloudFormation. Create a task definition to define an ML training job to be run by Amazon ECS. Run the ML task on Amazon ECS.

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