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Introduction to Process Mining: Understanding the Basics and Benefits

CSM Magazine

By visualizing the end-to-end flow, teams can identify inefficiencies and make informed decisions. Performance Analysis : Metrics such as throughput, cycle time, and resource utilization are essential for process optimization. How can we improve resource allocation? logs from ERP systems, applications, or databases).

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Revolutionizing large language model training with Arcee and AWS Trainium

AWS Machine Learning

Close collaboration with AWS Trainium has also played a major role in making the Arcee platform extremely performant, not only accelerating model training but also reducing overall costs and enforcing compliance and data integrity in the secure AWS environment. Clean up Don’t forget to tear down any resources you set up in this post.

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Accelerate machine learning time to value with Amazon SageMaker JumpStart and PwC’s MLOps accelerator

AWS Machine Learning

Thinking of ML in terms of pipelines that generate and maintain models rather than models by themselves helps build versatile and resilient prediction systems that are better able to withstand meaningful changes in relevant data over time. Many organizations start their journey into the world of ML with a model-centric viewpoint.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Amazon Bedrock provides a broad range of models from Amazon and third-party providers, including Anthropic, AI21, Meta, Cohere, and Stability AI, and covers a wide range of use cases, including text and image generation, embedding, chat, high-level agents with reasoning and orchestration, and more.

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Exploring summarization options for Healthcare with Amazon SageMaker

AWS Machine Learning

This wealth of information, while essential for patient care, can also be overwhelming and time-consuming for medical professionals to sift through and analyze. Models can be trained to analyze and interpret large volumes of text data, effectively condensing information into concise summaries.

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Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

AWS Machine Learning

Building a production-ready solution in the cloud involves a series of trade-offs between resources, time, customer expectation, and business outcome. It involves organizing teams effectively, designing IDP systems to handle workloads efficiently, operating these systems at scale, and continuously evolving them to meet customer needs.

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­­­­How Sleepme uses Amazon SageMaker for automated temperature control to maximize sleep quality in real time

AWS Machine Learning

Sleepme offers a smart mattress topper system that can be scheduled to cool or heat your bed using the companion application. The system can be paired with a sleep tracker that gathers insights such as heart rate, respiration rate, humidity in the room, wake up times, and when the user was in and out of bed.