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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

Orchestrate with Tecton-managed EMR clusters – After features are deployed, Tecton automatically creates the scheduling, provisioning, and orchestration needed for pipelines that can run on Amazon EMR compute engines. You can view and create EMR clusters directly through the SageMaker notebook.

ML 101
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

The traditional approach of manually sifting through countless research documents, industry reports, and financial statements is not only time-consuming but can also lead to missed opportunities and incomplete analysis. This event-driven architecture provides immediate processing of new documents.

AWS 112
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Customize DeepSeek-R1 671b model using Amazon SageMaker HyperPod recipes – Part 2

AWS Machine Learning Blog

The following diagram illustrates the solution architecture for training using SageMaker HyperPod. With HyperPod, users can begin the process by connecting to the login/head node of the Slurm cluster. These steps are encapsulated in a prologue script and are documented step-by-step under the Fine-tuning section.

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Transforming the future: A journey into model-based systems engineering at Singapore Institute of Technology

IBM Journey to AI blog

MBSE brings complex systems to life with visual models, moving away from the paperwork-heavy traditional methods. In other words, MBSE elevates systems engineering by using models. “In the past, we used other industry tools, but they were very limited,” says A/Prof Paw.

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Redesigning Snorkel’s interactive machine learning systems

Snorkel AI

Use cases over complex data types such as PDF documents gain priority as our customers have the right tools, like Snorkel Flow, to tackle them. Because frequent patching required a lot of our time and didn’t always deliver the results we hoped for, we decided it was better to rebuild the system from the ground up.

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Redesigning Snorkel’s interactive machine learning systems

Snorkel AI

Use cases over complex data types such as PDF documents gain priority as our customers have the right tools, like Snorkel Flow, to tackle them. Because frequent patching required a lot of our time and didn’t always deliver the results we hoped for, we decided it was better to rebuild the system from the ground up.

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Meeting customer needs with our ML platform redesign

Snorkel AI

Use cases over complex data types such as PDF documents gain priority as our customers have the right tools, like Snorkel Flow, to tackle them. Because frequent patching required a lot of our time and didn’t always deliver the results we hoped for, we decided it was better to rebuild the system from the ground up.

ML 52