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

AWS Machine Learning Blog

Discover Llama 4 models in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: SageMaker Studio and the Amazon SageMaker Python SDK. Alternatively, you can use the SageMaker Python SDK to programmatically access and use SageMaker JumpStart models. billion to a projected $574.78

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

AWS Machine Learning Blog

We use DSPy (Declarative Self-improving Python) to demonstrate the workflow of Retrieval Augmented Generation (RAG) optimization, LLM fine-tuning and evaluation, and human preference alignment for performance improvement. Evaluation and continuous learning The model customization and preference alignment is not a one-time effort.

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Can Using Deep Learning to Write Code Help Software Developers Stand Out?

Smart Data Collective

The learning potential of deep learning was further demonstrated by AlphaGo in 2016 and, today, it is used increasingly to create high level software engineering (SE) tools. Deep learning is achieved when data is run through layers of neural network algorithms.

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Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

Solution overview In this blog, we will walk through the following scenarios : Deploy Llama 2 on AWS Inferentia instances in both the Amazon SageMaker Studio UI, with a one-click deployment experience, and the SageMaker Python SDK. Fine-tune Llama 2 on Trainium instances in both the SageMaker Studio UI and the SageMaker Python SDK.

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Interactive Fleet Learning

BAIR

This approach is known as “Fleet Learning,” a term popularized by Elon Musk in 2016 press releases about Tesla Autopilot and used in press communications by Toyota Research Institute , Wayve AI , and others. Using this formalism, we can now instantiate and compare IFL algorithms (i.e., allocation policies) in a principled way.

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Otter-Knowledge

IBM Data Science in Practice

In Otter-Knoweldge, we use different pre-trained models and/or algorithms to handle the different modalities of the KG, what we call handlers. These handlers might be complex pre-trained deep learning models, like MolFormer or ESM, or simple algorithms like the morgan fingerprint. Nucleic acids research, 44(D1):D380–D384, 2016.

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Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

Challenges in FL You can address the following challenges using algorithms running at FL servers and clients in a common FL architecture: Data heterogeneity – FL clients’ local data can vary (i.e., Despite these challenges of FL algorithms, it is critical to build a secure architecture that provides end-to-end FL operations.

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