Designing a therapeutic protein that specifically binds its target in drug discovery is a staggering challenge. Traditional workflows are often a painstaking trial-and-error process—iterating through thousands of candidates, each synthesis and validation round taking months if not years. Considering the average human protein is 430 amino acids long, the number of possible designs translates to…
]]>In the era of big data and distributed computing, traditional approaches to machine learning (ML) face a significant challenge: how to train models collaboratively when data is decentralized across multiple devices or silos. This is where federated learning comes into play, offering a promising solution that decouples model training from direct access to raw training data. One of the key…
]]>One of the main challenges for businesses leveraging AI in their workflows is managing the infrastructure needed to support large-scale training and deployment of machine learning (ML) models. The NVIDIA FLARE platform provides a solution: a powerful, scalable infrastructure for federated learning that makes it easier to manage complex AI workflows across enterprises. NVIDIA FLARE 2.3.0…
]]>NVIDIA FLARE 2.2 includes a host of new features that reduce development time and accelerate deployment for federated learning, helping organizations cut costs for building robust AI. Get the details about what’s new in this release. An open-source platform and software development kit (SDK) for Federated Learning (FL), NVIDIA FLARE continues to evolve to enable its end users to leverage…
]]>NVIDIA FLARE (NVIDIA Federated Learning Application Runtime Environment) is an open-source Python SDK for collaborative computation. FLARE is designed with a componentized architecture that allows researchers and data scientists to adapt machine learning, deep learning, or general compute workflows to a federated paradigm to enable secure, privacy-preserving multi-party collaboration.
]]>Medical imaging AI models built with NVIDIA Clara can now run natively on MD.ai in the cloud, which enables collaborative model validation and rapid annotation projects using modern web browsers. These NVIDIA Clara models are free to use in any MD.ai project for collaborative research, such as for organ or tumor segmentation. AI solutions have been shown to help streamline radiology and…
]]>Getting AI up and running in hospitals has never been more important. Until recently, connecting an inference pipeline to perform analysis has had its challenges and limitations. There is a considerable amount of complexity in setting up and maintaining the hardware and software, deployment, configuration, and all workflow steps in an AI inference research pipeline. NVIDIA Clara Deploy…
]]>This is an updated version of Neural Modules for Fast Development of Speech and Language Models. This post upgrades the NeMo diagram with PyTorch and PyTorch Lightning support and updates the tutorial with the new code base. As a researcher building state-of-the-art speech and language models, you must be able to quickly experiment with novel network architectures.
]]>This post has been updated with Announcing NVIDIA NeMo: Fast Development of Speech and Language Models. The new version has information about pretrained models in NGC and fine-tuning models on custom dataset sections, upgrades the NeMo diagram with the text-to-speech collection, and replaces the AN4 dataset in the example with the LibriSpeech dataset. As a researcher building state-of-the…
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