![](https://hitconsultant.net/wp-content/uploads/2024/05/NVIDIA-AI-Microservices-for-Drug-Discovery-Digital-Health-Now-Integrated-With-AWS.png)
What you should know:
– Harnessing the power of AI for healthcare just got easier. Nvidia announcement integrating NVIDIA NIM, a set of cloud-native AI microservices, with Amazon Web Services (AWS).
– Strategic collaboration allows developers to leverage pre-trained training AI models for drug discovery, medical imaging analysis and genomics research, all accessible via user-friendly APIs.
Simplified access to powerful AI tools
NIM integrates seamlessly with popular AWS services such as SageMaker (for developing machine learning models) and ParallelCluster (for high-performance computing). Additionally, AWS HealthOmics, a specialized service for biological data analysis, can orchestrate NIM workflows. This integration simplifies the process of deploying cutting-edge AI models for healthcare and life sciences companies already using AWS cloud infrastructure.
Benefits of NVIDIA NIM on AWS
- Faster development: Researchers can bypass the development and packaging of complex models, accelerating the deployment of generative AI solutions.
- Multimodal workflows: NIM makes it easy to create workflows combining AI models from various sources, such as protein sequences, medical images, and patient records.
- Enhanced Drug Discovery: BioNeMo, a foundation for AI models in drug discovery, is included in NIM. Companies like Amgen are already using BioNeMo to train protein design models.
Concrete examples of success
- Bio A-Alpha: This company developing tools for predicting protein interactions saw a 10x speedup using BioNeMo models optimized for NVIDIA GPUs on AWS.
- Agile : This life sciences leader used NVIDIA Parabricks, a genomic analysis tool within NIM, to significantly improve processing speeds of variant calling workflows.
Beyond drug discovery and genomics
NIM offers much more than just scientific applications. It also includes:
- Major language models: These powerful language processing tools can be used to create AI-powered conversational healthcare assistants for patient education and clinician support.
- Visual Generative AI: This technology enables the development of digital avatars and chatbots for a more engaging user experience.