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Description
Amazon SageMaker JumpStart serves as a comprehensive hub for machine learning (ML), designed to expedite your ML development process. This platform allows users to utilize various built-in algorithms accompanied by pretrained models sourced from model repositories, as well as foundational models that facilitate tasks like article summarization and image creation. Furthermore, it offers ready-made solutions aimed at addressing prevalent use cases in the field. Additionally, users have the ability to share ML artifacts, such as models and notebooks, within their organization to streamline the process of building and deploying ML models. SageMaker JumpStart boasts an extensive selection of hundreds of built-in algorithms paired with pretrained models from well-known hubs like TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. Furthermore, the SageMaker Python SDK allows for easy access to these built-in algorithms, which cater to various common ML functions, including data classification across images, text, and tabular data, as well as conducting sentiment analysis. This diverse range of features ensures that users have the necessary tools to effectively tackle their unique ML challenges.
Description
NVIDIA PhysicsNeMo is a publicly available Python-based deep-learning framework designed for the creation, training, fine-tuning, and inference of physics-AI models that integrate physical principles with data, thereby enhancing simulations, developing accurate surrogate models, and facilitating near-real-time predictions in various fields such as computational fluid dynamics, structural mechanics, electromagnetics, weather forecasting, climate studies, and digital twin technologies. This framework offers powerful, GPU-accelerated capabilities along with Python APIs that are built on the PyTorch platform and distributed under the Apache 2.0 license, featuring a selection of curated model architectures that include physics-informed neural networks, neural operators, graph neural networks, and generative AI techniques, enabling developers to effectively leverage physics-based causal relationships together with empirical data for high-quality engineering modeling. Additionally, PhysicsNeMo provides comprehensive training pipelines that encompass everything from geometry ingestion to the application of differential equations, along with reference application recipes that help users quickly initiate their development workflows. This combination of features makes PhysicsNeMo an essential tool for engineers and researchers seeking to advance their work in physics-driven AI applications.
API Access
Has API
API Access
Has API
Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
PyTorch
Python
Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
PyTorch
Python
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/sagemaker/jumpstart/
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
developer.nvidia.com/physicsnemo
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization