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Average Ratings 0 Ratings
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
TorchMetrics comprises over 90 implementations of metrics designed for PyTorch, along with a user-friendly API that allows for the creation of custom metrics. It provides a consistent interface that enhances reproducibility while minimizing redundant code. The library is suitable for distributed training and has undergone thorough testing to ensure reliability. It features automatic batch accumulation and seamless synchronization across multiple devices. You can integrate TorchMetrics into any PyTorch model or utilize it within PyTorch Lightning for added advantages, ensuring that your data aligns with the same device as your metrics at all times. Additionally, you can directly log Metric objects in Lightning, further reducing boilerplate code. Much like torch.nn, the majority of metrics are available in both class-based and functional formats. The functional versions consist of straightforward Python functions that accept torch.tensors as inputs and yield the corresponding metric as a torch.tensor output. Virtually all functional metrics come with an equivalent class-based metric, providing users with flexible options for implementation. This versatility allows developers to choose the approach that best fits their coding style and project requirements.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Amazon SageMaker
Yes
Amazon SageMaker Unified Studio
Yes
Amazon Web Services (AWS)
Yes
Lightning AI
No
PyTorch
No
Integrations
Amazon SageMaker
No
Amazon SageMaker Unified Studio
No
Amazon Web Services (AWS)
No
Lightning AI
Yes
PyTorch
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/sagemaker/jumpstart/
Vendor Details
Company Name
TorchMetrics
Country
United States
Website
torchmetrics.readthedocs.io/en/stable/
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No
Product Features
Application Development
Access Controls/Permissions
No
Code Assistance
No
Code Refactoring
No
Collaboration Tools
No
Compatibility Testing
No
Data Modeling
No
Debugging
No
Deployment Management
No
Graphical User Interface
No
Mobile Development
No
No-Code
No
Reporting/Analytics
No
Software Development
No
Source Control
No
Testing Management
No
Version Control
No
Web App Development
No