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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
tensorViz provides a platform for developers to delve into PyTorch models and JAX/Flax programs through dynamic graph visualizations. In the case of PyTorch, users have the ability to open nested modules, examine tensor shapes recorded during a single forward pass, navigate to the corresponding Python source code, and observe architectural modifications alongside code differences. The site additionally features pre-recorded model graphs for users to analyze without the need for any installation. A free extension for VS Code can be found in both the Visual Studio Marketplace and Open VSX. For local capture, users must have Python installed along with the necessary model dependencies and sample inputs. There are also standalone coding-agent plugins that are currently in preview for Apple Silicon macOS; MCP agents can establish connections with the extension as well. While JAX/Flax graphs utilize CPU tracing to infer shapes, it is important to note that source edits for PyTorch models are not applicable in this context. By offering a seamless experience for both frameworks, tensorViz enhances the development and debugging process for users.
API Access
Has API
No
API Access
Has API
Yes
Screenshots View All
No images available
Integrations
Amazon SageMaker
Yes
Amazon SageMaker Unified Studio
Yes
Amazon Web Services (AWS)
Yes
Claude Code
No
Codex CLI
No
Cursor
No
GitHub
No
Model Context Protocol (MCP)
No
T3 Code
No
Visual Studio Code
No
Integrations
Amazon SageMaker
No
Amazon SageMaker Unified Studio
No
Amazon Web Services (AWS)
No
Claude Code
Yes
Codex CLI
Yes
Cursor
Yes
GitHub
Yes
Model Context Protocol (MCP)
Yes
T3 Code
Yes
Visual Studio Code
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$0
The VS Code extension is free to install. Core graph exploration and source-edit forms work without an agent or provider API key; connecting a coding agent is optional.
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
Yes
Mac
Yes
Linux
Yes
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
tensorViz
Website
tensorviz.dev/
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
Deep Learning
Convolutional Neural Networks
No
Document Classification
No
Image Segmentation
No
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
No
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