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Average Ratings 0 Ratings
Description
Amazon SageMaker Canvas democratizes access to machine learning by equipping business analysts with an intuitive visual interface that enables them to independently create precise ML predictions without needing prior ML knowledge or coding skills. This user-friendly point-and-click interface facilitates the connection, preparation, analysis, and exploration of data, simplifying the process of constructing ML models and producing reliable predictions. Users can effortlessly build ML models to conduct what-if scenarios and generate both individual and bulk predictions with minimal effort. The platform enhances teamwork between business analysts and data scientists, allowing for the seamless sharing, reviewing, and updating of ML models across different tools. Additionally, users can import ML models from various sources and obtain predictions directly within Amazon SageMaker Canvas. With this tool, you can draw data from diverse origins, specify the outcomes you wish to forecast, and automatically prepare as well as examine your data, enabling a swift and straightforward model-building experience. Ultimately, this capability allows users to analyze their models and yield accurate predictions, fostering a more data-driven decision-making culture across organizations.
Description
Amazon SageMaker HyperPod is a specialized and robust computing infrastructure designed to streamline and speed up the creation of extensive AI and machine learning models by managing distributed training, fine-tuning, and inference across numerous clusters equipped with hundreds or thousands of accelerators, such as GPUs and AWS Trainium chips. By alleviating the burdens associated with developing and overseeing machine learning infrastructure, it provides persistent clusters capable of automatically identifying and rectifying hardware malfunctions, resuming workloads seamlessly, and optimizing checkpointing to minimize the risk of interruptions — thus facilitating uninterrupted training sessions that can last for months. Furthermore, HyperPod features centralized resource governance, allowing administrators to establish priorities, quotas, and task-preemption rules to ensure that computing resources are allocated effectively among various tasks and teams, which maximizes utilization and decreases idle time. It also includes support for “recipes” and pre-configured settings, enabling rapid fine-tuning or customization of foundational models, such as Llama. This innovative infrastructure not only enhances efficiency but also empowers data scientists to focus more on developing their models rather than managing the underlying technology.
API Access
Has API
No
API Access
Has API
No
Integrations
Amazon SageMaker
Yes
Amazon Web Services (AWS)
Yes
AWS EC2 Trn3 Instances
No
AWS Trainium
No
Amazon SageMaker Unified Studio
Yes
Integrations
Amazon SageMaker
Yes
Amazon Web Services (AWS)
Yes
AWS EC2 Trn3 Instances
Yes
AWS Trainium
Yes
Amazon SageMaker Unified Studio
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
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
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)
Yes
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/sagemaker/ai/canvas/
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/sagemaker/ai/hyperpod/
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