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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.
Description
Discover a revolutionary approach to training machine learning models directly on your Mac with Create ML, which simplifies the process while delivering robust Core ML models. You can train several models with various datasets all within one cohesive project. Utilize Continuity to preview your model's performance by connecting your iPhone's camera and microphone to your Mac, or simply input sample data for evaluation. The training process allows you to pause, save, resume, and even extend as needed. Gain insights into how your model performs against test data from your evaluation set and delve into essential metrics, exploring their relationships to specific examples, which can highlight difficult use cases, guide further data collection efforts, and uncover opportunities to enhance model quality. Additionally, if you want to elevate your training performance, you can integrate an external graphics processing unit with your Mac. Experience the lightning-fast training capabilities available on your Mac that leverage both CPU and GPU resources, and take your pick from a diverse selection of model types offered by Create ML. This tool not only streamlines the training process but also empowers users to maximize the effectiveness of their machine learning endeavors.
API Access
Has API
API Access
Has API
Integrations
AWS EC2 Trn3 Instances
AWS Trainium
Amazon SageMaker
Amazon Web Services (AWS)
Integrations
AWS EC2 Trn3 Instances
AWS Trainium
Amazon SageMaker
Amazon Web Services (AWS)
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
1994
Country
United States
Website
aws.amazon.com/sagemaker/ai/hyperpod/
Vendor Details
Company Name
Apple
Country
United States
Website
developer.apple.com/machine-learning/create-ml/
Product Features
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization