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Average Ratings 0 Ratings
Description
A comprehensive platform for managing projects, models, and hosting, designed for organizations to transform their data and algorithms into cohesive, execution-ready AI strategies. Effortlessly build, train, and oversee models while ensuring security throughout the process. Create AI-driven products that can be accessed at any time and from any location. This approach minimizes the risks associated with AI investments and enhances strategic adaptability. It features fully automated processes for model testing, evaluation, deployment, scaling, and hardware load balancing, catering to both real-time low-latency high-throughput inference and longer batch inference. The pricing structure operates on a pay-per-second-of-use basis, including a service-level agreement (SLA) and comprehensive governance, monitoring, and auditing features. The platform boasts an intuitive interface that serves as a centralized hub for project management, dataset creation, visualization, and model training, all facilitated through collaborative and reproducible workflows. This empowers teams to work together seamlessly, ensuring that the development of AI solutions is efficient and effective.
Description
We created Tenstorrent DevCloud to enable users to experiment with their models on our servers without the need to invest in our hardware. By developing Tenstorrent AI in the cloud, we allow developers to explore our AI offerings easily. The initial login is complimentary, after which users can connect with our dedicated team to better understand their specific requirements. Our team at Tenstorrent consists of highly skilled and enthusiastic individuals united in their goal to create the ultimate computing platform for AI and software 2.0. As a forward-thinking computing company, Tenstorrent is committed to meeting the increasing computational needs of software 2.0. Based in Toronto, Canada, Tenstorrent gathers specialists in computer architecture, foundational design, advanced systems, and neural network compilers. Our processors are specifically designed for efficient neural network training and inference while also capable of handling various types of parallel computations. These processors feature a network of cores referred to as Tensix cores, which enhance performance and scalability. With a focus on innovation and cutting-edge technology, Tenstorrent aims to set new standards in the computing landscape.
API Access
Has API
API Access
Has API
Integrations
Amazon Web Services (AWS)
Docker
Google Cloud Platform
Microsoft Azure
Integrations
Amazon Web Services (AWS)
Docker
Google Cloud Platform
Microsoft Azure
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
SquareFactory
Founded
2022
Country
Switzerland
Website
www.squarefactory.io/product/
Vendor Details
Company Name
Tenstorrent
Country
Canada
Website
tenstorrent.com/devcloud/
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization