DeepSense Documentation
Welcome to the DeepSense technical cloud documentation wiki. This is the primary source for users with questions on the DeepSense cloud equipment and services. DeepSense uses cloud services from various cloud vendors for the development of AI projects. You'll now find all of our content on the sidebar. Just below you can see the available cloud services, and information about any planned outages we may have. Due to the nature of "unlimited resources" of cloud computing, DeepSense doesn't limit any cloud services that the projects need. The cloud services listed in the following tables are our currently running resources. This doesn't necessarily indicate we cannot use other cloud resources. DeepSense users are encouraged to contact us to apply for required cloud computing services. We continuously explore the best cloud solutions to the AI projects. We don't lock our solutions in any specific cloud vendors. The tables below show the cloud services we have tested and are developing projects on. More cloud services will be coming soon.
We routinely make changes and update the content. If you see anything missing, or have any suggestions for content, we would appreciate hearing from you. You can send us an email at (info@deepsense.ca). You can click on "Resources" on the navigation panel to find the technical details of the virtual machines and serverless computing services.
DeepSense Cloud Computing Services
Amazon Web Services (AWS)
| Availability | Service | Usage |
| Available | S3 (Simple Storage Service) | Amazon S3 can be used to store and retrieve any amount of data. Mainly use it for long term data storage or backing up your data. |
| Available | EC2 (Elastic Compute Cloud) | Amazon EC2 can be used to create virtual machines for training models in a manually configured environment. |
| Available | SageMaker Notebook | Amazon SageMaker notebook instance is a machine learning (ML) compute instance that runs the Jupyter Notebook App. |
| Available | SageMaker Studio - AutoML | Amazon SageMaker Studio provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps. |
Microsoft Azure
| Availability | Service | Usage |
| Available | Blob Storage | Azure Blob Storage is a store for objects capable of storing large amounts of unstructured data. Can be used for long term storage or backing up your data. |
| Available | Virtual Machine | Azure Virtual Machines are image service instances that provide on-demand and scalable computing resources for training models in a manually configured environment. |
| Available | Machine Learning Workspace | Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project lifecycle. |
Google Cloud Platform (GCP)
| Availability | Service | Usage |
| Available | Cloud Storage | Cloud Storage is a service for storing your objects in Google Cloud. Mainly use it for long term storage or backing up your data. |
| Available | Compute Engine |
Compute Engine is a customizable compute service that lets you create and run virtual machines for training models in a manually configured environment. |
| Available Soon | Vertex AI Notebooks | Vertex AI Workbench managed notebooks instances are Google-managed end-to-end Jupyter notebook-based environment. |
