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.
Available	SageMaker Endpoint	Amazon SageMaker Inference Endpoints are a powerful tool to deploy your machine learning models in the cloud and make predictions on new data.

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.

HPC on AWS and Azure

Availability	Service	Usage
Available	AWS ParallelCluster	AWS ParallelCluster is an AWS supported open source cluster management tool that helps you to deploy and manage high performance computing (HPC) clusters in the AWS Cloud.
Available	Azure CycleCloud	Azure CycleCloud is designed to enable enterprise IT organizations to provide secure and flexible cloud HPC and Big Compute environments to their end users.