Paperspace

Paperspace is a cloud-based platform for machine learning operations (MLOps) that aims to enhance the development and deployment of deep learning models. According to the vendor, Paperspace offers accelerated cloud computing capabilities, simplifying the process of building, training, and deploying AI models with an emphasis on simplicity, speed, and cost-effectiveness.

Key Features

Accelerated cloud computing: According to the vendor, Paperspace provides a platform for accelerated cloud computing, enabling the building and scaling of AI applications. The platform is designed to simplify the development, training, and deployment of AI models, focusing on simplicity, speed, and affordability for developers.

Notebooks: Paperspace offers Notebooks, a fully managed environment for exploring and developing AI models. According to the vendor, Notebooks come preloaded with popular ML frameworks and offer a free GPU plan. Users can quickly launch GPU or IPU-enabled Jupyter Notebooks from their browser, supporting any library or framework and facilitating collaboration and sharing.

Machines: Paperspace's Machines feature provides on-demand access to powerful and cost-effective virtual machines. Users can choose from a wide range of GPU options, including the latest NVIDIA GPUs. The machines are specifically designed for large-scale GPU-accelerated workloads and can be easily scaled up or down as needed. Users have root access and can connect to their virtual machines via SSH, as stated by the vendor.

Deployments: Paperspace's Deployments feature allows users to deploy and scale AI models in the cloud. According to the vendor, models can be deployed as API endpoints, simplifying the process of serving predictions and integrating them into applications. Deployments support various ML frameworks and provide high-performance, low-latency micro-services with a RESTful API. Users can conveniently monitor, scale, and version their deployments.

Workflows (BETA): Paperspace's Workflows feature provides a CI/CD approach for machine learning. According to the vendor, users can automate machine learning tasks and train models directly from pull requests or commits. Workflows aim to enable reproducible, maintainable, and deterministic models without the need for server configuration. It offers seamless integration with Git repositories, streamlining the model development and deployment process.