The AI Studio has been developed to significantly reduce edge AI application deployment complexity, time, and cost by breaking the barriers within existing application development and machine learning operations (MLOps) infrastructure that can hinder edge AI deployments.
Eliminating the complexities of integrating disparate tools and workflows, along with the introduction of multiple ease-of-use and intelligence features, AI Studio reduces to a matter of days the time required to go from models to deployed production applications.
“AI Studio is open and highly optimised for the AI development landscape that exists across heterogeneous ecosystems at the edge,” added Dmitry Zakharchenko, VP Research & Development, Blaize. “With the AI automation benefits of a truly modern user experience interface, AI Studio serves the unique needs in customers’ edge use cases for ease of application development, deployment, and management, as well as broad usability by both developers and domain expert non-developers.”
The combination of AI Studio innovations in user interface, use of collaborative Marketplaces, end-to-end application development, and operational management have collectively addressed the problems that tend to hinder AI edge ROI. Deployed with the Blaize AI edge computing hardware, AI Studio makes ,'AI more practical and economical for edge use cases where unmet application development and MLOps needs delay the pace of production deployment,' said the company.
The AI Studio code-free visual interface is intuitive for a broad range of skill levels beyond just AI data scientists, which is a scarce and costly resource for many organisations. “Hey Blaize” summons a contextually intelligent assistant with an expert knowledge-driven recommendation system to guide users through the workflow. This enables AI edge app development for wider teams from AI developers to system builders to business domain subject matter experts.
Users can deploy models with one click to plug into any workflow across multiple open standards including ONNX, OpenVX, containers, Python, or GStreamer. Support for these open standards allows AI Studio to deploy to any hardware that fully supports the standards.
Marketplace support allows users to discover models, data and complete applications from anywhere – public or private – and collaborate continuously to build and deploy high-quality AI applications.
AI Studio supports open public models, data marketplaces and repositories, and provides connectivity and infrastructure to host private marketplaces. Users can continually scale proven AI edge models and vertical AI solutions to effectively reuse across enterprises, choosing from hundreds of models.
The AI Studio model development workflow allows users to train and optimise models for specific datasets and use cases, and deploy quickly into multiple formats and packages. AI Studio’s unique Transfer Learning feature quickly retrains imported models for the user’s data and use case. Blaize edge-aware optimisation tool, NetDeploy, automatically optimises the models to the user’s specific accuracy and performance needs. With AI Studio, users can build and customise complete application flows other than neural networks, such as image signal processing, tracking or sensor fusion functions.
As a complete end-to-end platform, AI Studio will help users deploy, manage, monitor and continuously improve their edge AI applications. Built on a cloud-native infrastructure based on microservices, containers and Kubernetes, AI Studio is highly scalable and reliable in production.
AI Studio is available now to qualified early adopter customers, with general availability in Q1 2021.
