"We have used STM32Cube.AI in the past with great success. It has allowed us to implement high-performing AI applications running on low-cost MCUs. Today we are glad to see that this product is further evolving by offering an online interface. This will allow us to evaluate performance of the AI models and choose a proper hardware architecture earlier in the process so we can converge more quickly on the development of AI applications. Overall, we are very happy with the services and support the ST AI team has been providing to us."

"The Model zoo, STM32Cube.AI online interface, and remote benchmarking capabilities on STM32 boards makes it easier for our data scientists with various hardware knowledge to evaluate embeddability of AI models into our products’ microcontrollers. Additionally, being capable of testing our models on several STM32 microcontrollers in a few clicks enables us to consider embedded AI processing at an earlier stage in the design process and to take advantage of it to design advanced features.”

"The STM32Cube.AI Developer Cloud provides an easy way for our data scientists and embedded developers to collaborate and share their knowledge on embedded neural networks, which helps streamline our development process. The benchmarking feature also enables our data scientists to ensure that the models they create will run smoothly on microcontrollers. This allows us to remain competitive and provide the best solutions to our customers."

"Thanks to STM32Cube.AI Developer Cloud, we can confirm in a very short time the validity of our approach to create a product with embedded AI. With the board farm we are able to confirm that our model works on a microcontroller. We are also able to choose the most appropriate STM32 by performing a remote benchmark on different STM32 boards. Overall, this addition to STM32Cube.AI is really welcome and will allow us to make more innovative products in the future."

"The use of the STM32 Model zoo can greatly ease machine-learning (ML) workflow and significantly shorten time to market by providing a collection of pre-trained models for STM32 microcontrollers that can be easily accessed and integrated into a new project, reducing the need for time-consuming training and experimentation."

“We've been using the STM32Cube.AI from its early days and integrated the CLI in our development pipeline. The newest cloud-based REST API, with its Python wrapper/module, is going to dramatically lower the complexity of our CI/CD tooling maintenance. Combined with the exciting Model zoo, this new service is going to save time & empower our developers.”