Edge Native AI for Flexibility in Developing and Designing Smart Systems

By Saumitra Jagdale

Freelance Technology Writer

December 09, 2021


Edge Native AI for Flexibility in Developing and Designing Smart Systems

The progress in the field of OEM products comes at the cost of increasing complexities in the design and architecture of the systems. As the most trending feature contributing to this type of progress, Edge AI brings out the issue of compatible platforms for such dynamic development and designing. 

As the systems should be flexible enough to accommodate a wide range of core packages, not many platforms allow such flexibility and ease for such dynamic development and design. Although hardware cloud development platforms provide advanced features for scaling up and expanding the capabilities of a use case, many devices lack the sync SDK support for such platforms.

Retorting the above concerns, MicroAI’s launchpad is an edge native solution with fllexibility, allowing the connection of numerous devices and optimizing the complexities for building target systems. It assists many clients by easing and fast-tracking the design, development, testing, and deployment of embedded devices that run on MicroAI software. 

Talking more about the target systems, the platform helps optimize the equipment performance by providing a dashboard visualization of health scores, maintenance insights, and security status. Although maintenance is part of the regular routine of running a system, AI-enable predictive maintenance can significantly reduce the risk to the equipment of a target system.

Structure and Architecture of MicroAI AtomML

Credits: MicroAI

Developing system-specific edge AI models and working on integration issues with hardware, along with support and debugging, makes the whole development cycle tedious and intensive. MicroAI’s AtomML features an AI engine versatile enough to ingest time series data for optimal dataset training. 

“It moves the training and inferencing directly to the embedded device. Launchpad then simplifies and reduces the time and cost to integrate the MCU and MPUs into an edge device, which can be tested and scaled to POCs for mass deployment.”

MicroAI fits a wide range of industries by bringing out the AI features from the target components. This includes the automotive, energy, telecommunications, and semiconductor industries. Recently, MicroAI extended its AI training support to Rensas MCUs

MicroAI launchpad offers multiple functionalities for design engineers in the form of customizable dashboards and features, including account creation, authentication, mobile SIM, or LoRaWAN connectivity activation. It also provides credit card billing for global SIM connectivity and flexible connectivity to MicroAI AtomML software libraries.

Credits: MicroAI

MicroAI offers a range of software development kits for engineers to configure the applications. It also supports the maker community with the open-source SDK for ESP32 MCUs and Raspberry Pi SBCs. You can fill this form on MicroAI’s website to get the email of the Github repository links of the open-source SDKs. 

Along with the flexible SDK support, MicroAI’s Edge-native AI software solution delivers advanced capabilities in the areas of data ingestion and modeling, workflow automation, and dashboarding. You can visit the product page of MicroAI Launchpad for more information.

Saumitra Jagdale is a Backend Developer, Freelance Technical Author, Global AI Ambassador (SwissCognitive), Open-source Contributor in Python projects, Leader of Tensorflow Community India, and Passionate AI/ML Enthusiast.

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