Highlights common but avoidable machine learning pitfalls that can derail AI and advanced IoT sensor data analytics
SensiML Corporation, a leading developer of AI tools for building intelligent IoT endpoints, has released a whitepaper entitled “Building Smart IoT Devices with AutoML: A Practical Guide to Zero-Coding Algorithm Design.” This comprehensive overview defines the smart edge AI approach for building IoT sensing applications and explains the many benefits of creating intelligent endpoints. It then discusses automated machine learning workflows and reviews the key stages of the development process from modeling to prototyping. Most importantly, the paper also gives developers new to AI and machine learning practical guidance and examples for implementing designs using AutoML tools.
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AI holds great promise for IoT device developers seeking to build intelligence into IoT embedded sensing products, but until just recently it has remained beyond reach for most development teams. A new generation of AI development tools, such as those offered by SensiML, enable algorithm code to be automatically created by software that learns by example without explicit coding or data science expertise. Furthermore, state of the art tools such as those from SensiML can build sophisticated sensor code capable of running locally on microcontrollers embedded within the IoT devices themselves. This approach enables the developers of smart edge IoT devices to build applications with real-time responsiveness, adaptive smart devices, network efficiency and resiliency, and security and data privacy, without requiring extensive data science and firmware coding expertise. This whitepaper shows how such tools can be used to create smart edge IoT devices.
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What Readers will Learn
The objective of this whitepaper is to teach users how to implement the Smart Edge AI approach when planning, designing, executing and evaluating their smart IoT applications. Specifically, it discusses the various items IoT developers should consider when implementing automated machine learning and provides them with practical advice from sensor selection and data capture to generating local insights. The key sections of the whitepaper include:
- How the Smart Edge AI Approach Works
- The Key Stages of Implementing the Smart Edge AI Process
- Developing Your Application Model
- Prototype IoT Selection
- Sensor Selection and Data Collection
- Data Labeling
- ML Algorithm Development
- Converting an Algorithm to Optimized Endpoint Code
- Test/Validation of Local IoT Device Insight
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