Preface:

Tiny Machine Learning (Tiny ML)  is practice of deploying machine learning models on resource-constrained devices, such as microcontrollers or Internet of Things (IoT) devices, with limited processing power, memory, and energy consumption.

Benefits:

• Realtime : By bringing machine learning to the edge, TinyML enables real-time processing, reduced latency.
• Operate on resource constraint devices: TinyML models are designed to be lightweight, compact, and energy-efficient, enabling them to operate efficiently on resource-constrained devices.
• Instant Decision: TinyML enables on-device inference, allowing edge devices to make decisions instantly without relying on cloud connectivity or external processing.
• Data transfer contains: TinyML reduces the amount of data transmitted by performing local inference.
• Offline Operation: Some edge devices operate in environments with intermittent or no network connectivity. Operating on device itself support offline.
• Cost Reduction: Deploying machine learning models on resource-constrained edge devices reduces the reliance on expensive cloud infrastructure and continuous data transmission.
• Privacy and Security: Transmitting sensitive data to the cloud for processing raises privacy and security concerns, which is avoided.

Use Cases:

• Telematics and Usage-Based Insurance (UBI): TinyML can be used in telematics devices to collect and analyze data on driving behavior, allowing insurers to assess risk more accurately and offer usage-based insurance policies tailored to individual driving patterns.
• Claims Processing and Fraud Detection: By deploying TinyML models on edge devices, insurers can quickly assess the damage, estimate repair costs, and identify potential fraud in real-time.
• Property Risk Assessment: By analyzing information from IoT sensors and smart home gadgets. Insurance companies may track variables like temperature, humidity, water leaks, and smoke detection in real-time by deploying TinyML models on these sensors. This enables early risk detection, prompt alarms, and proactive risk mitigation steps. Additionally, it may result in more precise underwriting and customised insurance pricing.
• Fraud Detection: Running ML on the device for fraud detection has several advantages over cloud ML, including real-time processing, decreased latency, greater privacy, and less reliance on network access.
• Retail Operations and Inventory Management: TinyML may be used to improve retail operations and inventory management. TinyML models on edge devices allow them to analyze data from sensors and cameras to detect product availability, check stock levels, and track consumer behavior. This allows for more precise demand forecasts, more effective inventory management, and more personalized client experiences.

Difference between IoT and Tiny ML:

TinyML involves deployment of machine learning models on edge devices to enhance efficiency, reduce latency, improve privacy, and enable intelligent functionality. TinyML optimizes and compresses machine learning models to run efficiently on devices with limited resources.

IoT connects physical devices, sensors, and objects to enable data collection, communication, automation, data sharing, and remote control. Sensors, actuators, communication protocols, cloud computing, and data analytics are just a few of the technologies that make up the Internet of Things (IoT). Device communication, data management, and control are made possible by IoT technology.

TinyML focuses on deploying ML models on edge devices for real-time decision-making, while IoT is a broader concept encompassing device connectivity, data sharing, and automation.

Future Growth:

ABI Research, a worldwide technology market advisory predicts that  TinyML market will grow with shipment of the IoT devices from 15.2 million in 2020 to 2.5 billion in 2030. Each of this device enables the use of TinyML. One can imagine the need and opportunity for TinyML.

Conclusion:

TinyML involves optimizing and compressing ML models to run efficiently on these devices, enhancing efficiency, reducing latency, improving privacy, and enabling intelligent applications in various domains such as industrial automation, healthcare, smart homes, agriculture, and more. It empowers edge devices to perform inference and analysis locally, minimizing the need for constant cloud connectivity and enabling intelligent functionality at the edge.