Learn how to use machine learning on smart devices for various purposes and benefits, such as image recognition, speech synthesis, natural language processing, or gesture analysis.

- The biggest challenge is optimizing the model to the point where it can run on hardware with limited capabilities. Our phones are technological marvels but are incomparably weaker than the powerful graphics cards available in data centers. - Size - accuracy trade-off. Small models run faster but have lower accuracy. Larger models provide more accurate predictions but are orders of magnitude slower. - When deploying a model on an edge device, you must consider more unknowns. What if there is no internet? Or the phone's camera gets dirty? It is more difficult to control the conditions in which we run the model.

How can you use Machine Learning on smart devices?

Machine learning is the process of teaching computers to learn from data and make predictions or decisions without explicit programming. It is a powerful tool that can enhance the capabilities and performance of smart devices, such as smartphones, tablets, wearables, smart speakers, smart cameras, and smart home systems. In this article, you will learn how you can use machine learning on smart devices for various purposes and benefits.

1 What is Machine Learning on Smart Devices?

Machine learning on smart devices, also known as edge machine learning or on-device machine learning, is the practice of running machine learning models locally on the device, instead of relying on cloud servers or external sources. This means that the device can process and analyze the data it collects from sensors, cameras, microphones, or user inputs, and deliver results or actions in real time, without sending the data over the internet.

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2 Why Use Machine Learning on Smart Devices?

Using machine learning on smart devices offers several advantages, such as increased privacy and security, faster speed and efficiency, and improved customization and personalization. By keeping the data on the device, you can protect sensitive information from hackers or unauthorized access, as well as control how and when it is shared or deleted. Furthermore, processing the data locally can reduce latency and bandwidth requirements, save battery life and storage space, and enable offline functionality. Additionally, learning from the data on the device can help to tailor applications to your specific needs and preferences, as well as adapt to changes in the environment or user behavior.

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Piotr Skalski

Open Source Lead @ Roboflow | Computer Vision | Vision Language Models
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- Running models on Edge Device helps provide users with greater privacy. You no longer need to send sensitive data to the server. - Using the model locally on the device helps reduce latency. It is essential in features that require an almost immediate response, like text autocompletion. - It is often necessary in cases where the feature is expected to work even offline. A great example is Face ID, which is supposed to work even in airplane mode. - The edge device model deployment can also be a great way to cut costs. If the model runs on a phone or locally in a browser, you don't have to pay for servers that would run these models in the cloud.

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3 How to Use Machine Learning on Smart Devices?

To use machine learning on smart devices, you need to choose a suitable framework or platform, such as TensorFlow Lite or Core ML. Develop and train your machine learning model with tools like Python and Jupyter Notebook. Optimize the model for on-device deployment with techniques like quantization, pruning, compression, or distillation. Then deploy and run the model with libraries, APIs, or SDKs. Finally, test and evaluate the model for metrics like accuracy, speed, memory usage, or power consumption. You can also collect feedback from users to update the model.

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Kelvin Carrington Tichana

Data Analyst & Management Associate @ EdPlus at Arizona State University | IEEE Member| Internet of Things Community, IEEE| Electrical and Electronics Engineer, Data Analyst
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- An example I have seen and practiced is the conversion of ordinary TensorFlow models for computer vision to versions that run on embedded devices such as the Raspberry Pi. Although this strained the computing performance of the pi, my project was functional in object detection. - Edge Impulse has also helped me build more models for embedded systems centred on the Arduino RP2040.

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4 What are Some Examples of Machine Learning on Smart Devices?

Machine learning on smart devices can be used for various applications and domains, such as image recognition and processing, speech recognition and synthesis, natural language processing and understanding, and gesture and motion detection and analysis. For instance, Face ID on an iPhone can be used to authenticate a user, while Siri on an iPad can be used for dictation. Additionally, Smart Reply on Gmail can be used to compose quick responses, while Fitbit can be used to monitor heart rate. Moreover, Google Lens on an Android phone can be used to scan and search for information, while Alexa on an Echo Dot can be used to play music. Furthermore, Google Assistant on a Nest Hub can answer questions, while Nintendo Switch on a TV can be used for gaming.

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5 What are Some Challenges and Limitations of Machine Learning on Smart Devices?

Machine learning on smart devices presents several challenges and limitations, such as hardware and software constraints that can affect the quality and functionality of applications. Additionally, data availability and quality can affect the accuracy and robustness of models, requiring data augmentation, cleaning, or validation. Lastly, ethical and social implications may arise, such as privacy, security, bias, fairness, or accountability. Such implications can impact the trust and acceptance of machine learning applications, requiring transparency, consent, or regulation.

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Piotr Skalski

Open Source Lead @ Roboflow | Computer Vision | Vision Language Models
Report contribution
- The biggest challenge is optimizing the model to the point where it can run on hardware with limited capabilities. Our phones are technological marvels but are incomparably weaker than the powerful graphics cards available in data centers. - Size - accuracy trade-off. Small models run faster but have lower accuracy. Larger models provide more accurate predictions but are orders of magnitude slower. - When deploying a model on an edge device, you must consider more unknowns. What if there is no internet? Or the phone's camera gets dirty? It is more difficult to control the conditions in which we run the model.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you use Machine Learning on smart devices?

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1 What is Machine Learning on Smart Devices?

2 Why Use Machine Learning on Smart Devices?

3 How to Use Machine Learning on Smart Devices?

4 What are Some Examples of Machine Learning on Smart Devices?

5 What are Some Challenges and Limitations of Machine Learning on Smart Devices?

6 Here’s what else to consider

Machine Learning

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How can you use Machine Learning on smart devices?

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1 What is Machine Learning on Smart Devices?

2 Why Use Machine Learning on Smart Devices?

3 How to Use Machine Learning on Smart Devices?

4 What are Some Examples of Machine Learning on Smart Devices?

5 What are Some Challenges and Limitations of Machine Learning on Smart Devices?

6 Here’s what else to consider

Machine Learning

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