Embedded Machine Learning: From Data to Deployment

Embedded machine learning is all about bringing AI to the edge, where data is collected and processed in real-time. This approach is particularly useful for applications that require low latency and high reliability.

The first step in embedded machine learning is collecting and preparing data, which is often done using sensors and other data-gathering devices. According to a recent study, the average IoT device generates over 1.4 megabytes of data per day.

Having a robust data collection and processing pipeline is crucial for training accurate machine learning models. By leveraging edge computing, you can reduce the latency associated with sending data to the cloud and waiting for a response.

Embedded machine learning can be applied to a wide range of industries, from smart homes and cities to industrial automation and healthcare. In fact, a recent survey found that 70% of companies are already using or planning to use edge AI in their operations.

Embedded machine learning, or TinyML, is the field of machine learning applied to small devices like microcontrollers.

Recent advances in technology have made it possible for even the smallest devices to run sophisticated machine learning workloads.

Embedded ML can extract meaningful information from data that would otherwise be inaccessible due to bandwidth constraints.

This is a major advantage of deploying ML on edge devices.

On-device ML models can respond in real-time to inputs, enabling applications like autonomous vehicles.

These vehicles can't be viable if they're dependent on network latency.

By processing data on-device, embedded ML systems avoid the costs of transmitting data over a network and processing it in the cloud.

This is another key advantage of embedded ML, known as the Economics of BLERP.

Systems controlled by on-device models are inherently more reliable than those which depend on a connection to the cloud.

This is because on-device models don't rely on a network connection, making them less prone to errors.

When data is processed on an embedded system and is never transmitted to the cloud, user privacy is protected and there is less chance of abuse.

Data collection is a crucial step in embedded machine learning. You can start with preexisting datasets, which can be found in collections or Edge Impulse projects.

To get started with data collection, you can use Edge Impulse projects, which can be cloned to your account or downloaded from the Dashboard.

Here's a list of resources to help you get started with data collection:

Note that some of these resources are attributed to [3] and [1], indicating that they are part of a larger collection or project.

Data Collection is a crucial step in the data science process. It involves gathering relevant data from various sources to support your project or research.

You can start by exploring preexisting datasets and projects, which can be found on platforms like Edge Impulse. These resources can give you a head start and save you time.

Edge Impulse offers a collection of preexisting datasets, projects, and curation tools to help you get started. With a public Edge Impulse project, you can clone the project to your account and/or download the dataset from the Dashboard.

To collect data effectively, you need to understand the basics of data engineering. Introduction to data engineering is covered in section 3.2.1, which provides a brief overview of the topic.

Data engineering is a crucial aspect of data collection, and it's essential to understand what it entails. According to section 3.2.2, data engineering is the process of designing, building, and maintaining the infrastructure that supports the collection, processing, and storage of data.

Using existing datasets is a great way to get started with data collection. Section 3.2.3 explains how to use existing datasets, which can save you time and effort.

Responsible data collection is also an important aspect of data collection. Section 3.2.4 emphasizes the importance of responsible data collection, which involves ensuring that the data is collected in a way that respects individuals' privacy and rights.

If you're new to data collection, it's essential to get started with the right tools and resources. Section 3.2.5 provides a step-by-step guide on getting started with Edge Impulse, which includes videos and slides to help you learn.

Data collection with Edge Impulse is also covered in section 3.2.6, which provides additional resources and guidance on how to collect data using this platform.

Here is a summary of the resources mentioned in this section:

In a lab setting, data is often collected through manual entry or automated tools. This data can come from various sources, including sensors, devices, and databases.

Manual entry is a common method, where data is collected and recorded by hand. This can be time-consuming and prone to errors.

Automated tools, on the other hand, can collect data quickly and accurately, but may require more setup and maintenance. For example, a data logging device can collect sensor readings at regular intervals.

Data from various sources can be combined and formatted into a single dataset, making it easier to analyze and visualize. This is often done using data integration tools.

Data cleaning and quality checks are also essential steps in the lab, to ensure that the data is accurate and reliable.

Worth a look: Automated Machine Learning with Microsoft Azure Pdf Free Download

Model Training and Evaluation is a crucial step in embedded machine learning. It's where you take the data you've collected and use it to train a model that can make predictions or decisions on its own.

Related reading: Action Model Learning

Feature extraction from motion data is a key part of this process. According to Section 3.3.1, this involves extracting relevant information from the data you've collected.

Feature selection in Edge Impulse is another important step. As Section 3.3.2 explains, this involves selecting the most relevant features from the data to use in your model.

A machine learning pipeline is like a recipe for your model. It outlines the steps you need to take to train your model, from data preparation to model evaluation. Section 3.3.3 provides more information on how to set up a machine learning pipeline.

Model training in Edge Impulse involves using the data you've collected to train a model that can make predictions or decisions. Section 3.3.4 explains the process in more detail.

Evaluating your model is just as important as training it. Section 3.3.5 provides tips on how to evaluate your model and determine whether it's working as intended.

Underfitting and overfitting are two common issues that can occur during model training. According to Section 3.3.6, underfitting occurs when your model is too simple and can't capture the patterns in the data, while overfitting occurs when your model is too complex and fits the noise in the data rather than the underlying patterns.

Here's a quick summary of the steps involved in model training and evaluation: