Machine Learning in IoT: Advancements and Applications

by Jose Luis AmorosMay 30, 2023#MachineLearning, #HomePage, #IoT

Table of Content

Machine Learning in IoT Helps to Discover and Secure Devices
Other IoT Use Cases Benefiting from Machine Learning
Building Machine Learning Features on IoT Edge Devices

IoT is evolving and making impactful changes in virtually all industries by improving processes and products.

IoT devices and data transmissions are growing exponentially, creating many opportunities and challenges for enterprises.

IoT devices generate data in real time that is pushed to the cloud and can be used effectively with machine learning to generate intelligence. Machine learning in IoT is about working with large datasets to create accurate machine learning models trained to analyze and interpret data generated by IoT devices.

Machine Learning in IoT Helps to Discover and Secure Devices

IoT systems usually incorporate many IoT devices with different hardware, software, operating systems, and functions that must interoperate, lack self-protection, and have long-life cycles— creating a large network attack surface. These aspects create challenges in device management, visibility, monitoring, scalability, and security.

IoT devices are usually designed with fixed functionalities that provide data patterns suitable for training machine learning models and implement specific machine learning features that use data insights to trigger responses and alerts.

Machine learning for IoT can be applied to create models that learn from the device type (ID), specific instances, and behavior patterns for identifying and securing devices, detecting abnormal behavior, quarantining devices, improving perimeter defense, and preventing threats.

Machine learning for IoT can also provide a method for authenticating devices and improving user experiences by protecting devices with other methods besides signature-based methods.

Other IoT Use Cases Benefiting from Machine Learning

Machine learning can be used in many other IoT applications as well, such as predictive maintenance, smart homes, IoT supply chain, energy usage optimization, air quality analysis, temperature, task automation, etc.

Building Machine Learning Features on IoT Edge Devices

You can create machine learning capabilities in IoT edge devices using TensorFlow Lite—an open-source set of tools and libraries with models for many domains (including object detection, image classification, pose estimation, video, and audio classification).

IoT engineers can start a project using pre-trained machine learning models to deploy on embedded devices and extend or customize models according to the specific domain.

At Krasamo, we have worked with embedded software and created machine learning software for more than a decade, giving us the expertise to support projects for new partners.

About Us: Krasamo is a mobile-first Machine Learning and consulting company focused on the Internet-of-Things and Digital Transformation.

Click here to learn more about our machine learning services.

Feature Engineering for Machine Learning

Feature engineering is a crucial aspect when it comes to designing machine learning models, and it plays a big role in creating top-notch AI systems. Features are attributes that represent the problem of the machine learning use case and contribute to the model’s prediction. The process of feature engineering involves creating relevant and useful features from raw data combined with existing features, adding more variables and signals to improve the model’s accuracy and performance. It starts manually and can be accelerated by adding automated feature engineering tools and techniques. Follow the steps of feature engineering to optimize your machine learning models and create innovative products. 

DataOps: Cutting-Edge Analytics for AI Solutions

DataOps is an essential practice for organizations that seek to implement AI solutions and create competitive advantages. It involves communication, integration, and automation of data operations processes to deliver high-quality data analytics for decision-making and market insights. The pipeline process, version control of source code, environment isolation, replicable procedures, and data testing are critical components of DataOps. Using the right tools and methodologies, such as Apache Airflow Orchestration, GIT, Jenkins, and programmable platforms like Google Cloud Big Query and AWS, businesses can streamline data engineering tasks and create value from their data. Krasamo’s DataOps team can help operationalize data for your organization.