Deep Learning Anomaly Detection Gets Big Boost with Imagimob AI’s tinyML Platform
Anomaly detection or outlier detection is a formidable task in data identification and analysis processes. There are different mechanisms applied for the detection and mitigation of outliers using Artificial Intelligence (AI). One such application involves the use of deep learning (DL) using tiny Machine Learning. Today, an AI startup Imagimob announced their tinyML platform will be used to support the end-to-end development of deep learning anomaly detection.
Let’s understand the basics of outlier detection or anomaly detection and how DL is used in this process.
What’s an Anomaly or an Outlier?
Outliers have always existed in every statistical observation used to build machine learning algorithms. However, the definition of anomalies kept evolving and these got priority only recently with the rapid innovations of machine learning platforms that use Big Data. According to the industry definitions, an anomaly can be statistically defined in a data set as an ‘observation’ that deviates or falls far away from the accepted mean or median. An outlier in a data science project could be referred to as a rarity or an abnormal event whose deviating value could not conform with the accepted or normal behavior or expected trends. Anomaly detection, therefore, refers to the data-centric activity of identifying and deciphering abnormal patterns that don’t adhere to the norms as compared with previous or existing analyses. Outlier detection, in most cases, takes too much time and resources. And, sometimes, it’s impossible to identify an anomaly with a cent percent accuracy.
Where Does Deep Learning Anomaly Detection Gets Positioned?
Anomaly detection is a major activity undertaken during business analysis processes involving enterprise data or Big Data. In modern cases studies involving business operations, a large number of anomaly detection tasks are carried out in IT Networking, Security analysis, Risk assessment, Fraud analysis, medical data exploration, Cloud data ingestion, transformation and integration, and financial data management. Top industries that use anomaly detection are banking and finance, manufacturing, industrial robotics, social media intelligence, sales force predictions and quality control. Newer markets such as space exploration, blockchain, NFTs and Metaverse are also using anomaly detection in some way or the other to improve overall effectiveness of their business results. In 2022, this has become the central figure in any Big data and business intelligence activity involving AI and machine learning engineering operations.
What’s Imagimob AI Doing for Anomaly Detection?
The role of machine learning in modern enterprise data management remains unchallenged. Any data science team considers the deployment of MLops as a strong point, especially in building advanced deep learning techniques for anomaly detection.
is that it delivers high performance as well as eliminates the need for feature engineering, thus saving costs and
Deep learning anomaly detection has its own sets of advantages and scope in data science.
Not only is deep learning anomaly detection better for eliminating the need for feature engineering but it can also leverage and deliver excellent performance on the new generation of powerful neural network processors that is now hitting the market. This means that when going to the edge customers can make the most of their hardware.
Feature engineering, in simple terms, is the act of converting raw observations into desired features using statistical or mathematical functions. Feature engineering normally requires domain expertise and is in general very time-consuming.
With the added support for autoencoder networks in Imagimob AI, developers can now build anomaly detection in less time, and with better performance. Customers will be able to reduce development costs and shorten the time to market.
The anomaly detection solution from Imagimob has been tested and verified on the real-world machine and sensor data.
What’s new in the latest Imagimob AI release
New anomaly detection features
- End-to-end training and deployment of convolutional autoencoder networks for anomaly detection/predictive maintenance
- Anomaly detection starter-project for rotating machinery to get developers up and running in minutes
- Support for quantization of models in the graphical user interface. This includes quantized models, reducing model size, and decreasing inference time on MCUs without an FPU
- Improved model prediction – tracking of how models perform with millisecond resolution, before deploying given different confidence thresholds
- Faster training and model evaluation
- Increased support for large data sets
- Starter project for Renesas RA2L1 – Capacitive Touch Sensing Unit
- In total 8 starter projects, supporting sensors and MCU’s from Texas Instruments, Renesas, STMicroelectronics, Acconeer and Nordic Semiconductors
Deep Learning Anomaly Detection for IoTs and Sensors
Sensors. It’s funny, they measure all sorts of things without really understanding what they read.
What if we could make them intelligent?
What if we could make them feel when something is wrong?
An intelligent motion sensor would pick up the vibrations from a machine and directly know if there is a problem. An intelligent factory cell would analyze machine signals and warn an operator when something is out of the ordinary.
Now, imagine we’re adding machine learning inside the sensors or machines themselves. With the intelligence inside, data transfer costs would no longer be an issue. Neither would downtime or data integrity.
With the latest release of Imagimob AI, companies can develop such applications in-house.