In data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. (Wikipedia)
Artificial intelligence for IT operations (AIOps) combines sophisticated methods from deep learning, data streaming processing, and domain knowledge to analyse infrastructure data from internal and external sources to automate operations and detect anomalies (unusual system behavior) before they impact the quality of service. Odej Kao, professor at the University of Technology Berlin, gave a keynote presentation about artificial intelligence for IT operations at DevOpsCon Berlin 2021. In data stream processing we frequently struggle to find sufficient amounts of data. On the other hand, in AIOps we have many different sources (e.g., metric, logs, tracing, events, alerts) with several Terabytes of data produced in a typical IT infrastructure per day. We utilize the power of these hidden gems to assist DevOps administrators and jointly with the AI-models improve the availability, security, and the performance of the overall system.
One of the challenges with the pursuit of AI is the mismatch between the science fiction concept of artificial intelligence and the real-world, practical applications of AI. In movies and science fiction novels, AI systems are portrayed as super-intelligent machines that have cognitive capabilities equal to or greater than that of humans. However, the reality is that much of what organizations are implementing today for artificial intelligence are narrow applications of AI. This is in clear contrast to artificial general intelligence (AGI). The limit of our current AI abilities lets organizations implement specific cognitive abilities in narrow domains, such as image recognition, conversational systems, predictive analytics as well as pattern and anomaly detection.
From smart infrastructure grids to bot-authored news reports, algorithms and artificial intelligence capabilities are routinely working behind the scenes in various aspects of our day-to-day lives. COVID-19 only accelerated the adoption of automation across industries and Gartner pegged "smarter, responsible [and] scalable AI" as one of its top 2021 data and analytics tech trends. In this roundup, we've highlighted some of the ways AI is transforming everything from animal conversation efforts to matchmaking in the digital age. The agtech company AppHarvest is using a number of transformative practices to reimagine farming in the 21st century, including AI. The company is tapping computer vision and AI to help its robo-harvester, Virgo, pick ripe produce right from the vine.
In the IT Operations world, nowadays you hear the term AIOps frequently. Gartner defines it as that which, "… combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination." In simple words, AIOps is the application of AI and Automation to IT processes, for faster resolution of issues. Very often, we get all excited about the prospects of using AI and ML on IT event data. However, it's also important to consider that Automation is an equal contributor to any AIOps implementation. The role of AI & ML is used for generating the necessary signal or insight about a problem.
Artificial intelligence (AI) applied to healthcare wasn't on the radar 10 years ago. Outside of academic circles, relatively few people were even aware of the potential of the technology. It seemed like a distant fantasy. Today, though, that's all changed. AI is the new trend in innovation, and everyone in the sector is talking about it.
In this post, we look at how we can automate the detection of anomalies in a manufactured product using Amazon Lookout for Vision. Using Amazon Lookout for Vision, you can notify operators in real time when defects are detected, provide dashboards for monitoring the workload, and get visual insights from the process for business users. Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Defect and anomaly detection during manufacturing processes is a vital step to ensure the quality of the products. The timely detection of faults or defects and taking appropriate actions is important to reduce operational and quality-related costs. According to Aberdeen's research, "Many organizations will have true quality-related costs as high as 15 to 20 percent of sales revenue, in extreme cases some going as high as 40 percent." Manual inspection, either in-line or end-of-line, is a time-consuming and expensive task.
The rapid advancements in the technology of closed circuit television cameras and their underlying infrastructure has led to a sheer number of surveillance cameras being implemented globally, estimated to go beyond 1 billion by the end of the year 2021 . Considering the massive amounts of videos generated in real-time, manual video analysis by human operator becomes inefficient, expensive, and nearly impossible, which in turn makes a great demand for automated and intelligent methods for an efficient video surveillance system. An important task in video surveillance is anomaly detection, which refers to the identification of events that do not conform to the expected behavior. Abnormal events in the general sense have the characteristics of suddenness,in order to be able to understand the abnormal events in the first time, it usually takes a lot of manpower to stare at the monitoring screen for a long time to observe, so It will not only make people tired, but also easily overlook some inconspicuous events. Therefore, the automatic detection and recognition of abnormal events of surveillance video in complex scenes, as the core subject of intelligent video surveillance systems, is receiving more and more attention from researchers.
This workflow shows how to create a simple convolutional network and use it for image classification. This workflow shows an example of how to detect the fonts of letters using a convolutional network. This workflow shows an example of the View of the DL4J Feedforward Leaner nodes. This workflow shows basic concepts of the KNIME Deeplearning4J Integration. This workflow shows how to do anomaly detection of the MNIST dataset using a convolutional network.