Goto

Collaborating Authors

Anomaly Detection - Another Challenge for Artificial Intelligence

#artificialintelligence

It is true that the Industrial Internet of Things will change the world someday. So far, it is the abundance of data that makes the world spin faster. Piled in sometimes unmanageable datasets, big data turned from the Holy Grail into a problem pushing businesses and organizations to make faster decisions in real-time. One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Thus, anomaly detection, a technology that relies on Artificial Intelligence to identify abnormal behavior within the pool of collected data, has become one of the main objectives of the Industrial IoT.


Anomaly Detection, A Key Task for AI and Machine Learning, Explained - KDnuggets

#artificialintelligence

It is true that the Industrial Internet of Things will change the world someday. So far, it is the abundance of data that makes the world spin faster. Piled in sometimes unmanageable datasets, big data turned from the Holy Grail into a problem pushing businesses and organizations to make faster decisions in real-time. One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Thus, anomaly detection, a technology that relies on Artificial Intelligence to identify abnormal behavior within the pool of collected data, has become one of the main objectives of the Industrial IoT.


Why Real-Time, AI-Based Anomaly Detection Is a No-Brainer - DZone AI

#artificialintelligence

In the earliest days of big data, collection was the top priority. Business leaders needed to find innovative ways to collect as much information about customers and operations as possible. Now that this goal has been accomplished, a new problem has arisen. There is enough data available to optimize user experience, network performance, business operations, and more, however, between 60 and 73 percent of that data never gets put to good use. There is an overwhelming amount of different metrics and systems to track, making it increasingly difficult to evaluate business patterns and, more importantly, deviations.


How Machine Learning Detects Anomalies in Healthcare

#artificialintelligence

The digital revolution has changed the healthcare landscape irrevocably. Patients expect faster, more efficient care that costs less, which is where artificial intelligence (AI) can help. AI and machine learning allow healthcare organizations to evolve and keep up with trends and new methodologies. Data science enables systems to ingest massive quantities of information quickly, to generate insights and predictions that allow healthcare organizations to focus human attention on what's really important: providing quality care. One of the techniques that are essential for data teams, physicians, insurance analysts, etc., in healthcare to understand is anomaly detection.


The Growing Role of Machine Learning in Monitoring - DZone AI

#artificialintelligence

When does a buzzword stop being a buzzword? In the world of IT and software development, we are all too used to having terms and concepts thrown around left, right, and center. At some point, though, widespread adoption of the technology and platforms behind these buzzwords turns them into best practices and realities in the field. While the adoption of machine learning in DevOps is relatively slow compared to other industries, the potential is huge. To start understanding what has to gain from this rapidly developing field, one needs only to look at the world of monitoring and log analysis, where machine learning can be used to alleviate some of the main pain points experienced by DevOps teams -- namely, the analysis of vast volumes of data and the extraction of actionable insights from this data.