An anomaly can be defined as the unexpected pattern present in a large data set. And, the process of tracking out these unexpected patterns from the large data set is known as the anomaly detection. Therefore, an ADS system can detect several hundred types of business incidents today, as well as the interactions between the metrics that contribute to the incidents, your knowledge base will grow, and also your ability to detect even more incidents in the future. The idea of developing such a system was the result of long discussions on the client side, focusing on the various challenges and demands that several companies face. They really wanted a defect detection system that would help them deal with some challenges and variable patterns that are difficult to identify for the same, in such conditions the solutions based on machine learning are available to provide an idea of the problem.
The biggest challenge among us was the data anomalies presented in the huge data sets. These anomalies were a great hurdle for many business units in order to draw a meaningful conclusion to form a decision model.
The anomalies presented in the datasets were one of the biggest challenges. To develop a system which can detect these anomalies was another major challenge for us.
It was difficult to develop a system for companies that can face the constant and variable challenges where constant challenges are something that is structured in nature and also the standardization of the process can help us to deal with them.
To develop a system which can uncover important insights in even the most obscure and easily overlooked corners of any data set when it comes data present in digital businesses.
Since the data must be properly disinfected in order to be able to use them in decision-oriented applications. Therefore, our team used the Gaussian Distribution model, which plots the probability values of the timestamp attribute.
Machine learning provides an optimal solution by applying some complex algorithms to detect the outliers in the system in almost real-time data.
Machine Learning solutions allow the business to have the insights of these issues in the real-time and reduce the dependency of the offline/Periodic dashboards.
When business is dealing with some variable challenges and patterns are difficult to identify for the same, in such condition machine learning based solutions are available in order to provide an insight into the problem.
Automated, real-time detection can uncover important insights in even the most obscure and easily overlooked corners of any data set.
ML algorithms have the capability of data cleansing, in-line visualizations, metadata injection, data services and model versioning.
The Discovery Phase- Our business analysts performed a lot of research and brainstorming before the app development process. At the end of the discovery phase the “Who, Why What, When and Where” of the project were thoroughly scrutinized.
The Execution Phase- After gathering all the nuts and bolts to proceed, it was time for project execution. Our designers, developers and quality analysts followed the agile development process for execution and worked side-by-side to achieve the project aims. Our project managers also did regular reviews to check the progress of the project went smoothly and according to the time.
Sprint Base Project Delivery-We took the client’s feedback on each stage of development and applied the feedback in the project. It helped us to meet the client’s expectations in the best possible manner.
The Deployment Phase- Now, the final app was ready to be unleashed on the world after a rigorous development & testing period. We deployed the app in the app store.
01 ADS system allow businesses to have the insights of related issues in the real-time
02 Automate the analysis
03 Reduces dependency over periodic/offline dashboards
04 Provides the insights of complex strategic decision points
05 Redefine the value of Data