Pragma Edge Data Processing Machine Learning process real-time events analytic computations of streaming data. The platform provides Data Extraction, Analysis, Enrichment, Pattern Recognition, Visualization of data. The data can be feeded from applications, devices, sensors, social feeds, systems. Stream Analytics brings both client and server components.
Stream Analytics Clients’ side (Pragma Edge SMART Client) will help extract the data from logs, RDMS (database) to the server-side component.
Machine learning is having a dramatic impact on the way software is designed so that it can keep pace with business change. Machine learning is so dramatic because it helps you use data to drive business rules and logic. How is this different? With traditional software development models, programmers wrote logic based on the current state of the business and then added relevant data. However, business change has become the norm. It is virtually impossible to anticipate what changes will transform a market.
The value of machine learning is that it allows you to continually learn from data and predict the future. This powerful set of algorithms and models are being used across industries to improve processes and gain insights into patterns and anomalies within data.
But machine learning isn’t a solitary endeavor; it’s a team process that requires Data scientists, data engineers, Business analysts, and business leaders to collaborate. The power of machine learning requires a collaboration so the focus is on solving business problems.
Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine-learning model is the output generated when you train your machine-learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.