Enables to onboard trading partners
Data Processing Machine Learning
Process real-time events analytic computations of streaming data.
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.
Data Processing Machine Learning
drive business rules & logic
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.
Data Extraction
SMART Client provides a easy to deploy, data extraction capabilities from different sources of data. Pre build integrations are available to IBM Sterling B2B – MFT platform, Global Scape, Axway, EDIFICES. Any application data can be extracted either from database or log files
Data Analysis
Extracted data can be further analyzed to identify and route the data for further processing. Various business rules can be created to analyze the data and perform pre processing action on the data.
Data Enrichment
Various data enrichment options are available to create golden data set that creates the required business object data. Data enrichment services provides the ability to set correction values or update the data set with name – value Pairs
Pattern Recognition
Different patterns in the data can be detected at real time to send alerts or action before, during or after processing the data. An data processed though the system can be analyzed with advanced pattern reorganization processes that can detect data availability, non availability , pattern changes and send notification based on the set event
Data Visualization
Utilize modern data visualization methods to present the data. Various reports can be build with inbuilt visualization engine that provides the ability to build reports by drag and drop. The built reports can be saved and be used for personal reporting or made public to be leveraged by all the team members
Data Processing Machine Learning Use Cases
This powerful set of algorithms and models are being used across industries to improve processes and gain insights into patterns and anomalies within data.
Fleet Management.
IoT Solution.
Stock analysis and alerts.
Fraud detection.
End to End System Monitoring.
Data Processing Machine Learning - FAQ's
Yes, DPML is customizable product with Exposed APIs to do the job.
Yes, DPML do allow to generate Report of the repository and send it via download, S3, Email, and other.
DPML have migration scripts which are customizable as per need. All the data is needed to be provided in any format which can help to differentiate data. Like JSON, csv, XML. Migration scripts will handle the rest to load the data to DPML.
With traditional software development models, programmers wrote logic based on the current state of the business and then added relevant data.
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