The present subject matter discloses system 102 and method to enable machine learning based analytics platform. The method may comprise generating a graphical user interface to enable one or more stake holders to generate and manage a model for predictive analysis. The method may further comprise enabling a business user to define the business problem, and generate models to perform predictive analysis. The method may further comprise deploying the model, in distributed environment, over a target platform. The method may further comprise monitoring the model to identify at least one error in the model and re-training the model for performing predictive analysis based on the at least one error, thereby enable machine learning based analytics platform.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system 102 to enable machine learning based analytics platform, wherein the system 102 comprises: a processor 202; a memory 206 coupled with the processor 202, wherein the processor 202 executes a plurality of modules 208 stored in the memory 206, and wherein the plurality of modules 208 comprises: a graphical user interface module configured to enable one or more stake holders to generate and manage a model, wherein the stakeholders include a business user, a data scientist, a system engineer, and a system manager, and wherein the model is configured to solve a business problem defined by the business user; a central data analysis module comprising, a business user module for enabling the business user to define the business problem; a data preparation module for enabling the service manager to configure a data source location based on the business problem, extracting source data from the data source location, and translating the source data based on ontology mapping, define by the system Engineer, to generate a translated data, wherein the transformed data is stored in a distributed storage; a model creation module for enabling the data scientist and system manager to explore and visualize the translated data, transform and standardize the translated data to generate the model, evaluate and calibrate accuracy of the model, and save the model over metadata storage; a model deployment module configured to deploy the model, in distributed environment, over a target platform; a model monitoring module configured to monitor the model and identify at least one error in the model; and a model training module configured to re-train the model, for performing predictive analysis, based on the at least one error, thereby enable machine learning based analytics platform. 2. The system 102 of claim 1, wherein the graphical user interface module is configured to enable the stakeholders to design workflow of the model, wherein the workflow is generated using pick and place code blocks enabled by user interface elements of the graphical user interface module, and wherein the user interface elements include activity bar, property panel, a header, a main menu bar, a designer space, and a design Menu. 3. The system 102 of claim 1, wherein the communication between the graphical user interface module, the business user module, the data preparation module, the model creation module, the model deployment module, the model monitoring module and the model training module is enabled by an Extensible Markup Language (XML) based Analytics Data-Exchange Markup Language (ADML), wherein the ADML is used for building a bootstrap engine, and wherein the bootstrap engine is configured to enable the model creation module 218 for building the model using the source data received from the data source location. 4. The system 102 of claim 1, wherein the source data comprises High Business Impact (HBI) data, wherein the data preparation module is configured to mask the HBI data before extracting the source data from the data source location. 5. The system 102 of claim 1, wherein the model deployment module is further configured to enable a set of micro services based on the model, wherein the set of micro services are enabled to address one or more segments of the business problem. 6. The system 102 of claim 1, wherein the model is a light footprint model, and wherein the light footprint model is configured to operate over an edge device. 7. The system 102 of claim 1, wherein the data source location is selected from RDBMS, NoSQL, Big Data, File systems, and sensor data captured from IOT network. 8. The system 102 of claim 1, wherein the model is a cross-platform PMML Model, and wherein the model is executed in real-time mode and in offline mode. 9. The system 102 of claim 1, the target platform is selected from public cloud server, in-premise cloud server, installed devices Gateway or Edge in field. 10. A method for enabling machine learning based analytics platform, the method comprising: generating, by a processor, a graphical user interface to enable one or more stake holders to generate and manage a model, wherein the stakeholders include a business user, a data scientist, a system engineer, and a system manager, and wherein the model is configured to solve a business problem defined by the business user; enabling, by the processor, the business user to define the business problem, and the service manager to configure a data source location based on the business problem, extracting, by the processor, source data from the data source location, and translating the source data based on ontology mapping, define by the system Engineer, to generate a transformed data, wherein the translated data is stored in a distributed storage; enabling, by the processor, the data scientist and system manager to explore and visualize the translated data, transform and standardize the translated data to generate the model, evaluate and calibrate accuracy of the model, and save the model over a metadata storage; deploying, by the processor, the model, in distributed environment, over a target platform; monitoring, by the processor, the model to identify at least one error in the model; and re-training, by the processor, the model for performing predictive analysis based on the at least one error, thereby enable machine learning based analytics platform. 11. The method of claim 10, wherein the processor enable a graphical user interface for the stakeholders to design workflow of the model, wherein the workflow is generated using pick and place code blocks enabled by user interface elements of the graphical user interface, and wherein the user interface elements include activity bar, property panel, a header, a main menu bar, a designer space, and a design Menu. 12. The method of claim 10, wherein the data source location is selected from RDBMS, NoSQL, Big Data, File systems, and sensor data captured from IOT network. 13. The method of claim 10, wherein the model is a cross-platform PMML Model, and wherein the model is executed in real-time mode and in offline mode. 14. The method of claim 10, the target platform is selected from public cloud server, in-premise cloud server, installed devices Gateway or Edge in field. 15. A non-transitory computer readable medium embodying a program executable in a computing device for enabling machine learning based analytics platform, the program comprising: a program code for enabling the business user to define the business problem, the service manager to configure a data source location based on the business problem, a program code for extracting source data from the data source location, and translating the source data based on ontology mapping, define by the system Engineer, to generate a transformed data, wherein the translated data is stored in a distributed storage; a program code for enabling the data scientist and system manager to explore and visualize the translated data, transform and standardize the translated data to generate the model, evaluate and calibrate accuracy of the model, and save the model over metadata storage; a program code for deploying the model, in distributed environment, over a target platform; a program code for monitoring the model to identify at least one error; and a program code for re-training the model for performing predictive analysis based on the at least one error, thereby enable machine learning based analytics platform.