Machine Learning as service
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This document summarizes the typical layers of a machine learning application: the store layer collects and stores data from various sources in a big data store; the model layer uses machine learning algorithms to analyze the data, develop models for prediction, and test the models; the service layer exposes the models as APIs for external applications to consume and make predictions with new data.
Machine Learning as service
- 1. MLF( x, y) H Feature Samples Machine Learning As a Service ( MLAS)
- 2. APIs Classification KNN … ANN DCT Regression Linear ANN Decisi on tree Rando m Ensemble .. … .. .. Layer Architecture for Machine Learning Application Logs Store Layer Model Layer Consumer Apps Events Audits Reference data External Historical Data Trainingdata model Testingdata model Pre- train model ExploratoryAnalysisandVisualization Service Layer Web Dashboard Desktop others Algorithm tweaking Data tweaking Typically Machine learning application is consist of SMS layers - Store , Model and Service Layer as illustrated in below diagram
- 3. Store Layer This layer is a pivotal part of the layer architecture. It collect the data from different applications / system / devices / sensors in clustered environment. Each node has data lake for specific business aspect. The detailed is illustrated in the diagram -1. This layer is consist of following key components. Data Generation Enterprise have different source to generate data. It could be business apps, devices used for business purpose , sensors and other systems like - infra structure , communication or other hardware system. They also get data from business partners, franchises or agent that play a vital role in business decisions. Big Data Store Enterprise need to store aforesaid generated data in big store for analytic and machine learning. The data could be structure or unstructured format . Apps Device Sensors Social Sites Data Lake Azure Streaming Azure Data Factory Azure Event hubs CosmosDB Blob SQL Data Warehoues Data Generation Information Management Big Data Store Systems 1
- 4. Model Layer This layer is more about experiment and observation for accurate perdition. Modell is about the preparing training and testing data set with combination of machine learning algorithms to predict future value. There are several possible options for model construct. • Use pre-build model provided by cognitive platform • Develop specific model for particular objective using data science language like Python or R etc. This is most critical and cumbersome activity that helps to identify best fit machine learning algorithm for a business case and keep varying with different use cases. • Or combination of both Data Sets New data Sets Machine Learning Cognitive Services Analytics 1 2
- 5. Service Layer Expose the data model as a service to consume outside. This service is accessible externally from different types of applications to predict model with new data set. Models API Gateway Service Service Service Service Consumer Apps Service 2 Security New Data set
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