Build and Operationalize Machine Learning Models - StreamAnalytix White paper

White Paper

Build and operationalize machine learning models

Seamless training, testing, scoring, and model management with StreamAnalytix

Download this white paper to learn:

  • Advantages and use cases of machine learning (ML) across industries
  • Training and deployment of ML models using StreamAnalytix
  • Model management using StreamAnalytix
  • How to operationalize models using StreamAnalytix

Enterprises use ML for its advantages like high-speed data processing, real-time insights, consistent decision-making, reusability, and extensibility across use cases like:


Fraud prevention

Risk management

Insider threats

Claim settlement

Customer acquisition


Predictive maintenance

Tool life

Optimizing operations

Demand forecasting

Product development


Dynamic pricing

Flight traffic

Price errors

Fuel efficiency

Flight search analysis



Price optimization

Sentiment analysis


Data quality


Personalized medicines

Document processing

Equipment maintenance

Drug delivery optimization

Occupancy estimation


Next best offer

Customer churn

Loyalty program performance

Content personalization

Social media analysis

This paper details how you can build, train, test, score, manage, and operationalize ML models using StreamAnalytix – a self-service ETL and analytics tool. The platform helps enterprises create batch and streaming ETL pipelines using drag-and-drop operators on a visual IDE. It has a wide array of built-in operators for data sources, transformations, ML, and data sinks.

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