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:

FINTECH

Fraud prevention

Risk management

Insider threats

Claim settlement

Customer acquisition

MANUFACTURING

Predictive maintenance

Tool life

Optimizing operations

Demand forecasting

Product development

AIRLINES

Dynamic pricing

Flight traffic

Price errors

Fuel efficiency

Flight search analysis

RETAIL

Recommendation

Price optimization

Sentiment analysis

Frauds

Data quality

HEALTHCARE

Personalized medicines

Document processing

Equipment maintenance

Drug delivery optimization

Occupancy estimation

MARKETING

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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