Test Data for AI and ML

Artificial Intelligence (AI) and Machine Learning (ML) are two of the hottest buzzwords in the field of Information Technology. AI is behind the growing popularity of the Virtual Digital Assistant (VDA) as popularized by Google Home, Siri, Cortana and Alexa and used by consumers to answer questions and automate everyday tasks. Business are increasingly using VDAs for sales, marketing and customer service applications as well.

ML is a subset of artificial intelligence and is the enabling technology behind the rapidly growing field of predictive analytics. Machine learning uses sophisticated algorithms that allow computers to recognize patterns from current and historical data, learn from those patterns and then make predictions about future outcomes.

Machine Learning is used in a wide variety of business applications including:

  • Recommendations Engine
  • Fraud Detection
  • Personalized Marketing
  • Operational Efficiency
  • Dynamic Pricing
  • Risk Reduction
  • Health Care Applications
  • Insurance Applications
  • Predictive Maintenance

Meeting the Test Data Challenge for AI and ML

When developers and data science practitioners think about new applications for AI, ML and predictive analytics, they often think the bulk of the work will be in the development of the algorithms and how to code them. However, the biggest challenge is often on provisioning the data used to train, validate and test the model for accuracy and robustness. When perfecting a new algorithm for AI and ML applications, it helps to remember this simple rule of thumb:
The Accuracy of AI & ML Algorithms = High Quality Training Data & Test Data at Scale
The greater the volume and variety of training data used, the more accurate and robust the model for predicting future outcomes will be.

The challenge is this: How an testers provision a high volume of high-quality training data without spending an enormous amount of time collecting, labeling, classifying, cleaning, pruning, normalizing, and formatting the data with the help of domain experts who understand the data requirements.

That’s where GenRocket’s ability to generate high-volumes of data based on a predefined data model, data attributes and patterns of data variation is a perfect match for AI and ML application development. Once the domain expert specifies the data requirements, GenRocket’s real-time synthetic test data engine generates controlled and conditioned data at the rate of 10,000 rows per second. This allows developers and testers to create very large datasets on-demand for the separate purposes of training, validating and testing a machine learning application..

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