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.
Businesses 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.
Internet-based applications of machine learning are becoming commonplace –events that appear in your Facebook feed, product recommendations made by Amazon, and movie suggestions presented in Netflix – they all make predictions based on data patterns analyzed by machine learning algorithms.
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 Algorithms used for AI and ML = High Quality Training and 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 to 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 perfectly matches 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. In this case study, you will learn more about the business applications of AI and ML and how GenRocket can be used to better enable them with high quality on-demand test data.