Machine Learning

Machine Learning

1. Recommendation Systems

   • Recommendation systems are used across a wide range of industries, most notably online shopping sites, and offer significant value to an organization’s current or prospective customers by helping them discover new, relevant offers that are based on their behavior rather than a blanket promotion. This increased relevance and value stems from a deeper knowledge about customers, leading to stronger customer relationships and higher sales for the business.

2. Streaming Analytics

   • Types
     • Edge analytics
       • Data collected within device (i.e., gateway, sensor, on machinery, etc.)
     • In-stream analytics
       • Data collected between the device and the server (i.e., network security logs)
     • Al-rest analytics
       • Data collected at rest (i.e., database of customer information with real-time streaming events)
   • It’s now important to make decisions in real time (or nearreal time) based on predictions of machine learning models. Deploying machine learning models in realtime opens up opportunities to tackle safety issues, security threats, and financial risk immediately. Making these decisions usually involves embedding trained machine learning models into a streaming engine.

3. Deep Learning and Cognitive Computing

   • Deep learning is one of the foundations of cognitive computing, a field in which complex machine learning systems can perform specific, human-like tasks in an intelligent way. It is proving superior to past pattern recognition approaches due to its improved ability to classify, recognize, detect, and describe data. By combining natural language processing, large databases, and machine learning approaches such as deep learning, cognitive computing allows computers to translate problems from natural language into machine logic, query a knowledge base for potential solutions, and use machine learning algorithms to make decisions about those potential solutions