Recommendation engine industry using machine learning and Google Compute Engine
Personalized services and discovery are progressively at the center of our digital experiences henceforth enriched in real-time by vast amount of smart services, technologies and devices.
This momentum is streamlined
by emerging technologies like machine learning algorithms behind recommendation
engines. For those who are unfamiliar, Recommendation engines are the technology behind content discovery networks and the suggestion features of most ecommerce websites. With Recommendation engines, you can deliver relevant items at the right time and on the right page. So, adding that intelligence into your app or services, makes your application more attractive, enhances the customer experience and increases their satisfaction.
Once can also recall that, there are various components to a recommendation engine, ranging from data ingestion and analytics to machine learning algorithms. The scalability (highly compute-intensive workloads) is indispensable to provide relevant recommendations.
On Google Cloud Platform you can harness in real-time the following features and technologies:
- Multi-tenant, redundant datacenters
- Custom instance types
- Global network
- Years of experience in software infrastructure innovation, such as MapReduce, BigTable, Dremel, Flume, and Spanner.
