Scalable systems are designed to be capable of handling an influx of customers, demand, data, and/or web traffic. All systems need to be built with scalability in mind because businesses often hit problems with scale at the most critical moments. Take for instance Amazon’s server overload on Prime Day 2018. Their server crash, caused by unanticipated extremely high volumes of traffic, cost the company an estimated $70 million USD in sales. As seen in this case, not having scalable technology can impact the bottom line, negatively impact a customer’s experience, and slow or halt a company’s operation.
Machine learning and artificial intelligence are the latest trends to gain significant traction in the tech sphere. These types of algorithms are incredible in their power to provide insights, but they are not inherently scalable. In fact, most are trained on specific datasets for a specific use case, limiting their function for different uses. As more businesses collect data, opportunities for algorithms continue to increase – but only if they are built to scale.
When algorithms are deployed using a serverless architecture, they can effectively be scaled. Serverless architecture is a cloud-based execution model where the cloud acts as the server, dynamically managing machine resources and removing the need for management from the developer or operator. Within this framework, applications are typically broken up into microservices so each piece can be turned on and scaled individually as needed. Thus, serverless architecture allows for a large number of instances of an algorithm to be run without slowing or stopping other actions being processed through the cloud. As the number of requests increases, the server responds automatically to maintain both speed and reliability.
One Algorithm, Many Applications
Mass customisation refers to the process of managing multiple instances of an algorithm that have been trained on a specific dataset. For example, a single algorithm might be trained on different demand drivers, different store locations, or different configurations of a machine — simply by feeding it a unique data set. The question becomes how to maintain all of those different instances and manage which one is called at what point in time. A good mass customization scheme automatically registers, catalogs, and tracks algorithm instances to ensure that each unique dataset is handled appropriately. This process allows for many customised algorithms to be deployed in a scalable fashion without having to do manual work of creating and maintaining separate algorithms.
As more data is captured and analysed, it is critical for machine learning algorithms to monitor their output and match it with actual values to determine their own accuracy. When the accuracy is too low, the algorithm needs to be automatically retrained and/or warn the user of a potential issue. Algorithms lacking this capability must be manually re-trained and monitored by humans in order to be useful. With automatic retraining and monitoring, many highly accurate algorithms can be deployed without needing to hire many people to monitor and manage those algorithms, allowing the end-user to focus on other critical areas in their operation.
In some cases, plans for scalability are initially glossed over and only addressed when it becomes a pertinent issue, for instance, a server overload at Amazon. When supported by scalable technology, employees are given the resources and attention to make growth more predictable and certain. With algorithms built to scale, forecasts can become more accurate and machine downtime can be minimised, both of which lower volatility and increase value for the user.
Widget Brain and our partners can help companies scale their operations with our algorithms as a service. We provide the platform The Algorithm Factory and the algorithms to make machines smarter and more efficient with plugins easy to introduce to your current systems.
Want to know more about the scalable possibilities for your business and our intelligent algorithms? Contact us today atwww.widgetbrain.com/demo.
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