Yes, when our partners confront a shared large-scale data problem, Arcalea will seek to qualify it as a machine learning project.
Before we design, test, and deploy an ML model, Arcalea verifies the likely fit, value, and resources needed to bring the tool to production for our partners. ML projects require volumes of unstructured data to process in order to determine rules that predict outputs, and generally follow a recognizable development path. As a result, a practitioner must 1) define the problem, 2) verify the data exists in volume and quality, 3) create a problem statement that points to the type of algorithms and volume of data, training, testing, and optimizing likely required.
A model recently deployed focused on the ability to determine on-page search engine ranking factors within a specific target industry. With sufficient volume of data—site URLs, data scrapings from the sites, and SERP performance—industry brands could identify which page-level ranking factors were statistically probable for sites to rank on page one of Google’s SERP. As a result, brands in the targeted industry could hyper-optimize sites for Google ranking. Because neither contextual datasets nor search engine algorithms are static, the ML exercise must be repeated every 3-4 months to be accurate as variables change.
Learn more about Machine Learning here.