It is used to innovate, increase profits and drive more revenue, and improve efficiencies across organizations. There are a variety of applications for machine learning given massive volumes of data, company inefficiencies, and competitive pressures.
Sometimes it offers the solution itself and sometimes it underpins a layered solution involving other AI technologies and services, such as natural language processing. Machine learning can help with data transfer projects, reduce inefficiencies in internal systems for IT, finance, marketing, legal, and human resources, or offer a product development team the edge they need to soar above the competition. The possibilities for machine learning extend as far as our clients’ imaginations.
Once we identify machine learning as the best way to solve a particular challenge, we build a model and begin testing, often with a subset of data. Using learning curves we can predict the amount of data we need to acheive the best results. We work with our clients to compile this data and use it to train a production model, then deploy that model with our clients. For department heads looking to reduce the time it takes for redundant tasks, the results are immediate. And our clients who package machine learning into customer-facing products say they can more easily compete with industry giants by offering apps powered by cutting edge technology.
But some companies are particularly sensitive to the improvements this solution affords. In addition to those organizations wading through legacy data or making system changes, some areas where we have seen enormous benefits from machine learning are
In machine learning lingo, “labeled” data means we have the data and we also know the output that is associated with the data. For example, you’re trying to predict house prices based on features like the square footage and neighborhood of a house. You have labeled data in this case if you also have the price…