We are witnessing an increasing volume of Machine Learning (ML) use cases surfacing in a variety of industries, reiterating the point made in our earlier blog post Making Sense of Machine Learning. When the mechanism is sound, it is no longer doubtful that it can be implemented, simply the particulars of where, when, how, and to what extent will vary from case to case.
The following examples establish the diversity ML brings to the table across the board for enterprises, large, small, and in between.
E-Mail is a powerful tool of communication, ubiquitous with personal and professional life. However when individual addresses fall in the wrong hands, mayhem can easily occur. E-mail providers have made it their priority to automatically detect if an e-mail is spam and move it to spam folder, filtering or bypassing the need for the user to sort through each discrete junk message. This task is accomplished in an almost flawless manner since the provider is continuously learning words and meta-characteristics about e-mail to determine if a particular file qualifies as spam. Those attributes are gathered periodically from hosts of e-mails in their repository to be able to correctly and automatically classify a message as spam or not, a feat unimaginable a mere two decades ago.
Insurance is a complex and deep industry with different rules governing each state, type of insurance, and ever-changing legal ramifications for both the insurers and customers. The financial burden and/or gain for insurance carriers rest on processing claims, categorizing their validity, deeming them as legitimate or fraudulent, and carrying out the proper measure of rejection or payment. Machine learning fundamentally eases this process as new claims are filed everyday and insurers can eliminate the dead time that used to be incurred as claims were pending for a long time. Insurers can now utilize the research assembled on the merits or de-merits of each claim and quickly determine claims in an instant.
Manufacturing and Supply Chain systems are among the most tightly run corporations due to the tangible feature of managing several moving parts at once. Such firms are now able to accurately detect and predict component failures early on and prevent major outages. Although such establishments have largely relied on software to guide their orders and results in the past, the degree to which they could forecast failure and avoid disaster has multiplied significantly because of ML. This ability not only saves them time and money, it also serves as a catalyst to engender goodwill based on what ML has produced for them, an irreplaceable advantage.
Image Recognition systems can now precisely recognize key contents of an image, including distinctive identity. An image can be scanned in seconds and ML will tell you whether: it is a person, or object, or a building. If it is a person, ML then clarifies the emotional state of the person, and if the person is a celebrity, their famous name, and defining traits.
The list can go on and on. ML has the power to change things effusively, and that change is on the horizon. In the next blog post, let’s explore the positive reach and potential benefits Machine Learning can bring to IT Operations.
