Key machine learning trends that will rock your 2018
If there’s one tech term that takes the crown for being on the lips of everyone associated with the industry, it has to be “machine learning.” Abbreviated ML, machine learning has impacted almost every industry in one way or another. Everything, from unmanned diagnostics equipment that detects tumors and cancers to your favorite music streaming apps, uses machine learning now. Venture capitalists have invested millions of dollars in startups with a focus on machine learning. That’s not all: ML is in the budget plans of all enterprises that haven’t already started their machine learning journeys. Tech giants with platform businesses are advancing their ML capabilities to the next level. It’s fascinating that machine learning (which is actually one approach to achieving artificial intelligence in computer programs) is already a multibillion-dollar industry. Trends and predictions made about the industry back in 2016-17 have turned into realities. Now is time for every IT leader to recognize and track the machine learning trends that will keep this technology hot throughout 2018.
Edge computing mimics public cloud by providing compatible services and endpoints that applications can use. Machine learning applications depend a great deal on the ability of the program to execute complex data analytics operations quickly. Often, for low-latency applications such as in unmanned aerial vehicles, ML applications are prone to failure because of the lag caused by the need to carry out complex analytics in the cloud. Edge computing emerges as a solution. Particularly in infrastructures that can’t accommodate VMs and containers, edge computing offers a practical solution. The edge computing layer, here, connects developers with compute, store, and networking services. In 2018, this kind of serverless computing will deliver a lot of convenience to developers by reducing the overhead efforts of deploying code.
In most enterprises, IT system components are generating massive data (log files, status reports, error logs, and whatnot). Hardware components, software components, server applications, and operating system — there’s operational data being produced everywhere. By taking all this data into the purview of machine learning, enterprise IT can become proactive instead of reactive.
Watch out for updates about Amazon Mackie, an AI-powered operations management platform for IT. Azure Log Analytics is another of the machine learning trends to remember from this space.
Though chatbots are already a business force, 2018 is going to be a crucial year in terms of how machine learning technologies become accessible and relevant for end users in business settings. Personal virtual assistant applications are next in order. These apps will be able to connect with a user’s application usage information stored across databases. It will then detect patterns to prepare personalized usage experiences, such as lists of favorite screens, the anticipation of next actions, and easy access to knowledge base documents.
