Merely a half century or so ago, a semi-metallic contraption able to emulate the human brain in learning entirely new things by processing data would have promptly been hailed as the devil’s instrument, and then probably dismantled. Today, billions of dollars are invested, and some of the world’s most brilliant minds spend their time attempting to help machines get better at learning things on their own. And, though in its nascent stages, artificial intelligence powered by machine learning has started having a very tangible, real impact on the world around us.
From the food you eat to the cars you hail to get to work, and even to the path you have followed through the Internet to access this article, chances are that machine learning-based AI is facilitating your journey. From self-driving cars, language translators, image recognition and fraud detection to IoT, the real-world applications of machine learning are practically limitless.
However, machine learning is not easy. Far from it. Companies that do machine learning in-house find themselves spending huge sums on obtaining and retaining competent resources and infrastructure, and leveraging them properly. What’s more, often the investments involved in creating a full-fledged machine learning team to solve a particular problem far outweigh the benefits. Thus, while this approach makes sense for deep-pocketed, resource-rich companies such as Facebook and Google, in-house teams may not be the best option for many average and small companies.
Amid these practical constraints, options such as Machine Learning as a Service have emerged as a godsend for small and medium enterprises (SMEs) looking to leverage AI for solving pressing business problems.
Let’s explore some of the alternatives to building an in-house machine learning team:
Machine Learning as a Service
Building on the As a Service model, MLaaS offers companies an avenue to harnessing automated machine learning, where all the heavy lifting – including the computation – is taken care of by the service provider. Concerns such as establishing infrastructure for the machine learning process are also made redundant since all the requisite computing takes place within the service provider’s data centers.
Companies can simply plug in their data, and leverage all the turnkey capabilities of the machine learning service provider for wide-ranging business use cases such as risk analytics, marketing, predictive maintenance, fraud detection, manufacturing and supply chain optimization.
Associating with machine learning companies can help SMEs realize positive business outcomes in a tangible manner, by harnessing previously invisible insights for data-driven decision making.
Machine Learning Consulting
Another option on the table for SMEs looking to leverage machine learning and AI but unwilling to invest in an in-house team and associated infrastructure is machine learning consulting.
For instance, you might want to know what your existing and potential customers are talking about on social media. Or, you might be looking at deploying predictive analytics to discover consumer insights, reduce attrition, or improve your product recommendation feature. If it’s a single use case that does not warrant building an internal team, then machine learning consulting firms could be your best bet.
Machine learning companies typically start off by engaging with clients to understand their problem statement. Once the problem formulation is clear, the next step is to determine how machine learning and AI can help. Then begins the task of collecting relevant historical data, which if you already have will make the machine learning consulting company’s job that much easier. Otherwise, they will attempt to source and aggregate it for you.
Once both the data and the problem statement are there, multiple experiments are run in parallel to create a machine learning model. Assuming that its prediction capabilities are on-point, the model can then be packed into a full stack application with a user-friendly interface.
We have already seen plenty of examples wherein industry upstarts such as Quicken Loans, an emerging U.S. mortgage lender, have used machine learning, AI and cloud technology to disrupt the marketplace. The rise of MLaaS and consulting services has automated machine learning and put the technology within the reach of small and medium businesses, thereby enabling them to drive innovation and compete on par with industry leaders.
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