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.
Contact Us Today
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
A Winning Base for Successful Digital Transformations When it comes to developing a successful digital strategy, it is not just corporations planning to maximize the benefits of data assets and technology-focused initiatives. The Government of Western Australia recently unveiled four key priorities for digital reform in its new Digital Strategy for 2021-2025.Explore
Engage Your Workforce with a Modern Employee Intranet Solution The employee intranet has changed significantly since it was first introduced in the early 1990s. What started as HTML-based static portals have now evolved into intuitive communication tools complete with search engines, user profiles, blogs, event planners, and more. Today, many organizations are taking a second look at employee intranets to bridge gaps between teams, build company culture, centralize information, increase productivity, and improve workflow.Explore
Adopting emerging cloud technologies, consolidating resources, and improving processes is the key. “IT no longer just supports corporate operations as it traditionally has but is fully participating in business value delivery. Not only does this shift IT from a back-office role to the front of business, but it also changes the source of funding from an overhead expense that is maintained, monitored, and sometimes cut, to the thing that drives revenue,” said John-David Lovelock, research vice president at Gartner.Explore
Deliver Powerful Insights Instantaneously with Federated Queries - No Matter Where Your Data Resides The concept of federated queries isn’t new. Facebook PrestoDB popularized the idea of distributed structured query language (SQL) query engines in 2013. Over the years, AWS, Google, Microsoft, and many others in the industry have accelerated the adoption of a distributed query engine model within their products. For example, AWS developed Amazon Athena on top of the Presto code base, while Google’s BigQuery is based on Cloud SQL.Explore
What is Unstructured Data? Almost 80% of the data that enterprises and organizations collect is unstructured - data without a set record format or structure. Unstructured data includes data such as emails, web pages, PDFs, documents, customer feedback, in-app reviews, social media, video files, audio files, and images.Explore