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
For decades, Windows served as the workhorse of the business world. In recent years, however, a significant transformation has occurred with the rise of cloud infrastructure platforms. Enterprises now realize that legacy on-premises Windows workloads are impeding their progress. Core challenges include licensing costs, scalability issues, and reluctance to embrace digital transformation.Explore
Connecting more people to data has become imperative for organizations worldwide. In Top Trends in Data & Analytics for 2022, Gartner stated, “Connections between diverse and distributed data and people create truly impactful insight and innovation. These connections are critical to assisting humans and machines in making quicker, more accurate, trustworthy, and contextualized decisions while considering an increasing number of factors, stakeholders, and data sources.”Explore
Since the dawn of business, users have looked for three main components when it comes to data: Search | Secure| Share. Now let's talk about the evolution of data over the years. It's a story in itself if one pays attention. Back then, applications were created to handle a set of processes/tasks. These processes/tasks, when grouped logically, became a sub-function, a set of sub-functions constituted a function, and a set of functions made up an enterprise. Phase 1 – Data-AwareExplore
Practitioners in the data realm have gone through various acronyms over the years. It all started with "Decision Support Systems" followed by "Data Warehouse", "Data Marts", "Data Lakes", "Data Fabric", and "Data Mesh", amongst storage formats of RDBMS, MPP, Big Data, Blob, Parquet, Iceberg, etc., and data collection, consolidation, and consumption patterns that have evolved with technology.Explore
Enterprises have, over time, invested in a variety of tools, technologies, and methodologies to solve the critical problem of managing enterprise data assets, be it data catalogs, security policies associated with data access, or encryption/decryption of data (in motion and at rest) or identification of PII, PHI, PCI data. As technology has evolved, so have the tools and methodologies to implement the same. However, the issue continues to persist. There are a variety of reasons for the same:Explore
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore