Machine Learning has evolved much from the context of an Artificial Intelligence subset to the status of disruptive technology. However, there is a gap between popular belief in machine learning and what machine learning tools can actually accomplish. Though it is perched top on the Hype Cycle, it is harder for people to see where the practical applications of machine learning lie. So when trying to push to find innovative ways to gain even the smallest of competitive advantages, it is better to familiarize oneself with facts about this technology.

In essence, machine learning provides systems the ability to self-learn and improve from experience without being explicitly programmed. The umbrella term encompasses a variety of different technologies. However, due to fear of missing out on machine learning, everyone wants to be part of this wave even though they are not entirely aware of its potential and limitations. Making machine learning sound like something it's not yet could be bad for the technique itself. If it can't live up to the expectations that have been set, funders, programmers, enterprises, and scientists may decide it isn't worth their time. Here's an overview of the common myths and gaps related to this discipline.

Not Every Dataset Counts

According to general observance, companies fail to deploy their ML products because they jump in without having the appropriate data. To use machine learning, institutes and companies need data. And not just any data. One must match the data with the intent of the machine learning models, and this requires intention and design. In case the data does not exist. It will take the user some effort to get the appropriate dataset required. This is important as the model can only predict items based on the data given. For instance, a finance ML software cannot give information about the potency of a drug compound.

It takes Time

There are plenty of large scale-companies raving about leveraging machine learning applications. This leads to the myth that this technology is not for small scale companies or startups or the misconception that only organizations with strong financial backing can afford it. Neither of these is true. Machine learning has been used in the past by smaller companies with less budget, which may not result in the kind of media-friendly ways one might expect. Also, it takes time, experimentation, and effort to be successful in ML adoption, especially at the enterprise level. Compared to expectations, it is slow to take off, and simply having models is never enough. Companies need a robust framework and strategy too. But enterprise ML needs a little more holistic and uniform approach.  Sometimes, they may need to have MLOps.

We still need Experts

Unlike what has been marketed, machine learning does not totally automate the end-to-end process of data to insight (and action), as is often suggested. We do need human intervention. And having the right mix of specialists is equally important as they have the expertise to build prototype projects in different business lines. Thus, one must hire the right team in the context of her organization to ensure an assured path towards success.

Besides, it is important to note that organizations don't have machine learning problems. Instead, there are just business problems that companies might solve using machine learning. Therefore, identifying and articulating the business problem is mandatory before investing significant effort in the process and before hiring the machine learning experts.