Applied Machine Learning - MIND THE GAP
Machine Learning becomes the NEW NORMAL as more and more companies are embarking in such projects, therefore it's important to distinguish what's hype and what's real value for the business (i.e. gaps).
Below you can find five such gaps which will allow you to plan and execute your machine learning journey in a safe manner, with valuable outcome for the business:
Gap#1: All you need for a good start is Machine Learning expertise
The reality is that companies don't have Machine Learning problems. There are just business problems that companies might solve using Machine Learning. Thus, it is essential to identify and articulate the right way the business problem before investing significant effort in the process (i.e. time, budget, people). Also, a good practice is to prioritize the cases to be addressed based on the estimated business benefits.
Gap#2: Machine Learning is magic in getting quick business results at enterprise level
In practice, there is no magic in machine learning and the path to ML success is hard - it takes time and effort, especially at enterprise level. Machine learning needs a process and a powerful framework to be successful. Data and models are just some of the ingredients, but enterprise ML needs a holistic and uniform approach, and in order to industrialize ML, companies should address a new challenge - ML Ops.
Gap#3: The more data and algorithms, the better
Data and algorithms quality and relevance to the defined business problem matter most. The quality of the inputs (in terms of completeness, accuracy, contextual) determines the quality of the ML outcome applied to a business challenge. In addition to quality data and relevant algorithms, the other two key elements for success are: usage of the latest technologies (hardware and software) and the right mix of specialists (Data Scientists, Developers and Business Owners).
Gap#4: A good team of Data Scientists is all the enterprise needs for success
In practice a combination of top-down and bottom-up strategies will ensure the right balance. On one hand, it's important to create the ML vision and strategy, to ensure sponsorship at top level. On the other hand, it's equally important to have the right mix of specialists who will build prototype projects in different lines of businesses and thus learning by doing in the context of the organization and ensuring a strong path towards success.
Gap#5: Machine Learning will replace human labor
The fact is enterprise ML and people need each other. The new paradigm of Machine Learning is considered to have similar impact for humanity as other major discoveries had in the past (i.e. electricity or engine discovery), thus a similar trend is expected for human labor in ML era. Some of the jobs will be reinvented, but for sure machine learning will augment people and lots of other new jobs will appear (i.e. let's just imagine the jobs generated due to automotive industry). Repetitive and unpleasant tasks will be covered by ML applications while people will focus on value-added ones and thus getting additional professional satisfaction.
In conclusion, the gap between the ML hype (the promise) and real value exists and is significant; the imminent risk is top management to set the expectations at hype level, but the team to be able to execute at maximum real value level of ML; this may lead to a lot of dissatisfaction from both sides.
Thus, this Latin proverb fits well in this context: "Festina lente". In other words, if you don't Mind the Gap, be prepared to pay the price for being in a hurry.