It is very recent that there has been a surge of interest in Machine Learning applications and techniques. With time, organizations of all forms, businesses to corporates to governments, are steadily gaining momentum around machine learning techniques and understand its potential impact on their respective businesses.
However, the reality is further from the truth. We’re still in the early phases of adoption and it is after two decades of the introduction of machine learning that the models have now become mainstream. These new emerging technologies are considered as the key driver to productivity growth in the 21st century. The below-mentioned points will explore a number of aspects that should be considered before adoption of machine learning. It should be noted that these considerations should not be seen as hurdles to the adoption of machine learning but should act a checklist to make the best use of machine learning.
Addressing the skill gap
Artificial Intelligence, Machine learning, big data, etc. are relatively new and require skills which are not usually common in many IT departments. For instance, proper analysis of big data requires a data scientist who has a sound and in-depth understanding of data analysis techniques, business skills, and programming skills. It is rare to find data scientists and data engineers in IT departments as staffs. Usually, lack of expertise can be managed by multidisciplinary teams but the same cannot be managed in every organization or business. Organizations that hire for machine learning roles and who put resources behind its deployment are better poised for the future of business.
Although machines and computers are certainly faster and accurate in making calculations in comparison to a human, they are not necessarily capable of making better decisions, which a human can do. One must understand that Machine Learning is not explanatory, i.e. unless one is not an expert data scientist or a data engineer, it can never be understood that why the machine learning came to a particular conclusion. Without human intervention to feed the right data, the value of the results are reduced and the interpretation of these results make a whole lot of difference.
Strong leadership for machine learning success metrics
More often than not, new or less experienced organizations depend more on the product managers and executives to ascertain the success of machine learning projects. In large and experienced organizations, they entrust their data scientists and engineers to set priorities. Advanced organizations use multiple success metrics like statistical metrics, business metrics, and ML metrics, to gauge the predictive outcome of their machine learning tools. It is very clear that the adoption of machine learning introduces challenges that are far away from the standard practices and requires strong leadership to navigate the waters for better results.
The widely accepted notion is that all the new cutting edge technologies are expensive to implement. The moment an organization thinks of exploring the benefits of Artificial intelligence and machine learning, executives are ready to calculate the cost of purchases, licenses, consultants, etc. These days many companies are developing data analysis solutions using open source software which is free to use. Such open source software gives companies access to a global workforce and developers who can assist in machine learning tools and troubleshoot problems. These improvements can then be fed back into the open source code for the benefit of the community.
Fear, Uncertainty, and Doubt
It is well established that these new cutting edge technologies run on computers and are capable of much faster performance than a human. But in case of critical thinking, humans take the center stage in terms of innovative thinking, holistic approach and the ability to look at long term goals. All these ‘powers’ are yet to be replaced by computers. People are more worried about computers taking away their jobs but it is a known fact that technologies like artificial intelligence, machine learning, big data, etc. are not here to replace humans but make their lives easier and productive.
Machine learning has enormous potential, but in order to reap the benefits, it’s important to put your organization in a position to take advantage of all of it.