Machine learning is one of the sub-fields of Artificial Technology that helps to train machines into adopting behaviours that suites our purpose. In a simpler term, Machine Learning uses algorithms to perform iterative processes and learns from them. So, how exactly ML is deemed to be beneficial for us? The shortest answer to this might be, it can be used to save our time and effort.
How Important is Machine Learning?
Getting a good look at the market and its volume, it is pretty simple that in order to manage all that data, ML plays a significant role. Not only that, with the implementation of ML in an organization, it speeds up and optimizes the overall process. But, such great potential comes at a cost.
Here is our list of some of the challenges that you might face while adopting Machine Learning:
1) Lack of Proper Data Management
One of the biggest challenges of implementing Ml in an organization is the amount of raw data that it requires for the database. It is not like; only a few thousand would do the job. The data needs to be massive in size. While gathering the information is not sufficient enough. Sorting out which data needs to be processed is also crucial. Both sensitive and insensitive data are available, and it raises the security concern for the organization.
While the companies need to store these data in encrypted drives, it is always easier said than done. With the lack of any proper management, this is what makes ML inaccessible to most of the companies.
2) Lack of Infrastructure Required for Testing and Experimentation
According to most of the studies done on companies, Machine Learning is always hard to adopt, and the main reason for this is the lack of clear insight into the potential of ML.
If all of these seem complicated to you, let us make it understandable in simpler terms. Without the proper equipment and infrastructure, ML is very hard to achieve. And most of the times, companies are least interested in investing in such testing and experimentations. But, with proper knowledge on the returns that such investment can give, companies can create a great profitable growth.
3) Lacking Flexibility in the Business Models
Here we are talking precisely about the orthodox companies; we always prefer going by the books. But, in order to implement ML effectively, it is very important to have flexibility in their business models. The company needs to have a mindset to hire people with relevant skill-sets and make proper changes into their infrastructures.
Along with that, simply implementing Machine Learning can’t guarantee success. The company needs to go through certain tests and trials, and then only they can use ML for their own benefits. Studies have also shown that companies supporting random experimentation are more likely to succeed than the ones sitting idle and going by the books.
From the entire article, it might seem like implementing ML might be a tedious task, but it is not. With a clear view on how to use it and working towards the goal can lead to a successful implementation of Machine Learning.