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Machine learning has come up as a paradigm shifting technological advancement and its scope has expanded fast across different industries. Today retailers are using machine learning capabilities to stock manage their inventories, marketers are using … Read More
Machine learning has come up as a paradigm shifting technological advancement and its scope has expanded fast across different industries. Today retailers are using machine learning capabilities to stock manage their inventories, marketers are using it to segment customers, analysts are using it to find patterns in data, and sports managers are using it to predict when a player is likely to get injured. A world full of possibilities is out there. As nature would dictate there have to be some challenges. There are, in fact, quite a few challenges to be overcome in the field of machine learning; let us take a look at them.
While machine learning emulates the way of human cognition, training a machine to tell apples from oranges is not nearly as simple as training a child to do so. You need hundreds of images of apples and oranges in different shapes and colors to train the machine. While arranging these images do not sound like a challenge, things get a little harder when you are trying to train an algorithm with behavioral data where features are much more varied.
If you work with too little a data set, there will be chances of sampling noise, a large data set has its own challenges in terms of processing and sampling quality. Which leads us to the next point.
The saying ‘garbage in garbage out’ is very popular in the machine learning and data science communities. If you train your machine learning models with poor data the models will be inaccurate and far away from the desired target.
This is why data science professionals spend a large chunk of their active hours cleaning and organizing data. Moreover, the applicability of training data differs with the conditions in which the model is used. This leads us to the next challenge.
The machine learning model is trained using large samples of data and it may produce satisfactory results during the test. However, it does not guarantee that the model will function nearly as well when put in a real time environment with new data fluxing in.
The functionality and accuracy of the model is influenced by the geographical location where it is used, the devices it is used for, the political and economic situation in the region etc. For instance the sudden change in customer behaviour during the outbreak of the Covid-19 pandemic trumped all customer analytics models overnight.
Machine learning algorithms look for patterns in data based on the features or independent variables. These features are selected and extracted based on the domain and the target of the machine learning model.
The process of determining features is termed as feature engineering. Feature engineering has come up as an important part of machine learning as the features are really critical for the success of machine learning models and irrelevant features can cost a lot of time and money.
The features you target for customer segmentation cannot be the same as the ones you use for public health analysis even if you use the same demographic data.
It often occurs that a prototype machine learning model works fine but when scaled up to meet the enterprise requirements it falters and shows inaccuracies. Optimizing models for different data quantities and different situations is something that goes wrong the most times.
Model scalability and optimization, therefore, one of the most faced challenges in machine learning. For instance, a machine learning model that works for Iceland will not work for India because of the geographical, habitual, and demographic mismatch between the two countries.
Machine learning is a difficult trade to be in. It demands a lot of practice, patience, and skill from the practitioners. You cannot claim preparedness even if you graduate from the best data science institute in Bangalore unless you have handled real time projects and achieved results.
The field is characterized by constant change. The technique in vogue today may run out of currency tomorrow. You must have the right attitude to cope with these changes. The learning can never stop. The more cases you study, the more projects you handle, and the more paths to solving a problem you explore, the better.
The machine learning industry is facing challenges in terms of data quality, skill gap, feature engineering, scalability, and data acquisition. In spite of all these issues the industry has been growing in leaps and bounds and it will continue to do so.