Machine learning has seen use cases ranging from predicting customer behavior to creating operating systems for self-driving cars.
When it comes to advantages, machine learning can help enterprises understand their customers in depth. By collecting customer data and correlating it with behavior over time, machine learning algorithms can learn associations and help teams tailor product development and marketing initiatives to customer demand.
Some companies use machine learning as a primary driver in their business models. Uber, for example, uses algorithms to match drivers with riders. Google uses machine learning to show ride ads in search.
But machine learning comes with disadvantages. First and foremost, it can be expensive. Machine learning projects are usually run by data scientists, who earn high salaries. These projects also require software infrastructure which can be costly.
There is also the problem of machine learning bias. Algorithms trained on data sets that exclude certain populations or include errors can lead to inaccurate models of the world, which are, at best, unsuccessful and, at worst, discriminatory. When an enterprise bases core business processes on a biased model it can cause damage to regulatory and reputation.
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How to choose the right machine learning model
The process of choosing the right machine learning model to solve a problem can be time consuming if not approached strategically.
Step 1: Align the problem with the possible data inputs that should be considered for the solution. This step requires the help of data scientists and experts who have a deep understanding of the problem.
Step 2: Collect the data, format it and label the data if necessary. This move is usually led by data scientists, with the help of data wranglers.
Step 3: Choose which algorithms to use and test them to see how well they perform. This step is usually performed by data scientists.
Step 4: Continue to fine tune the outputs until they reach an acceptable level of accuracy. This step is usually done by data scientists with feedback from experts who have a deep understanding of the problem.
Importance of human explanatory machine learning
Explaining how a specific ML model works can be challenging when the model is complex. There are some vertical industries where data scientists have to use simple machine learning models because it is important for the business to explain how each decision was made. This is especially true in industries with heavy compliance burdens, such as banking and insurance.
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Complex models can make accurate predictions, but explaining to a lay person how the output was determined can be difficult.
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