How many times have you come across the words “AI”, “Big Data,” and “Machine Learning” in 2018? Chances are, the answer is too many. If you’re on a professional site like LinkedIn, you might have had ‘n’ number of sales reps breathing down your throat about their “new and revolutionary AI product” that will “automate everything.” The buzz surrounding machine learning has reached such fever pitch that organizations have lifted it to mythical standards.
Machine learning provides enterprises with the knowledge to make more informed, data-driven decisions that are faster and leaner than traditional approaches. However, it's not the mythical, magical process many build it up to be. Machine learning presents its own set of challenges compared to other approaches. Here, we discuss 5 common machine learning problems and how to overcome them.
1) Understanding Which Processes Need Automation
It's becoming increasingly difficult to separate facts from fiction in terms of machine learning today. Before you decide on which AI platform to use, you need to evaluate which problems you’re looking to solve. The easiest processes to automate are the ones that are carried on manually every day with no variable output. Complicated processes require further introspection before automation. While machine learning can definitely help automate some processes, not all automation problems need machine learning.
2) Beginning Without Good Data
The number one problem faced during machine learning starting out with a lack of good data. While enhancing algorithms often consumes most of your time in AI, data quality is essential for the algorithms to function as intended. Noisy data, dirty data, and incomplete data are the quintessential enemies of ideal machine learning. The solution to this conundrum is to take your time to evaluate and scope data through meticulous data governance, data integration, and data exploration until you get crisp and clear data. You should do this before you start.
3) Inadequate Infrastructure
Machine learning requires vast amounts of data churning capabilities. Legacy systems often can’t handle the workload and buckle under pressure. You should check if your infrastructure can handle machine learning. If it can’t, you should look to upgrade, complete with hardware acceleration and flexible storage.
Organizations often have analytics engines working with them by the time they choose to upgrade to machine learning. Integrating newer machine learning methodologies into existing methodologies is a complicated task. Maintaining proper interpretation and documentation goes a long way into easing implementation. Joining hands with implementation partners can ease you into implementation with services like anomaly detection, predictive analysis, and ensemble modeling.
5) Lack of Skilled Resources
Deep analytics and machine learning in their current form are still new technologies. As such, there is a shortage of skilled resources you can hire to manage and develop analytical content for machine learning. Data scientists often need a combination of domain experience as well as in-depth knowledge of science, technology, and mathematics. Recruiting them will require you to pay big bucks as these employees are often in high-demand and know their worth. You can also approach your vendor for staffing help as many managed service providers keep a ready bench of skilled data scientist to deploy anytime.
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