How many times did you come across the phrases AI, Big Data, and Machine Learning in 2018? Probably too many times. If you’re on a professional social networking site like LinkedIn, you might have had many sales reps trying to sell you their “new and revolutionary AI product” that will automate everything. The buzz surrounding Machine Learning has reached such a fever pitch that organizations have created myths around them.Machine Learning provides businesses with the knowledge to make more informed, data-driven decisions that are faster 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. Here are 5 common machine learning problems and how you can overcome them.
1) Understanding Which Processes Need Automation
It's becoming increasingly difficult to separate fact 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 seeking to solve. The easiest processes to automate are the ones that are done manually every day with no variable output. Complicated processes require further inspection before automation. While Machine Learning can definitely help automate some processes, not all automation problems need Machine Learning.
2) Lack of Quality Data
The number one problem facing Machine Learning is the lack of good data. While enhancing algorithms often consumes most of the time of developers 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 the time to evaluate and scope data with meticulous data governance, data integration, and data exploration until you get 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 to easing implementation. Partnering with an implementation partner can make the implementation of services like anomaly detection, predictive analysis, and ensemble modeling much easier.
5) Lack of Skilled Resources
Deep analytics and Machine Learning in their current forms are still new technologies. Thus, there is a shortage of skilled employees available 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. Recruitment will require you to pay large salaries 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 list of skilled data scientists to deploy anytime.
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