In 2018, how many times did you hear the terms artificial intelligence, big data, and machine learning? I'm sure I've done it a few times too many times. Machine Learning has become so popular that organizations have invented myths about it. Many sales agents may have tried to sell you their "new and groundbreaking AI software" that will automate everything if you're on a professional social networking site like LinkedIn.

Machine Learning equips organizations with the knowledge to make better-informed, data-driven decisions faster than they could use traditional methods. However, it isn't the mythological, magical procedure that many people imagine. ML has its own set of difficulties. 

Top ML Problems & Their Solutions

Here are five frequent ML issues and how to fix them.

Identifying Which Processes Should Be Automated

In today's world of Machine Learning, separating fact from fiction is becoming increasingly challenging. It would help to analyze whatever challenges you're trying to tackle before deciding on which AI platform to use. The operations done manually every day with no variable output are the easiest to automate. Before automating complicated procedures, they must be thoroughly inspected. While ML may undoubtedly aid in automating some processes, it is not required for all automation concerns.

Data Of Poor Quality

The absence of good data is the number one issue that Machine Learning faces. While improving algorithms takes up most of a developer's effort in AI, data quality is critical for the algorithms to work as planned. The fundamental adversaries of optimal ML are noisy, filthy, and incomplete data. Take the time to assess and scope data using diligent data governance, data integration, and data exploration until you have specific data, which is the solution to this problem. This is something you should do before you begin.

A Lack Of Infrastructure

Machine Learning necessitates a large quantity of data churning capacity. Legacy systems frequently fail to cope with increased workloads and buckle under pressure. Check to see if your infrastructure is capable of handling ML. You should consider upgrading, including hardware acceleration and flexible storage options if it can't.


By the time they decide to upgrade to Machine Learning, most companies have analytics engines in place. It's challenging to integrate newer ML approaches into current procedures. Maintaining correct interpretation and documentation can help make implementation much more accessible. The use of an implementation partner makes it easier to build services such as anomaly detection, predictive analysis, and ensemble modeling.

A Scarcity Of Qualified Personnel

Deep analytics and ML are still relatively young technologies in their current incarnations. As a result, there is a scarcity of qualified people to manage and generate Machine Learning analytical material. Data scientists frequently require a mix of domain knowledge and an in-depth understanding of science, technology, and mathematics. You'll have to pay a lot of money to hire these people because they're in high demand and know their worth. You can also ask your vendor for help with staffing, as many managed service providers keep a roster of qualified data scientists on hand to deploy at any moment.


Building robust models necessitate several crucial considerations throughout the process. While the factors above are beneficial, there are virtually countless considerations to be made even after the model has been finalized. Download iMerit's solutions brief for more AI and ML insights, including how to construct the finest ML model possible.

So, if you wish to grow your business using machine learning, Contact the ONPASSIVE team to know how.