5 Surefire Ways to Derail Your Machine Learning Project

The process of implementing something new typically involves making mistakes, heading in the wrong direction and then figuring out a way to right those wrongs and avoid those risks in the future. Adopting machine learning is no exception. If you aren’t careful, the mistakes you make can become encoded, at least for the time being, into your business processes. As a result, these errors will occur at scale and will be difficult to control.

On the other hand, when you proactively detect errors and take the steps to address and correct them right away, you’ll have much more success with the technology. To follow are five common pitfalls that can wreak havoc on your machine learning project so you’ll know what to watch for.

Lack of clear understanding.

Simply put, you cannot adequately solve a problem that you don’t fully understand. The same can be said for machine learning initiatives. If you don’t completely understand what problem you are actually trying to solve, the risk of errors goes up exponentially.

To avoid this, begin with a hypothesis statement. Ask what the problem is that you are trying to resolve and which models you plan on using to address that issue. This is key, because if it’s not done correctly from the start, things can go wrong very quickly.

Poor data quality.

The old adage, “garbage in, garbage out” can easily be applied to machine learning projects. If the quality of the data you are supplying isn’t up to par, the outcome will inevitably suffer. In fact, poor data quality is one of the top concerns of data managers, as it can impact analytics and ultimately influence business decisions in the wrong direction.

The result of these poor decisions can negative affect performance and make it difficult to garner support for future initiatives. Exploratory data analysis (EDA) can help you proactively identify data quality issues so you can prevent problems before they occur.

No specific purpose.

Another common contributor to machine learning failure is implementation without a clear purpose. In order for machine learning to produce ROI, it must be applied properly – not simply because it’s the cool thing to do. In fact, using machine learning when it’s not the best solution to a problem and/or not completely understanding the use case can ultimately cause more harm than good.

In addition to addressing the wrong problem, doing so can involve wasted time and resources, both of which come at a cost. To avoid this, identify the precise problem and desired outcome to determine whether machine learning is the appropriate solution. 

Insufficient resources.

It’s easy to underestimate the amount of resources required to do machine learning right, in particular as it relates to infrastructure. Without adequate processing power, successfully implementing machine learning solutions in a timely manner can be a difficult, if not impossible feat. And without the resources in place to allow for its deployment and use, what’s the point?

To address the expense and complexity of deploying a scalable infrastructure, leveraging a cloud service that can be provisioned on-demand may be the better option. Those wishing to keep things in-house should look for a lightweight, plug-and-play solution that doesn’t require coding and can be deployed across on-premises and private cloud platforms.

Poor planning and lack of governance.

It’s not unusual for a machine learning project to start off with tremendous enthusiasm only to lose momentum and ultimately end up grinding to a halt. When this happens, poor planning and lack of governance is most often to blame. For those projects that don’t cease, a lack of guidelines and limits can result in an exorbitant expenditure of resources without the beneficial end results. 

To keep things moving in the right direction, machine learning initiatives must be continuously monitored. In the event that progress begins to wane, it can be wise to take a break and reevaluate the effort. Keeping people engaged in the process is the key.

Machine learning can be a tremendous asset to an organization, but only if it’s planned, implemented and managed properly. By avoiding the five common pitfalls listed above, you can place your company in a much better position and improve your chances of long-term, sustainable success.

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