Often, machine learning projects fail because the data that is used in the project is of poor quality. This is because algorithms are only as good as the datasets they are trained on. Without high-quality data, machine-learning projects cannot solve a problem. Poorly defined questions can also result in unsolvable problems. These problems are universal to any process or tool. This article provides some insight into the reasons why ML projects fail.
Firstly, when implementing ML, it is essential to understand the pitfalls of this type of project. In some cases, organizations are overly eager to implement the technology, which leads to unreliable results. Having a clear idea of how to avoid these mistakes will make it easier to avoid failure altogether. Moreover, it is important to have a plan to make sure that ML projects are successful. A solid roadmap is a must-have in this regard.
Secondly, ML projects have creepy infrastructure requirements. ML algorithms tend to work better with more data, so compute power requirements are high. And as ML projects are typically made of sensitive data, companies cannot afford to have a leaky infrastructure. The failure to have an adequate infrastructure can lead to desertion of ML projects. So, what should organizations do? Listed below are some common reasons for ML project failure.
A lack of collaboration between teams. The data scientist and business user do not understand what counts, which leads to noisy results. This can lead to a complete rewrite of the project. Similarly, the project can end up in the research stage, leaving the business team unable to accept the results. According to a recent MIT Sloan Management Review, only 50% of large enterprises have a data strategy. Therefore, it is critical to develop a solid data strategy before embarking on a Machine Learning project.
Another common reason for ML project failure is that the project managers fail to accept the inherent fallibility of AI models. While these are not necessarily wrong, they will result in disastrous failure if they do not take into account the inherent fallibility of human beings. Additionally, real-time data may differ from the training data and preferences, making a model inaccurate. The failure to accept this fact often results in the project’s demise.
Organizations that make it through the first step often struggle to create a proper end-user application. Most often, they end up building nightmare applications that are difficult to update with changing data or model outputs. Not only are these nightmare applications difficult to maintain, but they also complicate the process. Ultimately, they can delay or even sabotage ML initiatives. You can avoid these pitfalls by understanding the underlying reasons behind failures.
The first reason why machine-learning projects fail is that they don’t have enough training data. Many organizations underestimate the amount of time it takes to train models, and therefore don’t get into production. A lack of training data is another common reason for ML project failure. Without high-quality training data, it’s impossible to develop an effective model. When you don’t have enough training data, you’ll end up with a faulty model and no real business impact.
The second reason why AI projects fail is that the underlying business problem isn’t sufficiently defined. There are countless problems that can be solved by other methods, and it’s important to define the business problem that AI is addressing. If the business problem isn’t solvable with AI, a more basic solution may be to invest in improving customer service skills. Ultimately, the first and most important reason why ML projects fail is that the business can’t justify scaling the project.
If your model doesn’t perform well, don’t put it into production. If the model isn’t working, you should test it using old data streams and fallback models. Testing is essential for AI. Experiments help companies build AI at scale. Experimentation enables companies to improve products, processes, customer experiences, and business models. This means that you should experiment to understand which models are best and which ones don’t.
Deploying ML projects often starts as an afterthought and ends in a bloody deployment. Models become unstable, pipelines are erratic, and security is often neglected. These underlying issues cause ML projects to fail, so make sure to start early. It will make continuous improvements feasible and reduce the likelihood of failure. And if you’re already running a production ML pipeline, why waste time and money on an inefficient ML project?