Why do Machine Learning Strategies Fail?

Machine learning (ML) is one of the most powerful tools used today. From planning your route on Google Maps to assisting you to tag your friend on Facebook, machine learning is all-pervasive.

However, there is a hidden facet to machine learning models. According to the October 2020 Gartner report on technology trends, only just 53% of initiatives made it to success from prototype to production, even at firms with AI experience. In firms where machine learning model building is still work-in-progress, failure-rate estimates could be as high as 90%.

Why so? Let us examine some of the common reasons why machine learning strategies fail. One key factor is lack of good talent, and that’s where as an aspiring machine learning engineer, you could begin by taking a Machine Learning course with data science learning.

Why do Machine Learning strategies fail?

Many algorithms fail during backtests, as their predictions cannot replicate with a new data set. Other models that pass such tests, run short of real-world expectations. 

There are many reasons why machine learning strategies fail. A key factor is that machine learning has to be trained well to be effective and useful.

Failures are either technical or strategic. Technical failure happens because of the machine learning engineer or data scientist, and strategic failure occurs because of poor planning or misaligned priorities. A common reason is when the data scientist/machine learning engineer is not technical or business savvy enough. 

Here are some of the factors that may have resulted in this situation:

Asking the wrong questions

At the core is gathering the right data, possible only when you ask the right questions. You must understand the domain and the business problems of the organization for expert categorization of the dataset. Even the most strong algorithms begin with evaluating the data types to validate the model’s conclusions.

Failure to focus on the business use case

When developing requirements, the question to keep in mind would be whether the Machine learning model offers value to the business? Bad planning and redundancy is another reason why machine learning strategies fail even before making the prototype. Misaligning R&D efforts with business interests is a chief reason for failure.

Insufficient data

Machine learning is heavily reliant on data and the inability to access data is a big problem. Although data-heavy organizations like Google and Facebook may not face an issue, smaller organizations and startups with highly skilled professionals and good funding still battle to get good data and the right data. For instance, data in the banking sector is sensitive and well-guarded, with restricted access, without which a machine learning strategy built around a relevant banking problem would fail.

Poor data quality

The consistency of data gathered and analyzed has an impact on the outcomes. Thus, poor data quality or inaccurate data leads to unfavorable outcomes – a common issue when dealing with tasks involving say the healthcare industry, the government sector, or other similar areas. This is because the data may be uncommon or subject to stringent access and restrictions. Start-ups are most vulnerable to this failure in machine learning strategy as they may lack the required resources to generate high-quality data.

Another scenario is the labeling of data in supervised learning, especially in manual labeling. Training your model with the wrong data leads to a bad product, and hence, a failed machine learning project.

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Machine learning algorithms depend on the training data they process. If this data is noisy, inaccessible, or, say, outside the bounds of the creation data set, probably the model will fail. The data may be insufficient for a machine-learning algorithm to learn, or for it to perform at the level required by the company.

A Rackspace survey reports 34 percent of the respondents stated poor data quality as the main reason for the failure of machine learning research and development, and another 31 percent said they lacked production-ready data.

Too much data 

Sometimes having too much data, especially data not very relevant to the problem under consideration, is a hindrance that may ruin any modeling attempt. 

Selection bias

Human judgment in data selection and the use of the type of computing method can affect the strategy of running the models unless the method is automated. When there is a selection bias in the creation dataset, you have a flawed model.

Improper testing or overfitting

It is especially true in supervised learning, where validation or cross-validation is used to compare the predicted accuracy of different models. So proper testing and iteration using numerous models give a good model, and failure to do so may result in the collapse of the machine learning strategy. Depending on the application, the right trade-offs between speed, accuracy, and complexity of various algorithms can give the best model, while improper testing may result in malfunction.

Not using the correct model

A model is a pre-version of reality. A good model helps users to focus on the critical features of the final product that are relevant. However, a model that works well in one circumstance may not work well in another. 

Using the wrong tools

Not using the correct technology stack in a project can lead to your machine learning strategies failing.

Hiring the wrong people

Having extensive subject knowledge is helpful, just as hands-on experience working with different types of datasets and business problems. A fine balancing act is needed by having the right person on the team: subject matter experts, good communicators, and an experienced talent pool.

Bottomline

Machine learning models can simplify things beyond our wildest dreams. Without success in model creation, the purpose is lost, and the entire exercise is wasted. For businesses to succeed, the machine learning models must be developed the right way. Companies must start by hiring the right people who have the certifications and experience.