Why Machine Learning Projects Fail — and How to Make Sure They Don’t

Machine learning projects are probably different from what you’re used to. Here’s how to do them right.

Written by Kevin Epstein
Published on Jun. 17, 2024
Why Machine Learning Projects Fail — and How to Make Sure They Don’t
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The hype about machine learning is well deserved. It’s making things easier for the companies that are taking advantage of it, changing the way they do business for the better.

With the availability of big data, low-cost data storage and less expensive, more powerful data processing, I expect the potential applications of ML to grow exponentially.

So, why are so many companies hesitant to jump on the ML bandwagon? And why is the success rate so low for those who embark on ML projects? After all, Gartner notes that up to 85 percent of ML projects ultimately fail to deliver on their intended promises to business.

What can companies do to ensure a higher success rate and fulfill the machine learning promise?

How Are Organizations Currently Using Machine Learning?

  • Financial institutions are using machine learning to better detect fraudulent activity.
  • Healthcare practitioners are using ML to diagnose diseases and prescribe appropriate treatments more effectively.
  • Manufacturing companies are using ML to monitor equipment so issues can be dealt with before they disrupt operations.
  • Streaming services are using it to identify customers at risk of taking their business elsewhere.

More by This AuthorStop Freaking Out About Generative AI


What Makes Machine Learning Projects Distinct?

To increase the chances of machine learning project success, the first step is to understand that ML projects are not the same as typical application and software development projects. There are different processes, terminology, workflows and tools.

There are also different staffing requirements. Among the most important are data scientists, who are critical when defining the success criteria, final deployment and continuous monitoring of the ML model.

Data engineers, business intelligence specialists, DevOps and application developers also play key roles. Few organizations have the internal resources to fill these positions. Their options are to hire them, which is difficult given that ML is still a relatively new field with few experienced professionals, or outsource.

Even if an organization does have the staffing covered, it’s difficult to facilitate collaboration and communication between the different teams. Traditional software and app development usually differ greatly from data science projects.

Whereas software development tends to be more predictable and measurable, data science can entail multiple iterations and experimentation. Expectations are different. Typical deliverables are different.


Machine Learning Project Challenges

Data Quantity and Quality

Machine learning projects use large data sets, since larger data sets facilitate better predictions from ML processes.

But as the size of the data increases, so do the challenges. Machine learning usually merges data from multiple sources. Often that data is not in sync, which can create confusion.

In addition, ML can merge data that wasn’t meant to be merged. This can result in data points with the same name but different meanings. Bad data can generate results that aren’t actionable or are misleading.

Data Labeling

A lack of labeled data can also be an issue. Some teams may try to take on the laborious task of labeling and annotating training data themselves. Some may even try to create their own labeling and annotation automation technology.

The problem is that they commit a lot of time and expertise to the labeling process rather than machine learning model training.

Outsourcing can save both time and money, but is ineffective if the labeling task requires specific domain knowledge. In such a case, organizations must also invest in formal and standardized training of annotators to ensure quality and consistency across data sets.

Or, they can develop their own data labeling tool if the data to be labeled is extremely complex. This can require more engineering overhead than the ML task itself, however.

When labeling data, there are two facets of this process that will contribute most to your project’s success. The first is the quality of labeling. The better you label data, the better your outcomes will be.

The second, and possibly more difficult to execute, is scaling. If you don’t have automation to help you label data at scale, your project will likely fail.

For instance, on the Amazon Web Services platform we have Amazon SageMaker Ground Truth. This service addresses quality and scale, has automation and allows for human in the loop for quality review or to label data that’s too difficult to automate.

Data Preparation

The data required in a ML project often reside in different places with different security constraints and in different formats — structured, unstructured, video files, audio files, text and images.

You have to prepare the data, which includes searching, cleaning, transforming, organizing and collecting data. It’s a time-intensive activity that requires teams to convert raw data into high-quality, analysis-ready output.

For both data labeling and data preparation, automation can help remedy the situation, particularly if you’re dealing with large volumes of data. But it requires expertise that internal teams typically lack.

Unrealistic Expectations

Machine learning projects aren’t cheap, so organizations often have overly ambitious goals for them or expect them to transform the company or a product and generate an enormous return on investment. This creates a lot of pressure that can lead to second-guessing about strategies and tactics.

These kinds of projects tend to drag out. As a result, the project teams and management lose confidence and interest in the project, and budgets max out. Even the most expertly run projects are doomed to fail if the goals are unrealistic.

In other cases, ML projects kick off without alignment on expectations, goals and success criteria between the business and project teams.

Without clearly defined success indicators, it’s difficult to determine whether a project is successful, what changes you must make, if the model effectively solves the intended business needs or if you should consider other options.


Machine Learning Success Factors

Here are some ways to overcome issues that can lead to project failure.

  • An understanding of how machine learning works and how it differs from other project kinds of projects.
  • A properly scoped project with realistic goals, budget and leadership support.
  • The resources to run a ML project, including experienced team members — whether in-house or outsourced.
  • Large amounts of data, preferably labeled.
  • The capability to for collect, store, label, clean, quickly access and process large volumes of data.
  • Software tools for executing ML algorithms.
  • A development platform, like AWS, Google, IBM or Microsoft.

More on Machine LearningHow to Install cuDNN and CUDA for Windows and Linux


Be Clear and Realistic

The potential of machine learning is enormous, but so are the hurdles to successful implementation. The challenges of different project dynamics, the necessity for high-quality data and the need for specialized expertise underline the importance of a well-thought-out strategy.

To ensure machine learning projects deliver their intended benefits, organizations must focus on realistic project scoping, comprehensive data management and continuous collaboration between cross-functional teams.

By setting clear expectations, investing in the right resources and fostering a deep understanding of machine learning processes, companies can navigate the complexities of these projects and harness the full power of machine learning to drive meaningful business change.

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