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

April 29 2021
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The hype about machine learning (ML) is well deserved. It’s not just making things easier for the companies that are taking advantage of it. It’s changing the way they do business for the better. For example, ML is:

  • Being used by financial institutions to quickly detect fraudulent activity
  • Enabling healthcare practitioners diagnose diseases and prescribe appropriate treatments more effectively
  • Helping manufacturing companies monitor equipment so issues can be dealt with before they disrupt operations
  • Allowing streaming services to identify customers at risk of taking their business elsewhere and helping determine what steps can be taken to retain them

With the availability of increasing amounts of data, low-cost data storage, and less expensive, more powerful data processing, the potential applications of ML are expected to grow exponentially.

So why then are so many companies hesitant to jump on the ML bandwagon – and why is the success rate so low for those that do embark on ML projects? Afterall, organizations such as Gartner note that up to 85% of ML projects ultimately fail to deliver on their intended promises to business.

More important: what can companies do to ensure a higher success rate so they can leverage the promise of ML?

Machine Learning is Different

To increase the chances of ML 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 especially critical when it comes to 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: hire them, which isn’t always easy 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 can be difficult to facilitate collaboration and communication between the different teams. Traditional software and app development usually differs 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.

The Issue of Data Quantity and Quality

There’s also the matter of data quantity and quality. ML projects use large datasets, since larger datasets facilitate better predictions from ML processes. But as the size of the data increases, so do the challenges.

Data is usually merged from multiple sources. Often that data is not in sync, which can create confusion. In addition, data can get merged that wasn’t meant to be merged, resulting in data points with the same name but different meanings. Bad data can generate results that aren’t actionable or insightful, or that are misleading.

The 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 a great deal of time and expertise is committed to the labeling process rather than ML model training.

Outsourcing can save both time and money but doesn’t work well if the labeling task requires specific domain knowledge. In those cases, organizations also must invest in formal and standardized training of annotators to ensure quality and consistency across datasets. The other option is to develop their own data labeling tool if the data to be labeled is extremely complex. However, this can require more engineering overhead than the ML task itself.

Yet another data-related issue is that the data required in an ML project often resides in different places with different security constraints and in different formats — structured, unstructured, video files, audio files, text, and images. Data preparation is required, a process that includes searching, cleaning, transforming, organizing, and collecting data. It’s a time-intensive activity that can require teams to spend up to 80% of their time converting raw data into high quality, analysis-ready output.

For both the data labeling and data preparation, automation can help remedy the situation – but again, requires expertise that internal teams typically lack.

Great Expectations

ML projects aren’t cheap, so it’s not uncommon for organizations to have overly ambitious goals for them. There are often expectations that a project will completely transform the company or a product and generate an enormous return on investment. That creates a lot of pressure that can, in turn, lead to second guessing on strategies and tactics. Not surprisingly, these kinds of projects tend to drag out. As a result, both 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 need to be made, if the model is effectively solving the intended business needs, or if you other options should be considered.

Machine Learning Success Factors

While there are no specific guidelines for ensuring a successful ML project, there are ways to overcome many of the issues that can lead to project failure. Among them:

  • An understanding of how ML works, how it differs from other project types, and what’s required to execute an ML project
  • A properly scoped project with realistic goals, budget, and leadership support
  • The resources to run an ML project, including experienced team members — whether in-house or outsourced, and a commitment to collaboration and open communication
  • Large amounts of data, preferably labeled
  • Capabilities for collecting, storing, labeling, cleaning, quickly accessing and processing large volumes of data
  • Software tools for executing ML algorithms
  • A development platform, such as AWS, Baidu, Google, IBM, or Microsoft

Partner with ClearScale

One of the best ways to execute a successful ML project – and leverage the benefits ML offers, is to partner with a company like ClearScale.

ClearScale has extensive experience in the latest and most popular ML and AI frameworks, libraries, platforms, and programming languages. That experience is complemented by vast expertise in software architecture best practices, DevOps, automation, data science, and more, as we’ve successfully demonstrated in a variety of projects for a wide range of customers.

In addition, collaboration and communication are integral to how ClearScale works. And, as an AWS Premier Consulting Partner with the Machine Learning Competency, ClearScale possesses proven expertise in using both the AWS Cloud and AWS services to provide cloud machine learning services.

Whether employing SageMaker AutoPilot, which automatically generates ML models, Amazon Translate, a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation, or any number of AWS’s many other ML, AI and cloud services, ClearScale knows how to identify and implement the right AWS services to solve specific business needs.

Get Ready to Kick Off Your Machine Learning Project

To learn more about the value of ML, download our free eBook, “Transform Your Business With Machine Learning.”

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