The hype around machine learning is understandable: it's making things easier for the companies that leverage it and changing the way business is done for the better.
We expect that the availability of big data, low-cost data storage, and cheaper yet more powerful data processing will exponentially expand the potential applications of ML.
So why are so many companies hesitant to jump on the ML bandwagon? And why do those that do embark on ML projects have such a low success rate? After all, Gartner Note It has been found that up to 85% of ML projects ultimately fail to deliver the intended business outcomes.
What can companies do to ensure a higher success rate and deliver on the promise of machine learning?
How is your organization currently using machine learning?
- Financial institutions are using machine learning to more effectively detect fraud.
- Healthcare professionals are using ML to diagnose illnesses and prescribe appropriate treatment more effectively.
- Manufacturing companies are using ML to monitor their equipment so they can address issues before they disrupt operations.
- Streaming services use it to identify customers who are at risk of taking their business to other companies.
What are the characteristics of a machine learning project?
The first step to increasing the chances of success for any machine learning project is to understand that ML projects are different from typical applications or software development projects: they have different processes, terminology, workflows, and tools.
Talent requirements also differ, most importantly data scientists, who play a key role in defining the success criteria for ML models, their eventual deployment, and ongoing monitoring.
Data engineers, business intelligence specialists, DevOps, and application developers also play important roles. Few organizations have the in-house resources to fill these positions. The options are to either hire these talent or outsource them, which can be difficult because ML is still a relatively new field with few experienced experts.
Even if an organization has enough people, it is difficult to foster collaboration and communication between different teams. Traditional software and app development is usually very different from data science projects.
While software development tends to be predictable and measurable, data science can require multiple iterations and experimentation. Expectations are different and typical deliverables are different.
Machine learning project challenges
Quantity and quality of data
Machine learning projects use large datasets because the larger the dataset, the more accurate the predictions from the ML process.
But as data size grows, so do the challenges. Machine learning typically involves integrating data from multiple sources, which is often not synchronized and can become confusing.
Additionally, ML can combine data that should not be combined. This can result in data points with the same name but different meanings, and inappropriate data can lead to non-actionable or misleading results.
Labeling Data
A lack of labeled data can also be an issue, leading some teams to attempt the tedious task of labeling and annotating training data themselves, while others try to create their own automated labeling and annotation technology.
The problem is that a lot of time and expertise is spent on the labeling process, not on training the machine learning model.
While outsourcing can save both time and money, it is not effective when the labeling task requires specific domain knowledge. In such cases, organizations must also invest in formal, standardized training of their annotators to ensure quality and consistency across datasets.
Alternatively, if the data you want to label is very complex, you could develop your own data labeling tool, although this may require more engineering overhead than the ML task itself.
When labeling data, there are two aspects of the process that contribute most to the success of your project. The first is the quality of the labeling: the better your data is labeled, the better the results will be.
The second, and perhaps more difficult to do, is scaling. Without automation to help label data at scale, projects are likely to fail.
For example, the Amazon Web Services platform offers Amazon SageMaker Ground Truth, a service that addresses quality and scale, is automated, and allows humans to be involved in the quality review and labeling of data that is too difficult to automate.
Data Preparation
The data required for ML projects often resides in different locations, with different security constraints, and in different formats, such as structured and unstructured files, video files, audio files, text, and images.
Data needs to be prepared, which involves finding, cleaning, transforming, organizing, and collecting data. This is a time-consuming task that requires teams to transform raw data into high-quality, analyzable output.
Both data labeling and data preparation can be improved through automation, especially when dealing with large volumes of data, but they require expertise that in-house teams typically lack.
Unrealistic expectations
Because machine learning projects aren't cheap, organizations often set overly ambitious goals for their machine learning projects or expect them to transform their business or products and generate huge returns on their investments. This creates a lot of pressure and leads to regrets about their strategy and tactics.
These types of projects tend to drag on. As a result, project teams and management lose confidence and interest in the project, budgets reach their limits, and even the most professionally run projects are doomed to failure if the goals are unrealistic.
In some cases, ML projects begin without aligned expectations, goals, and success criteria between the business and project teams.
Without clearly defined success metrics, it will be difficult to determine if the project was successful, what changes need to be made, whether the model is effectively solving the intended business need, or whether other options need to be explored.
Machine learning success factors
Here are some ways to overcome issues that can lead to project failure.
- Understand how machine learning works and how it differs from other types of projects.
- Properly scoped projects with realistic goals, budgets and leadership support.
- Resources to execute ML projects, including experienced team members, either in-house or outsourced.
- Lots of data (preferably labeled).
- The ability to collect, store, label, clean, and quickly access and act on large amounts of data.
- Software tools for running ML algorithms.
- Development platforms from AWS, Google, IBM, Microsoft, and more.
Be clear and realistic
The potential of machine learning is enormous, but so are the hurdles to successfully implement it. The challenges of different project dynamics, the need for high-quality data, and the need for expertise underscore the importance of a well-thought-out strategy.
To ensure machine learning projects deliver the intended benefits, organizations need to focus on realistic project scoping, comprehensive data management, and ongoing collaboration among cross-functional teams.
By setting clear expectations, investing in the right resources, and fostering a deep understanding of the machine learning process, companies can navigate the complexities of these projects and fully leverage the power of machine learning to drive meaningful business transformation.
