A recent Gartner report says that only about 53% of machine learning projects reach the production phase.
What happens to the other 47%?
Industry wisdom tells us that—lack of skills, quality data, and a lack of a thorough process— bring machine learning projects to a halt.
It is wise to understand the reasons why your machine learning projects came to a halt before you begin rebooting them.
The following four steps can help you do that.
Step 1 - Start with your dataAssess how your data was labeled and by whom. The first question you should answer is—how you prepared and structured your data? It would help if you understood who cleaned and prepared your data. Data preparation is a highly skilled endeavor. Lack of a skilled workforce to label your data can jeopardize your entire ML project. We both know that quality data is crucial for machine learning success. Still, many organizations fail to dedicate the right amount of attention and resources to label data. Here are three reasons why high-quality data is crucial:
- You need high-quality data to train your machine learning models.
- You need high-quality data to test your models.
- You need high-quality data to validate your models.
Step 2 - Assess executive buy-inAssess when you got executive buy-in and how involved they were. You should ask the following question: When did you get executive buy-in, and how involved were they throughout the project? You see, two problems concerns getting an executive buy-in:
- Not getting a commitment from the beginning: Many machine learning projects never get a complete buy-in or commitment at the beginning of the project. With no high-level support, your ML project hasn't got much time to survive.
- Executives lose interest in the project quickly: Even if you get an early commitment, the combination of failing to define a tangible outcome and failing to allow your executives to monitor the project's process regularly—can bring your ML projects to a debilitating halt.
Step 3 - Assess your infrastructureAssess what tools and human resources were used to run your project. Next, you must assess the tools that were used to run your project. Many organizations tend to build the infrastructure to—label their data and run their models—from scratch. Building the entire infrastructure is expensive and time-consuming. Always look for cloud technologies to run your ML projects and make it much easier and more likely for your projects to succeed. The following observation you should make is to understand if you had skilled, trained talent onboard to manage data preparation to model execution. There aren’t enough people today who can build ML models, so closing the skills gap is critical. In-house training is essential, as is adopting tools that make ML easier.
Step 4 - Bring in expertiseIf you don't have in-house ML expertise, choose a ready-made solution. If you don't have deep in-house machine learning expertise, you may look to solutions like AWS' SageMaker JumpStart. This will help your ML engineers get started quickly through a set of solutions for the most common use cases that can be deployed with a few clicks:
- including predictive maintenance,
- fraud detection, credit-risk prediction,
- churn prediction, and
- personalized recommendations, among others.