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 data

Assess 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:

1. You need high-quality data to train your machine learning models.
2. You need high-quality data to test your models.
3. You need high-quality data to validate your models.

If you haven't got the proper process and resources to label your data, you will never be able to build sophisticated ML models.

Step 2 - Assess executive buy-in

Assess 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:

1. 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.
2. 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.

There have been cases where project leaders make commitments with executives without considering the time required to train and test the machine learning models.

As the project takes time to train and test models, executives can quickly lose interest in the project.

Understand how outcomes were measured and how you helped your executives keep a tab on the project's process.

Step 3 - Assess your infrastructure

Assess 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 expertise

If 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.

The idea is to find cloud-based solutions to get your ML projects up and running through open-source areas such as natural language processing, object detection, and image classification models.

You may also choose a qualified data labeling vendor.

Find a reputable data training partner and get your data prepared in phases quickly and cost-effectively.

You may not need all your data to be labeled at once.

While most ML projects choose supervised ML models, your ML engineers must get their hands on enough data to train ML models.

As your engineers train the models, you can occupy the data labeling vendor to prepare data to test the ML models.

Getting data labeled in a phased manner helps you save time and move your projects to production quickly and cost-effectively.

The best way to train your data is via the human-in-the-loop data labeling approach

Even if you choose to build your labeling tool and label the data in-house, you may want to hire a data labeling partner exclusively for the QA process.

Data labeling partners have highly trained and experienced data labelers and QA specialists, and choosing to get an experienced team to quality-check your labeling can deliver high-quality data.

Traindata is built by ex-Yahoo!s with over 15 years of experience managing and preparing data for large-scale ML projects. Visit www.traindata.us to hire us to label your data.