3 Criteria for a Successful Machine Learning Project

A checklist for a successful machine learning project

Many companies are taking advantage of the latest AI and machine learning technologies to create better products and services. If you consider joining their ranks, you are wise to do so. However, before diving into a new machine learning project you need to make sure that you have identified the best opportunity for your company. Here are three criteria that will help you check if your idea is worth the investment:

Three criteria of a successful machine learning project

1. Can it generate revenue?

Machine learning projects, like any other business investment, should contribute positively to the bottom line.

To understand how a machine learning (ML) project can do that, you and your team need to answer the following question: how can our ML system enhance the end user experience of our product or service?

It can be useful to think about what real-life problems your customers might experience and match them to the ML problems you are capable of solving. When the realistic use case is found, think about a monetization model for the new functionality. Are users willing to pay for it? Will the new functionality increase average revenue per user and total revenue?

If you think about a machine learning project that promises to create greater business process efficiencies, there is no need to build your own machine learning system from scratch, consider buying an AI solution from a vendor. In most cases, you’ll be able to find a market-proven solution for your specific problem that will save you both time and money. For example, if you want to bring a chatbot into your e-commerce store, you can do it within minutes thanks to companies like Morph.ai, Botcommerce.io, Letsclap.io.

‘Your best machine learning projects should bring you additional revenue  —  these are the solutions you must own.’ – Kevin Dewalt

2. Can you use existing, proven technology?

Big companies like Google and Amazon are making significant breakthroughs in the field of AI and machine learning all the time, and they are making it publicly available. With Google Cloud’s AI, you can get access to modern machine learning services, with pre-trained models and a service where you can create your own tailored models. Amazon also provides a diverse selection of pre-trained services for automatic speech recognition, natural language understanding, and image recognition.

Building new models based on existing, pre-trained algorithms is a popular concept called transfer learning. It entails building innovative AI products by leveraging APIs from others in the industry.  

In other words, there is no need for you to spend your budget developing something that is already out there. You don’t have to reinvent the wheel, just make a better use case for the proven technology… and let the big players spend big money on R&D and make fundamental advances in AI.

3. Can your internal data support it?

Generally speaking, there are three things that can enhance the performance of AI: faster computers, better algorithms, and better training data. The first two are widely available in the market through the development of GPUs and publicly available APIs from big players like Google, Facebook, and Amazon. The same applies to publicly available data.

Both you and your competitors can take advantage of these tools to build better, more complex models, which means that only proprietary training data can provide an ongoing competitive advantage.

Acquiring enough good quality data has always been one of the biggest issues in machine learning. Training data can get expensive because it often takes many hours of expert work to properly label it. As a result, many companies lean on existing training data that is available online. That is useful but only in combination with your own data. Keep that in mind when answering this question.

What’s next

When your idea meets all the criteria above, you can dive deeper into your project, thinking about key aspects of your ML system. If you don’t have people with machine learning experience within your company you should also think about selecting a data science company that will help you turn your most innovative ideas into reality.

I hope this article was helpful for you and wish you good luck with your future machine learning projects! 

Work with InData Labs on your machine learning project

Have a project in mind but need some help implementing it? Drop us a line at info@indatalabs.com, we’d love to discuss how we can work with you.