10 February 2021

Four Pillars of a Successful Machine Learning Project

Should you consider Machine Learning technologies for your project? Find out from our simple checklist

10 February 2021

Four Pillars of a Successful Machine Learning Project

Should you consider Machine Learning technologies for your project? Find out from our simple checklist

10 February 2021

Four Pillars of a Successful Machine Learning Project

Should you consider Machine Learning technologies for your project? Find out from our simple checklist

Are you wondering how you could leverage the affordances of Machine Learning in your business? As developers of Machine Learning solutions since 2016, we've accumulated a lot of experience that we'd like to share in this article. Our idea is simple: not every company seeking to implement Machine Learning technologies actually needs them.

“

After reading this guide, you will understand if you should proceed with your ML initiative

“

After reading this guide, you will understand if you should proceed with your ML initiative

“

After reading this guide, you will understand if you should proceed with your ML initiative

If you are thinking of using ML algorithms in your business, you are already aware of the possibilities that they bring. You understand that there exists a class of smart algorithms capable of computing vast quantities of data to perform complex tasks. They can help your business be faster, smarter, and more precise. You are also likely to have already faced custom software development and hiring dedicated teams, so you may think that Machine Learning development goes the same way. And this is the first pitfall you may stumble upon.

Who is it for?

What you should be prepared for

The first thing you have to understand is Machine Learning projects are very risky. It is never possible to estimate the work hours required for the project. At the very beginning, there is no way to understand which direction to go. After elaborating an idea, we will set some paths and we will have to take a step on each of them. Even the first estimate on each option is a mini-research requiring a certain number of work hours.

Another thing to realize is you invest in the R&D process and not the end result. As opposed to traditional software development, having invested one-fifth of the total budget, you will not receive one-fifth of the final product. Instead, you will probably get proof if your hypothesis is right or wrong together with understanding, where to go next.

Usually, the first R&D step will last from 1 week up to 4 months. After that you may realize that you haven't been collecting your data properly and we can't use it to build a valid model. Sadly, one of our clients had to pay around $30,000 just to get this insight. Understandably, not every company is ready to invest this much in a 15-page report. It is only the ones who are really seeing the growth opportunities and the value that ML technologies can bring them.

As opposed to traditional software development, having invested one-fifth of the total budget, you will not receive one-fifth of the final product

Why do you need ML?

Machine Learning is used in different sectors: starting from retail and finance, through healthcare, to education and charity. Most companies want to invest in ML to gain competitive advantages in the market. The leading players will get into complex R&D simply because they know their competitors can't afford it. This will win them some time to come up with more competitive advantages. In fact, according to a research by Microsoft, companies using AI are outperforming by 5% those which have no AI strategy. If you are determined to become one of those, you should start by answering these simple questions:

1

Do you really need this feature?

Make sure you are not following the hype. Can your business function without ML this technology? Could there be an automation solution that will bring 80% of the effect for 20% of the budget that you'd spend on ML? If there is any chance that your feature can be made using traditional software engineering, you should go for it. Make sure that the problem you are trying to solve is important enough.

2

Do you have the necessary data?

When speaking about ML algorithms, we don't always mean we need lots of data. But usually, we do. And sometimes we don't need a lot, but the data must be appropriate, and it should exist somewhere in the world. Otherwise, there is no way we can help you. We can improve your data or train you on how to do that, but it will most likely be hard, expensive, and inefficient.

1

Do you really need this feature?

Make sure you are not following the hype. Can your business function without ML this technology? Could there be an automation solution that will bring 80% of the effect for 20% of the budget that you'd spend on ML? If there is any chance that your feature can be made using traditional software engineering, you should go for it. Make sure that the problem you are trying to solve is important enough.

2

Do you have the necessary data?

When speaking about ML algorithms, we don't always mean we need lots of data. But usually, we do. And sometimes we don't need a lot, but the data must be appropriate, and it should exist somewhere in the world. Otherwise, there is no way we can help you. We can improve your data or train you on how to do that, but it will most likely be hard, expensive, and inefficient.

1

Do you really need this feature?

Make sure you are not following the hype. Can your business function without ML this technology? Could there be an automation solution that will bring 80% of the effect for 20% of the budget that you'd spend on ML? If there is any chance that your feature can be made using traditional software engineering, you should go for it. Make sure that the problem you are trying to solve is important enough.

2

Do you have the necessary data?

When speaking about ML algorithms, we don't always mean we need lots of data. But usually, we do. And sometimes we don't need a lot, but the data must be appropriate, and it should exist somewhere in the world. Otherwise, there is no way we can help you. We can improve your data or train you on how to do that, but it will most likely be hard, expensive, and inefficient.

3

Do you have relevant expertise?

You should clearly see which intellectual functions you are trying to replace with AI. If you are not an expert in this field, neither are we. Subject matter experts (SMEs) are the true authorities who lay down the foundational knowledge upon which your model is built. And they must be on board with what you are trying to achieve. Otherwise, you'll face a lot of resentment. For example, not all math teachers are ready to cooperate in building an adaptive online math tutor, feeling threatened that AI will take their jobs away.

4

Do you have the finances?

You should realize that we are working with an iceberg. The question is, which part of it we see: is it 1/10 or 1/100? After the first R&D stage it may turn out that the data is excellent, but you could end up spending 10 times the amount than you have planned. The good thing, it will be probably worth it.

3

Do you have relevant expertise?

You should clearly see which intellectual functions you are trying to replace with AI. If you are not an expert in this field, neither are we. Subject matter experts (SMEs) are the true authorities who lay down the foundational knowledge upon which your model is built. And they must be on board with what you are trying to achieve. Otherwise, you'll face a lot of resentment. For example, not all math teachers are ready to cooperate in building an adaptive online math tutor, feeling threatened that AI will take their jobs away.

4

Do you have the finances?

You should realize that we are working with an iceberg. The question is, which part of it we see: is it 1/10 or 1/100? After the first R&D stage it may turn out that the data is excellent, but you could end up spending 10 times the amount than you have planned. The good thing, it will be probably worth it.

3

Do you have relevant expertise?

You should clearly see which intellectual functions you are trying to replace with AI. If you are not an expert in this field, neither are we. Subject matter experts (SMEs) are the true authorities who lay down the foundational knowledge upon which your model is built. And they must be on board with what you are trying to achieve. Otherwise, you'll face a lot of resentment. For example, not all math teachers are ready to cooperate in building an adaptive online math tutor, feeling threatened that AI will take their jobs away.

4

Do you have the finances?

You should realize that we are working with an iceberg. The question is, which part of it we see: is it 1/10 or 1/100? After the first R&D stage it may turn out that the data is excellent, but you could end up spending 10 times the amount than you have planned. The good thing, it will be probably worth it.

These are the things we recommend you keep in mind before starting an ML project. If you feel that some of the parts are missing, do not go there. Consider traditional software engineering instead

However if you think it might be worth risking, this means you have all the components in place. Each of them is your huge competitive advantage. Other companies in your industry are likely to lack at least one of them. Reports say that the majority of companies are actively working on a roadmap for handling data (68 percent), yet only 11 percent of these companies have completed this task. If you have the life-changing idea, the data, SME, and a budget, you are likely to fall into those 11 percent. If you are not sure, you can always reach out for help and ENBISYS experts will figure out the most suitable solution for your needs.

Need a helping hand with your future data science strategy? At Enbisys we can help you work on your roadmap for handling data. Contact us now for a free consultation!

Dmitry Bubnov

CEO, co-founder

Four Pillars of a Successful Machine Learning Project

Four Pillars of a Successful Machine Learning Project

Four Pillars of a Successful Machine Learning Project

CBDOe.krasnova@enbisys.com

CBDO
e.krasnova@enbisys.com