Machine Learning (ML) Solutions: In-house vs. outsourcing

Machine Learning (ML) Solutions: In-house vs. outsourcing

Photo by Kevin Ku on Unsplash

Companies that want to start utilizing machine learning solutions typically have two options. They can decide to build an in-house team or decide to work with an external machine learning agency. Both these approaches have their pros and cons. Also, both require different resources and assets. In this article, we are going to take a closer look at machine learning solutions and compare the in-house team with outsourcing.

Let’s start our analysis by examining the in-house team option. Next, we will switch to machine learning outsourcing.

Machine learning solutions with an in-house team

Here, everything is cut and dried. You have to gather a team from scratch and give them everything they need to do their job correctly, starting with the IT infrastructure (and the data platform in the very center of it). What are the benefits of this solution? For starters, you have everything under constant control. You know every aspect of your project and can shape it according to your current needs and requirements. Secondly, the in-house team, by definition, works only for your company. Therefore, they can devote all their knowledge, experience, time, and resources to building your machine learning project. And thirdly, for many companies, the aspect of intellectual property is of paramount importance. With the in-house team, you can keep everything within your company. As a result, you can easily modify, develop, and monetize your machine learning solution.

Of course, as always, there are some downsides to this approach. First off, we have to mention the global IT workforce shortage. There are not enough IT specialists, and machine learning is no exception. This means that finding and hiring worthy candidates can be a challenging and time-consuming task. Secondly, you must have some expertise in machine learning yourself if you are about to build a machine learning team from scratch. You have to know:

  • What kind of expertise you’re actually looking for
  • What tools your team will have to use
  • What exactly do you want to achieve, and why

If it’s your first encounter with machine learning, there is solid research waiting for you. And the last downside worth mentioning relates to money. Full-time machine learning specialists usually make good money, and you have to pay them every month, whether there is work or not. So, if you’re planning on executing one project, the in-house team will be a waste of money.

Machine learning outsourcing–a flexible solution for many companies

Now, let’s talk about the second scenario–machine learning outsourcing. What can we say about this option? This time, we’ll start with the cons. For starters, working with an external company means that you’re just one of many clients they work with. As a result, you can’t expect full-time availability. Perhaps, executing your project will take more time, and there will be more obstacles along the way. Secondly, the machine learning agency will first have to get acquainted with your company, its goals, and products. Again, it takes some time. And thirdly, you have to take care of confidentiality and cybersecurity matters. After all, you work with a completely separate entity. Make sure your interests are properly protected!

And what about the pros? The first thing that comes to mind is that machine learning outsourcing is a very flexible solution. You pay just for the service and time, nothing else. This way, it’s also a profitable solution money-wise. You don’t have to build the entire ML infrastructure from scratch–your machine learning agency already has one. You don’t have to pay salaries for several months before you will be able to see the first results. And finally, you don’t have to worry about all the technicalities. Everything is in the hands of your machine learning agency. Secondly, machine learning projects are usually quite complex. Overseeing them can be a full-time job. With a decent machine learning agency, you have one contact person (typically a project manager) who regularly informs you about the progress of your project, so it’s much more straightforward. And lastly, you don’t have to worry about workforce shortage. Your machine learning agency already has all the professionals they need to finish your project.

If it’s your first time with machine learning, or you’re simply looking for a flexible, profitable solution–machine learning outsourcing is your best bet. In many instances, you will pay less than in the in-house model. Plus, you don’t have to worry about every stage of the project. From many perspectives, this solution is better! And if you’re looking for a trusted machine learning agency, we invite you to contact Addepto. We’ve been developing machine learning projects for over 10 years now. We have all the necessary expertise and infrastructure to devise, develop, and maintain your machine learning solution. Drop us a line to find out more!

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