Opinion

Many companies have embraced machine learning technology to produce various machine learning solutions to solve their business problems. With the ever-increasing amount of data generated every day, Many companies use data to build intelligent solutions to enhance their business operations through automation.
Machine learning development follows a series of phases implemented in a machine learning project to create a business solution. These phases involve specifying the business challenges to be solved, data collection, data preparation, model training, model evaluation, and production deployment of any machine learning model.
When it comes to developing machine learning solutions, many companies take one of two approaches.
The first approach is to employ a group of experts and build a machine learning team to work on a company’s specific business concerns. To produce successful machine learning solutions, the team should have the appropriate Machine Learning skills and abilities.
Data Scientists, Machine Learning Engineers, Business Intelligence analysts, and other Machine Learning experts may be part of the machine learning team. This approach is highly recommended, particularly for long-term machine learning projects.
The second approach is outsourcing the development of the machine learning project to a contractor, freelancer, or an agency that provides machine learning services. The company signs legal documents (i.e. NDAs, etc.), to discuss the main project’s major goals and ideas with the outsourced team and while sharing the relevant data to develop the business solution.
In this approach, companies will not invest in much ML software or equipment necessary to do the project. The outsourced team with high-level experts in Machine learning will work on the project and deliver the solution to the client. This approach allows the company to save both time and money.
In this article, you will learn about the benefits of running your own machine learning team at work.
Why It Is Important to Manage Your Own Machine Learning Team
1. Full Control of Your Data
In this digital age, data is a new asset for any company. You might have some sensitive data for your machine learning project that you do not want to share with external suppliers. Exposing your confidential data can extremely lead to privacy issues, but having an in-house machine learning team can ensure that your data is kept private.

Your in-house team can use open-source MLOps to work on your projects, from data preparation to model deployment. The ability to own and manage open source MLOps technologies gives a company complete control over the machine learning project.
2. Having Domain Expertise to Work on Your Project
External suppliers may have domain knowledge, but they will not be on the same level as your in-house machine learning team. Someone who has spent a long time in a certain area of your company will have a better knowledge of the business challenge you are trying to solve with machine learning technology.
It is impossible to build high-quality models in any machine learning project without domain knowledge. We all know that feature engineering is an important part of machine learning implementation, and if you don’t have enough domain knowledge to work on feature engineering, your ML project will be at risk of failing.
"At the end of the day, some machine learning projects succeed, and some fail. What makes the difference? Easily the most important factor is the features used." – Prof. Pedro Domingos from the University of Washington
3. Easy to Handle Uncertain Requirements
When working on a machine learning project, it might be challenging to pinpoint the exact project requirements needed to solve your business problem with 100% accuracy. After exploring and analyzing the dataset you are working on, you may uncover new requirements (such as gathering more data for specific features).

As a result, setting those requirements for external suppliers to work on your machine learning project would be challenging because it will disrupt their Management process, as they have a tendency to work on a variety of projects for many organizations. It can also increase costs by requiring external suppliers to handle your additional requirements.
Uncertain requirements can be easily managed when you have an in-house machine learning team to work on your project. A company will also not worry about any new additional costs or any complications to implement the project successfully.
4. Better Project Management
External suppliers frequently work on many machine learning projects for different clients at the same time, which may cause some of the projects to fall short of expectations. As a result, this can lead to delays in delivering the solution to solve the business problem.

It is recommended for a company to have its own machine learning team because it will allow them to have complete control over the project, manage it effectively, and provide the solution on time.
5. Easy to Make Quickly Customization
Having a machine learning team in your company allows you to simply customize your machine learning solution so that it can quickly adapt to new business changes or needs over time. When you outsource, it can take a long time for the external suppliers to understand and execute new changes for your company.
Conclusion
Making a decision about how to implement a machine learning development process in your company is a difficult task that can be handled with the right approach.
If you want a quick consultation or proof of concept to see if a machine learning solution can bring value to your business, you can outsource a machine learning team. Then, once you’re certain that a Machine Learning solution is required, we recommend hiring a team of Machine Learning professionals to work on your business problem.
Even if you are certain that the external supplier is a suitable fit for your machine learning project, you must ensure that the external supplier is willing to collaborate with you on a long-term basis. Your employees can also supply the domain knowledge that an external supplier needs to fully comprehend the business challenge you’re trying to address and the dataset you’ll be using.
In this situation, you will be able to create a financially successful Machine Learning product. Otherwise, the company may end up squandering resources (money and time) on producing a service/product that only appears to be useful but it does not solve your customers’ challenges.
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