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Why should you outsource machine learning tasks?

Artificial intelligence consulting services (AI) and machine learning (ML) solutions are becoming increasingly popular among small and large enterprises. These solutions help extract valuable insights from large amounts of data collected by companies worldwide and are of great help in boosting employees’ productivity, enhancing the decision-making process, and automation of diverse day-to-day processes. Many companies utilizing ML solutions decide to outsource the machine learning tasks and work with a trusted external partner.

What are the crucial benefits of this business model? Let’s find out!

Adversarial Machine Learning

Unluckily, despite the tremendous benefits machine learning offers, implementing this technology can be difficult for the vast majority of organizations, especially those that do not have a thorough knowledge and understanding of AI and data science capabilities. That is why most firms face the question of whether to build an in-house team of ML specialists or outsource their ML projects to an external company specialized in machine learning and AI consulting.

Benefits of machine learning outsourcing

We want to show you four significant advantages of machine learning outsourcing. Let’s examine them closer:


ML engineers teach and train intelligent algorithms by putting large datasets into them so that they operate like humans. Such training, in order to be successful, needs to be comprehensive and consistent. Before machines can mimic human work, they need to be fed with extensive amounts of data on a daily basis. By outsourcing this process, your algorithms can understand problems and analyze data without slowing down the related processes.

ML algorithms’ abilities include:

  • Categorizing information
  • Improving the decision-making process
  • Making predictions for the future

Today, ML algorithms and apps can be used to execute diverse tasks. However, you have to consider that building such an algorithm from scratch is a challenging and long-term process. That’s why, when working with an external ML partner, you can rest assured that you have experienced specialists who deal with machine learning daily working on your project.

The outsourced team of ML developers can effectively speed up the process of the development of ML algorithms. Outsourcing tasks related to neural networks and machine learning will allow your business to save time and money since while the outside team works on machine learning, in-house employees can freely focus on their primary objectives. What is more, contract workers usually have their own equipment and software. This way, you don’t have to spend time and money on the provision of additional software and on-site materials.


There are many ML projects that require 100% accuracy.  However, it is considered a rookie mistake when in-house teams expect ML algorithms to always make accurate predictions without considering the limitations of this technology. Part of the machine learning process includes allowing room for an error. As the new information is uploaded into the system, the machine learning algorithm has to be properly trained first. In this process, outsourced teams look closely at the AI analysis results and create the predictive algorithms that suggest the best decisions.

One of the most common issues encountered by internal teams working with AI is the lack of time to upload large volumes of data into machines so that systems are able to learn and perform their daily tasks. Without the complete and precise data sets, AI tools will produce useless results that cannot be used to draw any conclusions. That is why outsourcing companies make sure that they can devote enough time to enter vast amounts of specific data required to develop a decent ML system.


As we have already mentioned, machine learning algorithms can be used to predict future situations and scenarios. This capability includes foreseeing the customer behavior based on their past habits and activity. It can be done, for example, by suggesting products in online shops or movies on streaming platforms.

When you decide to outsource machine learning projects, you can easily concentrate on your project’s UX and customer service parts. As your external partner deals with the technicalities, you can ensure that the ML algorithm is useful from the customer’s standpoint. And there’s the testing part.

Testing is a necessary step applied to confirm that the ML algorithm correctly interprets the data and operates as expected. Without the proper testing part, you won’t be able to assess your algorithm’s efficiency and usefulness. Testing is what makes your project fully usable in the specific conditions of your company and industry. An external ML company ought to fully understand the need to perform thorough tests before launching the algorithm or the ML-based app.

Generally speaking, outsourced ML consulting companies are more experienced in the introduction of AI into diverse systems and applications. That is why enterprises like yours can benefit from choosing outside players to perform such tests. By doing so, you can minimize the risk of making testing errors and will significantly reduce the time spent on such activities.


An external ML company can also help you create an intuitive analytics module that’s based on the analysis of:

  • User information
  • Web page views
  • Search queries
  • Time spent on a website by every individual user

In the vast majority of machine learning projects, analytics features are extremely important. They help you assess your activities and predict future events. External ML companies and agencies will help you make the most of the ML-based analytics in your company.

Machine Learning And Artificial Intelligence 

ML and AI play a vital role in your digital acceleration. These digital technology elements intertwine to reduce the burden and lengthy results of traditional ways of doing business. 

ML has become a helpful tool for both human and AI decision-making. AI makes intelligent systems perform tasks like humans, whereas ML teaches machines with data to do a specific task and provide an accurate result. With the advancement of information technology (IT), many technological solutions have emerged stemming from ML and AI.

While you choose to outsource your machine learning tasks, it’s also a good idea to consider adopting robotic process automation (RPA). Or at least, outsource your ML tasks to a company that uses RPA to come up with the best results. 

Robotic process automation is the latest trend in automation technology that relies on a scalable, digital workforce. This technology streamlines manual processes and reduces the burden on employees because software bots can do repetitive tasks more accurately and efficiently. 

Using RPA is like outsourcing manual tasks to intelligent software bots. RPA can communicate with your business applications and systems like a human being can perform. Adopting this technology can boost your productivity by freeing your employees to do more strategic work to add value to your business. Learn more about robotic process automation at https://www.helpsystems.com/solutions/automation/robotic-process-automation.


To sum up, outsourcing machine learning projects and endeavors is beneficial from many perspectives. It helps you make the most of machine learning, save time, costs, and workforce. It also enables you to focus on your customers and their needs.

External companies usually have broad experience with diverse ML and AI projects so that you can work with them no matter what your project is based on. In many instances, external companies prove to be more flexible and efficient, especially compared to full-time specialists and engineers. If you want to find out more about machine learning outsourcing and AI consulting, visit Addepto.com for details.

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