Why your company should outsource machine learning tasks

Machine learning (ML) used to be exclusively about productivity and cost. However, with evolvement of technology, experts have predicted that the use of machine learning in business operations will be twice as much compared to the past years as businesses recognize that machine learning helps companies save greatly in terms of costs and labor. Machine learning gives advantages for companies when it comes to problem-solving independently, refining customer experience, gathering large sets of data and giving valuable business insights, and tweaking business operations that can put the company into a competitive advantage.

It was predicted that in 2018, large and medium-sized enterprises will intensify their use of machine learning and that it will be doubled in 2020. Analysts have then predicted that machine learning will have stronger growth in investment and adoption globally. According to the forecast of the International Data Corporation, the growth is expected to rise from $12 million in 2017 to $57.6 billion by 2021.

Here are some considerations with regard to the benefits and risks of machine learning outsourcing and tips to further assist you in choosing the right partner.

Benefits of Machine Learning Outsourcing

High-Level Data Scientists. Implementing machine learning solutions involves broad subject knowledge and extensive experience for its complex developments. Therefore, having high-level scientists to work on ML projects is necessary. However, having in-house developers may be expensive whilst teaming up with service providers that cater to your requirements especially in data science projects is beneficial and easier.

Time Saving and Cost-Effective Development. Neural networks perform a lot of complex tasks such as categorizing information, decision making and having the ability to make future predictions based on data. These things can be quite challenging and time-consuming.  Hence, it is beneficial to have an outsourced team of developers that can speed up the process of neural network development. Having outsourced partners allows for a quicker building of teams and scaling them up once needed. It is also a fact that experienced outsourcing companies have exemplary necessary equipment and ML software, which lets you save from extra costs on it.

Better Resources to Provide ML Services. Having outsourced teams to work thoroughly when it comes to creating predictive algorithms for decision making is important. In-house team members may lack data science tools, especially when handling errors involving machine learning. Outsourced teams deem very helpful as they have access to a wide range of libraries and tools for collecting, exploration, preparation, and visualizing datasets.

Effective Data Management and Safety. Outsourcing developers for machine learning projects are helpful when it comes to securing sensitive information with regards to your company's data. Aside from that, implementing proper and systematic management, organization, and storage of data on various platforms is well taken care of when handled by experienced professionals.


Risks of Machine Learning Outsourcing

Communication Gap. One of the most common risks is communication pitfall. Broken lines of communication, especially in case outsourced staff is off-site, might be a probability. However, once communication standards have been established among teams in the company, conflicts may be prevented.

Data Security. Exposing sensitive organization data is one of the risks all companies need to be prepared for. In particular, it is the case when outsourcing or delegating tasks contain private materials. Thus, it is very important to make sure the provider of services you'll choose in handling sensitive data is trustworthy and reliable. Also, make sure to lay out security measures that each party agrees on upon the safety of data.

Project Management Failure. Outsourced teams or machine learning consulting companies have a tendency to take on many different projects for different businesses which can often lead to errors and delays in delivering requirements. It is important to establish a detailed timeline and agree on a reasonable deadline to avoid such conflicts. It is also crucial that you plan meetings regularly to be updated with the project's progress.

Lack of Domain Knowledge. A good quality of models in any project relies heavily on domain knowledge. If there's no adequate domain knowledge, feature engineering as the key component of ML implementation is at high risk of becoming a failure. It is important to make sure that specialists in the business niche field are present to avoid conflicts in the project.

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How to avoid risks of ML outsourcing?

Choose the right provider. It's key to understand whether the company you are choosing for their services has an ample amount of experience in delivering data science projects that match your industry niche and if it works well with the data storage systems that you currently have.

Sign the NDA. Having the legal documents that clearly state the terms and arrangements between your company and your outsourcing partner is important. It is wise to sign the non-disclosure agreement so that sensitive data is protected and make sure to claim ownership over the ideas and solutions to avoid any legal problems in the future.

Explain the main project goals and ideas. To ensure the success of your company's collaboration with your chosen outsourced team, it is important that you are both on the same page when it comes to ideas and business goals. Constant communication is a key player and it is best to have a regular schedule for meetings and an open line in case you need clarifications during the project.

Safety is the most important. Even though it is a collaboration, you should always be careful with the data and information you're sharing with your outsourced development team. Provide the information essential to the project to avoid safety conflicts in the future.

Keep abreast of the project. Management tools are very important in the success of your project. This also plays an important role in making sure that conflicts are prevented. By using project management tools, you can assign tasks, specify details, set deadlines, monitor work progress, and set up a meeting with each other.

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Final Thoughts

Machine learning development is one of the most prominent and effective approaches in business processes. That's why companies opt to implement ML and AI technologies in their businesses. However, there are challenges in terms of recruiting and managing an in-house team of ML developers and AI specialists as it is more expensive and time-consuming.

Outsourcing partners for machine learning consulting appear to be more cost-effective while delivering a higher quality of work compared to in-house teams. 

With that said, outsourcing machine learning is a smarter choice for machine technologies implementation given that outsourced teams have more tools, larger talent pools, and professionals with extensive experience and knowledge in the field of machine learning development.

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