Bespoke vs Off-the-Shelf Machine Learning: Which One is Right for You?

boxed vs bespoke machine learning

Have you found answers to your most burning questions about machine learning (ML), identified several practical use cases in your business domain, and decided that investing in machine learning is actually a good idea? Now when you have financial approval to go ahead with ML – what’s next?

It’s time to decide what will be more beneficial for your business – bespoke machine learning development or off-the-shelf ML solution?

While this may largely depend on your finances, the state of your data, and the results you expect, we’ve put together this blog to take a look at what you can expect from each type of machine learning.

Bespoke Machine Learning

Custom ML is built solely for your business to identify the most critical questions and answers. Your bespoke software provider will examine your data in detail and help determine what you want to get from implementing machine learning. Then they will come up with a solution that suits you best from many perspectives such as costs, customer loyalty, time to market, etc.

Off-the-Shelf Machine Learning

Large suppliers usually provide boxed ML capabilities to a wide variety of organisations. They come with pre-built integrations, so getting started is pretty quick. However, you will be provided with generic algorithms that do not suit your specific needs and goals.


Bespoke vs Off-the-Shelf Machine Learning

The table below compares custom and off-the-shelf ML capabilities from the following perspectives: specificity, scalability, maintenance, data integration, durability, costs, and development time.

Bespoke ML Off-the-shelf ML
Specificity Algorithms and models are designed specifically for your most sophisticated needs and allow you to answer your most critical business questions more seamlessly. Generic features and algorithms with limited to zero specificity for your business or niche.
ScalabilityThe models can be scaled and improved as your business grows and matures.New algorithms can only be utilised when they get platform updates, and they still be highly-irrelevant to your unique business case.
Maintenance and supportYou have a dedicated team/resource to maintain your ML project and address errors and issues immediately while minimising impact on your business and/or customer data.You’ll be assigned an account manager who deals with dozens or hundreds of other client tickets and requests. You’ll have to wait in queue for quite some time to get the required support, which can have a drastic impact on your users and system performance.
Data integrationAll of your current data will be integrated through bespoke API development.You’ll only be able to use prebuilt APIs so the chance is slim all of your current data will be integrated seamlessly. You’ll need to invest in bespoke workaround (a.k.a. kludge) development anyway.
DurabilityAs ML models can be scaled or customised depending on your particular needs, your ML investment will have a long-term impact on your business success.Due to limited scalability, there’s no guarantee of durability. Nor will you be able to accumulate sufficient ML knowledge in-house for future projects.
Development timeYou need to take time to explore and identify correct algorithms to get the best value for your ML investment.Models are available instantly, just plug in your data and play with it.

Does It Make Sense to Outsource Bespoke Machine Learning?

A couple of years ago, machine learning (ML) was all about performance and cost. However, as technology advances, experts predict that the use of machine learning in business operations will double compared to past years. That’s because more businesses recognise now that machine learning helps companies save significant costs thanks to human labour automation and higher quality of the outcome. 

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Machine learning gives companies the edge when it comes to solving problems on their own, improving customer service, collecting large datasets, delivering valuable business insights, and adjusting business operations that can give the company a competitive advantage.

It was predicted that in 2020, large and medium-sized enterprises would double the use of machine learning on their bespoke software development projects. Today, analysts expect that machine learning will see more significant growth in investment and adoption worldwide.

“Spending on AI and ML will grow to $57.6 billion in 2021,” IDC.

While large enterprises can afford to build bespoke ML solutions in-house, SMEs and startups are typically limited in their ML development and implementation capabilities. They lack internal AI and software engineering talent and/or project management (PM) resources; nor can they afford high costs and a long time to source developers and build a team in-house, etc. In this case, bespoke ML outsourcing can be a viable option for companies looking to jump fast on the AI tech bandwagon without paying a fortune for this.


Here are some considerations regarding the benefits and risks of outsourcing machine learning, as well as tips to help you choose the right partner.

Machine Learning Outsourcing Benefits

Access and hire high-calibre data scientists and software developers for half the price you’d pay in the UK/Western Europe/North America

Implementing machine learning solutions requires extensive domain knowledge and extensive experience in complex development. Hence, it is necessary to have highly qualified data scientists to work on machine learning projects. Having in-house developers can be costly; however, partnering with service providers that meet your requirements, especially on data science projects, is cost-effective and more effortless.

Outsourced teams usually have access to a wide range of libraries and tools to collect, explore, prepare, and visualise your datasets.

Enjoy time-saving and cost-efficiency

Neural networks perform many complex tasks such as categorising information, making informed decisions and data-based predictions for the future. This can be quite difficult and time-consuming. Hence, it is useful to have an outsourced ML development team to speed up the neural network development process. Engaging external partners allows you to build teams and scale them as needed quickly. In addition, experienced outsourcing companies typically have extensive know-how. They can help you save costs by using reusable components from their code libraries as well as pre-built templates and non-trivial workarounds.

Manage data more effectively in a highly-secured environment

Outsourcing machine learning project development makes sense when it comes to protecting your company’s and/or your customers’ sensitive data. In addition, experienced professionals will take good care of implementing proper and systematic management, organisation, and storage of data across different platforms.

Machine Learning Outsourcing Risks

Like any other business venture, outsourcing ML development can be risky, namely:

Communication gaps

One of the most common risks in outsourcing is communication pitfalls. Outsourcing providers that fail to build effective virtual communication systems and protocols with customers are unlikely to deliver appropriate value for your investment. Poor communication results in the wrong choice of architecture or methodology as well as incorrect evaluation of team performance, which translates into additional overheads, lost customer loyalty, and other detrimental things that can screw your entire project.

Once the right communication standards are established between the customer and their extended teams, conflicts, and issues can be prevented.

Data security

The disclosure or leak of confidential business data is one of the risks that all companies must be prepared for when outsourcing. This is particularly the case when outsourcing or delegation tasks contain proprietary materials. Thus, it is imperative to ensure that the service provider you choose to handle your machine learning and sensitive data is trustworthy and reliable. Also, be sure to outline the security measures that each party agrees on regarding data security.

Project management errors

Machine learning consulting and/or bespoke software development companies tend to take on many different projects from different customers, often leading to human errors and delays in meeting requirements. It is important to establish a detailed timetable and agree on a reasonable time frame to avoid such conflicts. It is also vital that you schedule meetings regularly to keep abreast of the progress of the project.

Lack of knowledge in the subject area 

The quality of models in any project largely depends on the knowledge of the subject matter. If you do not have sufficient knowledge in the domain, the development of functions as a key component of machine learning implementation runs the risk of failure. To avoid conflicts in the project, it is important to ensure that specialists in your business niche are involved early on.

How to avoid risks of bespoke ML outsourcing

Choose the right provider!

We keep repeating this “mantra” in nearly every single blog we publish. It’s key to understand as early as possible whether your prospective ML development partner has sufficient experience in implementing data science projects that are relevant to your industry or whether it performs well with the storage systems you currently have.

Sign the NDA

It is important to have legal documents that clearly set out the terms and agreements between your company and your outsourcing partner. It’s wise to sign a nondisclosure agreement (NDA) to keep sensitive data protected, and be sure to claim ownership of ideas and solutions to avoid any future legal problems.

Establish effective communication with your ML dev partner

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

Project management tools are critical to the success of your ML project. It also plays an important role in conflict prevention. Using PM tools, you can assign tasks, provide details, set deadlines, track progress, schedule appointments, and more. 

Focus on cybersecurity

Although this is a collaboration, you should always be careful with the data and information you share with your external development team. Make sure to hire a security consultant to evaluate your current level of security and help you put together a cybersecurity roadmap for your data project to ensure it’s well protected across an entire SDLC.

Final Thoughts

Machine learning development is one of the most promising and effective approaches to business process automation. This is why more and more companies today choose to integrate machine learning and artificial intelligence technologies into their businesses. However, there are challenges in recruiting and managing an internal team of machine learning developers and AI specialists. It is pretty expensive, time-consuming, and requires a high level of company maturity.

As such, outsourcing machine learning can be a smarter choice over using off-the-shelf platforms, given that outsourcing teams have access to robust tools and technologies and a larger pool of talent to source from. They also boast lower rates of development compared to your home country.

Do you want to learn more about our bespoke machine learning capabilities and why choose Evolve as your long-term ML outsourcing partner? Drop us a line or call us at +44 116 298 7460 and let's talk business!

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