AI Outsourcing and In-House Development: Choosing the Best Approach for your next Machine Learning Project

AI Outsourcing and In-House Development: Choosing the Best Approach for your next Machine Learning Project

Mateusz Łach - January 10, 2022

Companies hoping to gain a competitive advantage over their competitors in today’s ever-evolving marketplace must increasingly turn to artificial intelligence. However, with the dynamic rise of various opportunities and development approaches, it can be more complex than ever for a business to decide between outsourcing AI software development or keeping it in-house.

Whether following the outsourcing or in-house development approach, the success of any project is contingent on the team’s expertise, which is also true for AI development. Depending on the scale, goals, features, and budgetary constraints of the project, a company must choose the most convenient and efficient model for software development.

This article will explain the importance of hiring specialized domain experts in AI development and highlight the pros and cons of AI and machine learning outsourcing, hoping to help you make an educated decision.

The Epidemic Flips the Script

The COVID-19 epidemic threw everyone into remote mode overnight and did so without a proper warning. And while many people don’t really like the change, there is no denying a vital paradigm shift has happened. Today, all work is remote, and every developer is a remote worker now. Other than that, it turns out its business as usual – companies seek the best talent, and the best talent is not always at their fingertips.

If there is anything good to say about COVID-19, it helped eliminate many typical concerns about outsourcing and urged many businesses around the globe to look into its multiple advantages.

What Is Outsourcing AI Project Development?

What Is Outsourcing AI Project Development?

By definition, software development outsourcing involves teaming up with a skilled external provider – ideally with previous experience working on a project (or many projects) similar to yours.

Because outsourcing eliminates the painful process of hiring and maintaining an in-house team of experts, it is known to reduce IT costs while allowing access to top engineering skills. By outsourcing your product to a skilled team with proper domain expertise, you can shorten your product’s time to market and gain many other advantages.

Today, software development outsourcing is a common tactic for many tech businesses, from startups to enterprise companies. But with all its advantages, many misconceptions have arisen around this approach. One of them, for example, is that outsourcing is cheaper. While this may be true, money it’s not what makes outsourcing attractive (not to mention we favor the term “cost-effective”).

The Advantages of AI Development Outsourcing

There are many benefits to outsourcing, and some of them are often overseen – especially in AI and machine learning projects. Let’s look at some of them.

Instant Access to Domain Expertise

Building and launching a successful artificial intelligence and machine learning application is tedious and complicated. Due to the shortage of skilled professionals, hiring qualified and dedicated developers for IT projects has become challenging, especially when it comes to data scientists and machine learning engineers. And that’s not only true for small startups struggling with hiring skilled talent for their projects. Even big tech companies (go out of their ways to acquire promising AI startups in bulk.)[https://www.statista.com/statistics/943232/ai-startup-acquisitions-by-company/]

It takes a team of dedicated AI specialists to build a successful and cross-functional AI development team.
It takes a team of dedicated AI specialists to build a successful and cross-functional AI development team.

There has been a growing demand for data scientists and AI experts for the past decade, while domain talent is invariably in short supply. Hiring and training data science staff are thus costly as recruitment processes drag for months and don’t guarantee success. Outsourcing is seen as a much-needed remedy – it makes it possible for businesses to find and choose experts based on the requirements of their machine learning projects.

Domain expertise cannot be underestimated. An external team with artificial intelligence and a machine learning background will be an undeniable benefit, increasing success chances. This is especially important for complex projects where know-how is essential. A team with a proven track record in AI will add plenty of value to your project. Partnering with a dependable external provider offers the chance to leverage top-tier technical expertise in your project.

Instant Access to Artificial Intelligence and Machine Learning Tooling

Even the most experienced in-house team may lack access to specialized data science tools. Machine learning outsourcing gives you access to domain expertise and a wide range of libraries and tools for collecting, exploring, preparing, and visualizing datasets. This may make a massive difference for the project.

Instant Access to High-Quality Data

Instant Access to High-Quality Data

Building machine learning and deep learning models requires high-quality data. Even if your organization has access to clean, large-scale data relevant to the model, working with big data is time-consuming and requires experience.

Proper data preparation and cleansing are crucial for AI project development. Suppose your organization rarely collects and/or stores customer and other transnational data. In that case, it will take a while to get started and see any fruitful results of your AI project development.

Collecting and working with large datasets requires experience and time. To make a machine learning platform that works to your advantage, you need to feed it with your own high-quality data sets – and this takes time. It takes a lot of effort to ensure that the platform’s predictions are good. Many off-the-shelf AI solutions offer lower-quality predictions for specific data cases but excel in areas like natural language processing. Logistics, medicine, finance – specialization is critical.

AI Development Outsourcing Shortens Your Time to Market

Time equals money, and hiring a dedicated AI team from a technology partner can bring results faster than fostering and hiring an in-house AI team.
Time equals money, and hiring a dedicated AI team from a technology partner can bring results faster than fostering and hiring an in-house AI team.

Somewhat as a knock-on effect of better access to quality data sets, outsourcing is a great way to speed up the development and deployment process. Just like every software development agency out there, AI developers usually specialize in specific areas or applications. By partnering with a service provider that has previously built similar models and has the relevant experience and experts, you’ll save precious time struggling to get your in-house teams up to speed.

Outsourcing Can Be Cheaper Than In-House Development

While intuition suggests that building an in-house IT team is less expensive than partnering with an external provider, there is evidence to the contrary. Once you factor in all salaries, hardware costs, software licenses, and cloud service subscriptions, the price tag gets hefty. Not to mention there will be additional maintenance costs and all the expenses required to keep your team abreast of the current industry trends (e.g., through training, workshops).

These invisible costs explain the growing popularity of outsourcing, where prices are more predictable – and investors love predictable ROI – instead of splurging on an in-house team that’s not always fully used, funds can be directed where they make an actual difference.

Outsourcing Is a Remedy for Invisible Costs

When hiring AI experts for your in-house team, you need to consider many “invisible” costs beyond your employees’ wages and workstations – from recruitment to software licenses, training and employee benefit, their tea and coffee, holiday and sick pay, etc. This also means time spent searching, interviewing, recruiting, and onboarding new employees. While these costs should be expected in recruitment processes for every development project, AI experts are especially hard to hire, exacerbating the problem. All the prices and associated time spent hiring new employees will no longer be your worry by deciding to outsource. When building an application, inexperienced businesses tend to overlook some expenses. In the outsourcing model, the agency should bear a big part of the burden connected with building, deploying, and maintaining the ML model, bringing time and money savings in return.

High Availability

Reliable and experienced outsourcing companies offer you the flexibility to use teams of dedicated AI and machine learning experts – right when you need them. As an employer, you don’t need to spend time hiring extra staff or training new hires. Instead, developers become part of your project from day one.

An in-house team is prone to disruptions due to sickness, vacations, or unforeseen circumstances. Outsourcing takes the burden of dealing with such obstacles off your shoulders, thus guaranteeing uptime and smooth operations.

Scalability and Flexibility

Scalability and Flexibility

To remain competitive, fast-growing companies need to be able to scale dynamically to match the market’s demands, which is especially difficult for specialized AI and ML projects. By rigidly sticking to the decades-old “in-house team” approach may turn out to be expensive and simply impractical, especially if there is no feed of challenging, specialized projects to fill their backlogs.

Secondly, in-house ai development teams are usually fixed in size. Few companies have all the resources required to implement all the in-house systems and services. By outsourcing IT activities, smaller companies can compete with more prominent players without compromising on the quality of the final product.

Expanding development teams to meet the project’s demands can turn out excessively costly. By partnering with an external provider, you allow yourself the comfort of scalability with no additional cost on your end and can seamlessly expand to cover your needs. You can also work concurrently with different outsourcing companies to – according to their expertise – meet the diverse ML needs of your project.

More Focus On Your Core Business

It’s easy to get distracted when juggling different activities relevant to the company’s operations but outside of the core business goals. Outsourcing is the answer to this problem, helping business managers to focus their energy on areas within their competencies. By teaming up with a seasoned AI development team, you gain the precious time to focus on your core business.

How to Choose the Best Service Providers?

Building an in-house AI/ML team is challenging in itself, but hiring an external contractor can be just as tricky without knowing what to look at. To choose the best vendor, you should make sure they tick the four key boxes.

Portfolio and References That Support Their Domain Expertise

Building AI solutions requires domain experience, intelligence, and a particular combination of skills to create algorithms that leverage machine learning. Remember to check your future partner’s portfolio of AI projects. Ideally, you’re looking for an agency specializing in big data, data science, AI, machine learning, and many relevant projects under their belt. AI vendors should have experience in your business’ industry or business objective and have expertise in machine learning technologies.

Portfolio and References That Support Their Domain Expertise

Their past projects, client references, and success stories are a testament to their expertise and should be among the primary deciding factors for you. To ensure the best fit, look for an agency with experience in your industry.

Team Members

To make sure the service provider is a match, you can do an additional check of each individual team member’s background. Usually, a review of their LinkedIn profiles should be enough. However, if you’re after more information, it’s a good idea to scour their GitHub profiles and make sure if the actual experience in the field backs their claimed expertise.

Why Outsourcing AI Development Makes Sense

While not rid of its risks, there’s no denying outsourcing is a reasonable way to leverage AI and accelerate the growth of your business.

Due to the specific limitations and challenges connected with recruiting and managing in-house ML and AI experts.

Outsourcing is simply a smarter choice for machine learning projects – agencies offering specialized machine learning consulting and development services prove more cost-effective and have specific domain expertise and tooling.

Are you looking for an experienced IT outsourcing company? Are you looking for custom predictive analytics software and ways on how to adopt AI? Drop us a line – our experts will be happy to discuss your project and the possible ways to implement it.

About the author

Mateusz Łach

Mateusz Łach

AI & Digital Business Consultant

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Mateusz is a digital strategist and innovation enthusiast. He enjoys building new products and concepts, often with the help of AI. Mateusz joined Nexocode with the mission to consult startups, mid-size companies, and enterprises on their digital transformation journey and help them benefit from custom artificial intelligence solutions.
Responsible for overall business development and sales activities. A geek of new technologies.

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