Machine Learning Outsourcing: The Smart Way to Scale Your Business

Machine learning is a complex yet ripe-for-opportunity field that evolves at an incredible speed. It seems like the future belongs to smart ML-driven systems, but the path to get there is thorny and quite expensive. You see the headlines about its transformative power, but uncertainty about the new technology and its fit for your business sows seeds of doubt. But what if there was a way to experiment with ML without breaking the bank or getting lost in the technical weeds? Yes, we talk about machine learning outsourcing.

Outsourcing strategy for IT, software development, cybersecurity, and a number of other business processes has proved beneficial to nearly 83% of businesses that outsource. Machine learning and artificial intelligence outsourcing is a smart and cost-effective way for large and small businesses to understand the technology’s potential to improve their operations and drive growth. The proposition to build an ML product with the help of an external team seems lucrative, but you’re not sure and have some concerns. That’s why we offer you to learn about all the nuances of ML outsourcing, the positive and the negative, to understand whether and why you need it.   

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What is Machine Learning Outsourcing?

Machine learning outsourcing is the practice of hiring an external agency to handle the development, deployment, and sometimes even maintenance of your ML projects. You can outsource only certain ML tasks (such as data exploration or model development) or the full range of tasks within the ML lifecycle.   

Here’s a breakdown of the concept:

• You have a business problem or opportunity you believe ML technology could address.
• Instead of the creation of your own internal team of data scientists and engineers, you partner with specialized machine learning services companies.
• The company takes care of all ML development tasks, from data preparation and model development to deployment and continuous support.
• You benefit from their expertise, established tools and infrastructure, as well as a team of high-level ML experts, all at a lower cost than internal team recruitment.

Think of it like this: You wouldn’t build a power plant to run your office lights, would you? When you outsource machine learning projects, you can leverage the expertise of specialists without the overhead of team creation. 

Types of Machine Learning Outsourcing Services

Since ML technology applies to virtually any field due to adaptable algorithms, the variety and scope of ML projects can be diverse. That’s why machine learning services companies offer different types of services to meet the needs of different projects. So, what are your options here?   

Custom Machine Learning Model Development

This service takes your specific problem and develops a one-of-a-kind model to address it. A reliable IT outsourcing services partner will support you throughout the entire process and help you with problem definition, as well as model development, deployment, and beyond. Custom ML development is the best way to make sense of your large sets of data and build a highly targeted solution that will give you a huge competitive advantage. Yet, be ready to pay a premium for the expertise and time required to build custom ML or AI models.

Integration of Pre-built Machine Learning Models

If you need a faster and more cost-effective solution for a certain task, pre-built model integration might be your answer. Pre-trained ML models are ready-made solutions that handle common tasks (image recognition or sentiment analysis) without the need for custom development. Needless to say, a solution built with the help of a ready-made ML model will be less resource and time-intensive. A service provider you will choose for collaboration will assess your needs and offer the most suitable pre-built model. This option is ideal if you have a well-defined problem that can be solved with readily available pre-trained models.

Data Annotation and Preparation Services

Data is the fuel that powers ML models. However, raw data often needs to be cleaned and labeled before use for optimal model performance. This is an obligatory phase of ML system development, regardless of whether you choose custom development or pre-built model integration. Data preparation requires decent skills in database management systems, data manipulation libraries, and other technologies to remove errors, format data, and label it properly so that the model can learn from it to produce accurate results.    

Why Outsource Machine Learning?

ML technology can advance any business process across any industry. It’s fast, applicable almost anywhere, and learns on its own without human involvement. Yet, we suggest that you already know about it and are more curious about how to integrate ML into your business in the most risk-free and effective manner possible. Here, business leaders have to weigh the pros and cons of in-house vs. outsourcing development to make a well-informed decision. 

But, given the scarcity of data scientists and ML engineers in the US, UK, and Western Europe, the creation of your own ML team is as futile as searching for a unicorn on Wall Street. The demand for such specialists far outstrips the supply. Almost two-thirds of decision-makers report talent shortages in AI fields. That’s one of the reasons why many companies choose to outsource machine learning development, but far from being the only:

Focus on Core Business Functions

When you outsource ML projects, you free up your internal team to focus on what they do best – run your core business. You don’t need to spend time, effort, and money to find, recruit, train, and manage data scientists; your tech partner handles it all. This lets you and your internal team concentrate on tasks that drive revenue and growth.

Access to Global Talent and Advanced Technologies

ML evolves at a rapid pace, and top talent is scarce. IT outsourcing companies often have access to a global pool of qualified data scientists and engineers with diverse skill sets in computer vision, natural language processing (NLP), and the best programming language for ML – Python. So, you benefit from the latest expertise and top-notch technologies without the limitations of geographic location.

Cost-Effectiveness and Scalability

The in-house ML team requires significant upfront investment in infrastructure (powerful servers and data storage), specialized ML and data analysis software, and highly skilled personnel. Just approximate how much can it cost! Quite a lot. From this perspective, machine learning outsource service is a more cost-effective alternative that lets you pay only for the services you need. Plus, this strategy is beneficial as you can easily scale the resources up or down to meet your project’s demands even as they change along the way.

Faster Time-to-Market

Professional AI and ML development services companies have established workflows and expertise that allow them to build and deploy ML projects faster. Even if you manage to create an in-house team of experts (which is a true challenge nowadays), they would need some time to develop similar processes and gain the same level of efficiency and experience. So, outsourcing allows you to get your project up faster and launch it quicker. And being first to market with a new ML-powered solution can give you a huge advantage.

How to Outsource Machine Learning Effectively

So, you firmly decided that machine learning outsourcing is what you need, but where do you start? Here’s a brief 4-step overview to help you establish a successful partnership. 

Step 1. Identify the Right Machine Learning Tasks for Outsourcing

Not all ML projects are the same. Some tasks, like data collection and cleaning, are easily outsourced. Others, like core algorithm development, might require in-house expertise due to their strategic importance and need for deep domain knowledge. The point is you should analyze your project and identify the tasks better suited for external expertise, particularly those that demand specialized skills or much of computer power. 

Step 2. Choose a Credible Outsourcing Partner

The emphasis here is on “credible”. There are quite enough machine learning services companies, but you need to find one with the ideal blend of expertise, methodologies, and cultural fit to match your needs. So, to choose the right service provider and hire AI engineers, take into account these factors:

Expertise and Experience

You want a partner with a proven track record in your industry and a team who are professionals in ML and related disciplines. A strong team should include experienced data scientists and engineers with a deep knowledge of the specific algorithms and techniques relevant to your project needs. Feel free to ask for detailed information on their past projects and areas of specialization.

Technology aptitude is important but not the only criterion. Check their experience working with similar-sized companies to yours and how they tackled comparable challenges, as these are valuable add-ons for positive project outcomes.

Pro Tip from Anna Dziuba, VP of Delivery

Communication and Collaboration

Evaluate the potential partner’s communication style and ability to articulate technical concepts clearly. How actively are they involved in the discussion, and how easy are they to communicate with? Assess their responsiveness and openness to feedback. A smooth rapport and transparent communication from the provider are a great deal in product development that will ensure your project stays on track and no misunderstandings are left.

Schedule regular meetings to discuss progress, address concerns, and ensure alignment with your goals. Also, you may conduct a trial project to assess compatibility before you commit to a larger engagement.

Pro Tip from Nataliіa Dynka, Business Project Manager

Security and Confidentiality Protocols

ML projects often involve sensitive data, so you should find a company that prioritizes data security and adheres to strict confidentiality protocols. Ensure they have robust security measures to protect your data from unauthorized access or breaches. Ideally, they should have certifications like SOC 2 or ISO 27001, which show their commitment to data security best practices.

Step 3. Establish Clear Goals and Expectations

Clearly define your goals to let everyone know what you want to achieve through this ML project or what problem you are trying to solve. It would also be great if you could define the metrics you use to assess the project’s success. Communicate these goals explicitly to your chosen partner. Develop a detailed project scope with deliverables, timelines, and costs so your outsourced team is clear about what you expect at the end.

Step. 4. Implement Management and Communication Strategies for Outsourced Projects

Successful outsourcing depends on how well communication flows between you and your partner. For this reason, clarify what communication channels will be used for what purposes and set regular meetings where you will discuss progress and address roadblocks. Also, it’s really helpful to use project management tools, which allow you to track progress and maintain transparency with remote teams. 

Challenges in Machine Learning Outsourcing

The solution that promises to fix all your problems and has only advantages without any pitfalls raises suspicions. There are no such universal solutions, at least in the IT world. So, while many companies agree that machine learning and artificial intelligence outsourcing are beneficial, you should be aware of some challenges you can encounter.

Data Security and Privacy Concerns

Since ML development deals with tons of data as well as sensitive data, security and privacy are the biggest concerns. In fact, sharing information with an external partner always carries risks. To mitigate this risk and make sure your project and data are in safe hands, select a development company that follows the best industry security standards and has relevant certifications (ISO 27001, SOC 2, etc.), not to mention NDA (non-disclosure agreement) and other protection protocols. Another critical point is to have data ownership and usage protocols outlined in your contract. 

Quality Control and Oversight

When you opt for machine learning outsource services, it can be tricky to maintain high-quality standards, but that doesn’t mean you should relinquish all control. Here’s how to stay in the driver’s seat: 

• As we already mentioned, define success metrics upfront and establish regular communication channels to monitor progress.  
• Utilize project management tools for enhanced transparency. 
• Conduct code reviews and hold periodic quality control checks to ensure the work meets your standards.

Cultural and Language Barriers

Outsourcing means you can find a tech partner from any place in the world; therefore, you might face challenges such as cultural differences and language barriers, especially when you outsource to regions from the other side of the planet. Certainly, it will create roadblocks. In this regard, nearshore software development may be the best variant as it offers a broader talent pool compared to hiring locally yet still maintains closer time zones and cultural similarities, which can facilitate better communication and collaboration. A team should also have strong English language skills and experience working with international clients. 

Intellectual Property Rights

When you outsource, there’s a risk that your proprietary information or innovations could be compromised. To protect your IP, draft a clear and comprehensive intellectual property agreement with your outsourced company. It should explicitly outline ownership rights for any models, algorithms, or discoveries made during the project.

Best Practices for Successful Machine Learning Outsourcing

Fruitful collaboration, regardless of the type, requires effort and commitment from both parties. So, here are some unwritten rules to follow to make your cooperation smooth and productive.

Comprehensive Due Diligence on Potential Vendors

Don’t just pick a name out of a hat! Do your homework and research potential candidates. Key factors that will help you pick a credible provider we outlined above. To sum up, that’s experience in building solutions for your industry, expertise and tech knowledge of their specialists, and a portfolio with projects similar to yours. You can dig deeper: check references, assess their security protocols, and ensure they align with your company culture.

Strong Non-Disclosure Agreements Covering All Aspects of the Project

Your data is the fuel for your ML outsourcing project, and it needs to be properly protected! Besides the actual data for the ML model, you may need to share proprietary information and business strategies with a development company to let them better understand your needs. So, initiate a rock-solid NDA before you share any sensitive information. This agreement should cover everything from data to algorithms and intellectual property to guarantee your confidential information remains just that – confidential.

Effective Project Management and Agile Methodologies

Project management is another critical aspect that handles the organization, planning, and execution of your project. Generally, an AI development company manages a project. They assign a project manager who oversees all the activities, coordinates the team, and ensures the project aligns with your goals and requirements. 

However, your involvement is also required. You or someone from your organization should give input, set priorities, and regularly communicate with the project manager to ensure your project moves in the right direction. Agile, in this case, is one of the most popular and the best-working approaches for many teams as it allows for flexibility, regular progress checks, and course corrections as needed.  

Continuous Communication and Feedback Loops

Don’t leave communication to chance. Establish a clear communication cadence at the outset of the project. This could involve weekly video calls, daily check-in emails, or a combination of both based on the project’s complexity. Regular feedback after each interaction or sprint (in agile methodology) is vital to keep the project on track.

The Importance of Ethical Considerations and AI Fairness

ML is powerful, but you might have heard that it can sometimes perpetuate biases and inequalities. If you don’t want to build a system that inadvertently reinforces these issues, choose a partner committed to responsible AI development. Discuss potential biases in your data and how to mitigate them to ensure your models are unbiased.  

Why Partner with Relevant for your ML Project

Instead of struggling to recruit hard-to-find and costly talent, opt for machine learning outsourcing to get a flexible team and build cost-effective ML solutions. Clients around the globe turn to us when they seek specialized expertise, whether computer vision, AI chatbot development, or NLP, and want to accelerate their development. That’s because Relevant already has the right specialists and the necessary tools to drive ML projects effectively.  

We offer ML and AI development services and can build ML-powered apps or reinforce data governance along with data operations. Whatever your project is, we guarantee you get the most value from your data and for your money. On top of the technical expertise, we also provide strategic guidance and support throughout the project. So, if you want to dip your toes into ML technology to learn how it can supercharge your business, all that at a lower cost and minimal risk, contact Relevant today to stay ahead of the competition tomorrow.