Machine learning outsourcing: how to fill the draining talent pools?

According to Precedence Research, the Machine Learning (ML) market is projected to surge to $771B by 2032, a significant increase from $38B in 2022. It indicates the significant growth in demand for AI and Machine Learning experts. This rapid expansion has led to a global shortage of AI specialists, leading to a significant challenge in talent acquisition efforts. Despite increasing investments in ML, there remains a scarcity of qualified professionals to meet the rising demand.

In response, many organizations are turning to alternatives such as Machine Learning outsourcing to address their talent shortages effectively. Outsourcing ML projects enables companies to access a pool of skilled developers without the complexities and time-consuming processes associated with traditional in-house hiring. 

Let's explore how organizations can tap into these talent pools and efficiently fill their ML talent gaps through outsourcing solutions.

Shortage of Machine Learning talent in the US

The number of available Artificial Intelligence and Machine Learning professionals does not meet the growing global demand. According to LinkedIn, there are over 73,000 open vacancies in the US that list Machine Learning expertise as one of the required skills. At the same time, there are only 23,000 AI experts in the country. 

Such a gap between talent demand and supply pushes organizations to find alternative ways to fill their open positions. The race for AI and ML talent is on, and the industry leaders go to great lengths to hire top experts in the AI domain. Machine Learning outsourcing can offer a solution to a talent shortage problem.

The problem the US industry leaders are facing is prevalent across the globe; the same thing happens in most Western countries—the lack of experienced engineers is critical. Fortunately, numerous experts and IT outsourcing destinations are available.If you're considering outsourcing Machine Learning projects to remote teams, N-iX provides a reliable destination for your needs.

Why ML outsourcing is the way to go

Machine Learning holds immense potential, but building and maintaining an in-house ML team can be costly and time-consuming. This is where ML outsourcing comes in, offering strategic advantages for businesses of all sizes.

Optimization of development costs

Building an in-house Machine Learning team entails significant expenses, including recruitment, salaries, benefits, and infrastructure. Outsourcing Machine Learning tasks allows businesses to access specialized expertise without the overhead costs of maintaining a dedicated team. 

Businesses can invest in additional infrastructure, software licenses, and ongoing maintenance. Instead, these responsibilities are shouldered by the outsourcing provider, freeing up capital and resources that can be reinvested in core business activities. 

Instant access to domain expertise

Outsourcing companies have Machine Learning developers with specialized skills and extensive experience in various ML domains and industries. This expertise covers many areas, including data analysis, algorithm development, model training, and deployment. 

Partnering with outsourcing providers, businesses gain immediate access to this wealth of knowledge and skill, eliminating the need for lengthy recruitment processes or extensive training programs.

Minimizing the time to market

A shortage of expertise and limited resources increases the risk of project delays, errors, and suboptimal outcomes, leading to missed market opportunities and decreased competitiveness.

Machine Learning outsourcing providers often have pre-built frameworks, libraries, and solution accelerators that can expedite development cycles and shorten time to market. With streamlined processes, proven methodologies, and experienced teams, you and your team can accelerate the delivery of ML projects from concept to production.

Also, outsourcing ensures the scale of resources dynamically according to project demands. Whether scaling up during periods of high demand or scaling down during lower-demand periods of time, organizations can adjust their outsourcing arrangements to align with their budgetary requirements.

Diverse availability of vendors

Machine Learning outsourcing provides companies with access to a global talent pool of vendors with diverse backgrounds, expertise, and experience levels. This diversity enables companies to select vendors that best align with their project requirements, budget constraints, business objectives, etc.:

• Specialized skills (data preprocessing, algorithm development, model training, and deployment);
• Industry expertise (healthcare, finance, retail, and manufacturing);
• Customized service (consulting, project management, development, training, and support);
• Geographical flexibility (onshore, nearshore, and offshore locations).

Effective data management and safety

Any company must implement robust data security measures to protect sensitive information from unauthorized access, breaches, and cyberthreats. That’s why companies like N-iX employ encryption, access controls, monitoring tools, and regular security audits to safeguard data integrity and confidentiality throughout the ML project lifecycle.

Moreover, risk mitigation strategies to address potential data-related risks, such as data loss, corruption, or misuse, are a must-have. A reliable IT outstaffing partner ensures data backup procedures, disaster recovery plans, and contingency measures to minimize disruptions and ensure business continuity in unexpected circumstances.

Outsourcing providers are proficient in handling large volumes of data efficiently and securely. With established data management protocols and best practices, they ensure that data is collected, processed, and stored in compliance with regulatory requirements and industry standards.

Read more: Machine Learning consulting: From a concept to measurable business value

How to choose the best partner for Machine Learning outsourcing

With numerous options comes the difficult task of picking the right partner for your next project. If you want to outsource Machine Learning projects, you need to know who your potential partners are, what kinds of services they offer, their reputation, and whether they can deliver the expected results. 

Check their portfolio and ML project's track record

The first thing is to narrow down your list of potential partners on rating platforms like Clutch and filter companies by expertise and rating. Select the AI and ML expertise and the companies with the highest rating. That way, you will already narrow your search down to just a couple of dozen companies. 

Once your list is shortened to just a handful of top-rated software development companies, check their track record with AI and Machine Learning projects. Make sure your potential vendor has experience working on AI and ML projects.

Choose midsize and large companies

The scope of your project may change over time, you might want to add new features to the product or expand its functionality. As the scope of your project changes, so does the composition of your team, and you want to be sure your potential vendor has a large enough talent pool to keep up with your fluctuating needs. 

Partnering with smaller companies, you might find it challenging and time-consuming to scale your team up. Larger companies with more than 250 experts on board, however, can scale their development teams much faster. With a vast talent pool, your potential vendor can guarantee scalable teams for outsourced Machine Learning projects. 

Ensure comprehensive development solutions

Some software development companies focus on a few aspects of the development process. In contrast, the others can deliver full-cycle solutions to drive your project from the ideation stage and up to market launch. Working with a full-cycle partner is more convenient as you will have all the development stages covered by a single team of developers.

Full-cycle ML outsourcing allows you to have all the project-related operations in one place, which maximizes communication efficiency, optimizes spending, reduces redundant administrative effort, and makes the project delivery process more convenient for you and your dedicated development team. This includes after-development support and maintenance to keep your product stable and fully functional for a long period.

Check security and compliance

Whenever you partner with the outsourcing team, you will inevitably share some of your sensitive business data with them. You will need a partner who adheres to all the relevant security standards like PCI DSS and ISO 27001 and guarantees total protection of your sensitive data. 

Success stories: Optimizing warehouse operations with ML

Working with clients who lead innovation in their industry, we need to develop a comprehensive set of solutions to help the client stay on top of the competition. The N-iX team worked with a German A-list tech supplier with over 700 warehouses worldwide to automate manual labor, streamline inventory management, create a mobile app for warehouse operators, and develop an embedded computer vision system for no-touch tracking of goods. 

Machine Learning has been used extensively throughout this project, especially for the computer vision system we have developed. Machine Learning is an essential part of any computer vision system, as the AI experts have to teach the system to recognize specific objects. For this particular project, our Machine Learning experts have developed a system capable of the following features:

• Pallet detection;
• Label recognition;
• Box counting;
• Damage detection;
• Fuzzy information matching.

Value for the client: The Machine Learning solutions we’ve implemented helped the client to reduce the amount of human labor across all the warehouses, which allowed the client to cut costs and streamline warehouse operations.  

Final thoughts

Machine Learning can be implemented under different circumstances, solving many business challenges. AI is becoming a huge leverage across industry verticals. Early application of this technology is the key to staying ahead of the competition. 

The race for the industry’s best Machine Learning talent is on, and the talent pools of the US and Western European countries are running out. Therefore, outsourcing Machine Learning projects to other countries has become more appealing. 

Fortunately, you have the option of outsourcing Machine Learning projects to one of the best software development partners and getting access to some of the industry’s best Machine Learning experts quickly and easily.

Why choose N-iX for your ML project?

N-iX is a tech leader of the industry’s best Machine Learning experts with strong AI and ML expertise proven through numerous projects. Here’s why you should consider N-iX as your partner for your machine-learning project:

• N-iX has 21 years of experience in Machine Learning and has delivered over 30 AI and data projects in the last year alone. We have a team of over 200 Data and AI experts and more than 2,200 software engineers and IT experts with experience delivering data analytics services across various industries, such as telecom, manufacturing, healthcare, and more.
• Our services cover a wide range of expertise, from BI and Big Data to ML/AI and Data Science. We provide end-to-end solutions, from designing implementation strategies to building intelligent models, MLOps, advanced analytics, and solution maintenance.
• We have worked with industry leaders and Fortune 500 companies: Gogo, Redflex, Ringier, Cleverbridge, and Fortune 500 enterprises, helping them successfully launch and scale their Data Analytics and Machine Learning directions.
• N-iX has received numerous awards and recognitions for its excellence in the IT industry, including the Global Outsourcing 100 Award, CRN Solution Provider 500 List, and EMEA Inspiring Workplaces Awards.
• We comply with all established data security standards and regulations, including ISO 27001, ISO 9001, ISO 27001:2013, GDPR, and PCI/DSS.