Summary: Machine learning outsourcing is a rapidly growing industry, with organizations from various industries seeking to leverage the power of artificial intelligence and machine learning to improve their operations and stay ahead of the competition. In this article you can learn more about the current state of machine learning outsourcing, the effects of the pandemic in machine learning and the challenges that are faced.
As artificial intelligence (AI) adoption continues to increase at incredible speed, the demand for talent in data engineering, machine learning and data science skills has sky-rocketed. Outsourcing the development of machine learning solutions allows organizations to quickly assemble teams that can tackle complex challenges that require AI capabilities. Deloitte’s Global Outsourcing Survey 2022 reveals that cybersecurity, data analytics and AI are the top outsourcing priorities for organizations.
Table of Contents
- Understanding Machine Learning Outsourcing
- Build vs Buy: Outsourcing ML Products and Services
- Should You Offshore, Onshore or Nearshore Machine Learning Services?
- How the Pandemic Shaped the Outsourcing Market
- Benefits of Machine Learning Outsourcing
- Challenges Facing Machine Learning Outsourcing
- The Future of Machine Learning Outsourcing
Understanding Machine Learning Outsourcing
Outsourcing machine learning is hiring a third-party organization to build machine learning (ML) solutions. There are many business models to outsource ML projects such as implementing existing products through managed services. delegating full tactical projects to a third party team or staffing resources with specialized skills to join an internal team. The right business model will depend on the business objectives and internal capabilities.
Outsourcing can cover a breadth of activities in the machine learning lifecycle: from exploring data, to building models to creating ML Ops pipelines. These activities may require specific skills in subfields like computer vision and natural language processing (NLP) or experience in programming languages like Python.
Build vs Buy: Outsourcing ML Products and Services
The AI revolution has spurred innovation in digital products. With so many new products released every day, many organizations struggle to decide whether to buy existing AI products or to build a custom solution from scratch. The build vs buy decision will be driven by market positioning and cost analysis. If there is an affordable product on the market that solves the business problem with the functionality and experience desired by the users, it is likely the organization will decide to buy. Managed services are the most common outsourcing business model in this scenario, where there is a long-term relationship to support the application implementation, optimizations, maintenance and all its complex processes.
If the organization has imagined a unique digital solution or user experience that can provide a significant competitive advantage, building a custom product could prove to be a worthwhile investment. In these types of engagements, machine learning outsourcing providers will be collaborative partners that share accountability and are driven by business outcomes.
Should You Offshore, Onshore or Nearshore Machine Learning Services?
When evaluating outsourcing models, organizations will face the decision of outsourcing to countries on the other side of the planet, nearshoring services to countries on the same time zone, or onshoring development in the same country.
In the US, companies that are outsourcing design, development or machine learning work are pivoting towards a nearshore business model where they can maximize all the benefits of reducing costs, adding diversification without compromising quality and communication. Organizations that are protective of their intellectual property and concerned about complying with federal regulations should consider nearshoring to Puerto Rico.
How the Pandemic Shaped the Outsourcing Market
The Covid-19 pandemic had many profound effects on society and the way we work. Unlike a few years ago, a lot of organizations are now comfortable with remote and hybrid working environments. Communicating via slack, gathering in video conferences, and finding times to connect is integrated in the professional culture of most technology companies.
This change in the way we work makes the shift to partnering with third parties to outsource machine learning and other activities. The transition to onboard external teams is much easier now than ever before.
The pandemic also spawned a movement coined The Great Resignation, where employees have been actively considering different job opportunities and career paths that suit their professional goals and lifestyle. Outsourcing is becoming more popular as it can mitigate the risks of employee turnover and accelerate new resource recruiting and onboarding.
Following the Great Resignation, massive layoffs in tech giants like Meta, Microsoft and Twitter overwhelmed the headlines and shifted the job markets. Many factors may have contributed to it including inflation, over hiring, pandemic correction and the potential of a recession. Outsourcing is often used as a strategy to expand a team and avoid layoffs.
Nearshoring machine learning development is a sound strategic move to meet the rising demand for better digital experiences and offset the risk of the great resignation or expensive layoffs, while embracing the new hybrid and remote work environments.
Benefits of Machine Learning Outsourcing
The most impactful innovations are rarely built by organizations working in silos, but by ecosystems. Large and small organizations in both the public and private sectors are outsourcing work to trusted partners to drive digital transformation.
Outsourcing machine learning services has several benefits including:
- Tapping into new specialized skill sets to your team
- Accelerating the time to market
- Adding diversification to the team
- Reducing costs
- Reducing bottlenecks and single points of failure
Challenges Facing Machine Learning Outsourcing
The industry is facing some key challenges when it comes to outsourcing development of machine learning solutions including cybersecurity, building trustworthy AI solutions, and blending organizational cultures in an ecosystem of vendors.
Like all software, machine learning solutions are vulnerable to malicious attacks including data breaches, security breaches and intellectual property theft. Proper cybersecurity policies and procedures must be proactively implemented as part of the development process.
Biased data sets, poor model interpretability, and weak AI governance can all lead to lack of trust in a machine learning solution. Kicking of projects with human-centered design practices and implementing interpretability and ethical AI practices in the organizations will provide the foundation for building a trust-worthy solution.
Organizations that contract several third parties must move away from working in silos and work to create a positive and cohesive cross-company culture. The most successful ecosystems operate with trust, openness and transparency creating an environment where people from different companies come together and act as one team.
The Future of Machine Learning Outsourcing
Wovenware understands the changes in the technology outsourcing industry from first-hand experience. Our clients no longer prioritize cost cutting above all else. Instead, they turn to us when they require specialized expertise, such as in computer vision and NLP, or when they want to fast-track the development of a digital innovation. The key to the future of machine learning outsourcing is the establishment of collaborative partnerships focused on co-innovation.
