The demand for machine learning talent far exceeds supply. ML job postings on LinkedIn increased by over 450% from 2015 to 2021, yet 57% of organizations report a shortage of ML skills.1 Crowdsourcing has emerged as an flexible, on-demand solution to help bridge the gap.
This complete guide examines crowdsourcing for machine learning, including key benefits, challenges, best practices, examples, and how to get started.
What is Crowdsourcing Machine Learning?
Crowdsourcing machine learning leverages online platforms to access on-demand ML talent for data science projects. Rather than hiring full-time expertise, companies can submit ML tasks like data labeling, model development, and deployment to a distributed network of remote specialists.
Platforms like Appen, Scale, and Clickworker connect businesses with global pools of data scientists, developers, domain experts, and data annotators. These marketplaces let you scale up or down flexibly based on workload.
Crowdsourcing provides targeted ML capabilities without the overhead of recruitment, office space, equipment, and benefits associated with permanent hires. It offers agility for early prototyping and one-off initiatives where on-demand skills are beneficial.
The Growing Demand for Machine Learning Experts
Machine learning has exploded in popularity in recent years. Gartner forecasts that data and analytics leaders will allocate up to 50% of new project investment to AI by 2024.2
Yet most organizations struggle to build robust internal ML capabilities. One survey found that 56% of data and analytics leaders have difficulty retaining ML talent.3 Salaries also continue rising, with the average ML engineer in the U.S. earning over $200,000 per year.4
This supply-demand imbalance creates fertile conditions for crowdsourcing ML skills. Let‘s examine the benefits this approach offers.
Benefits of Crowdsourcing Machine Learning Projects
Access Specialized Expertise
Crowdsourcing platforms offer access to ML experts across every skillset. For example, Clickworker maintains a global crowd of over 500,000 registered experts, including over 7,500 data scientists and AI specialists. Such a large talent pool lets you find niche skills like TensorFlow expertise or computer vision.
Niche ML skills like computer vision, NLP, reinforcement learning, and blockchain are readily available. Domain knowledge in areas like finance, healthcare, and telecom can also be valuable.
Cost and Time Savings
Crowdsourcing ML tasks can deliver significant cost savings compared to hiring full-time equivalents. One study by Google found crowdsourced data labeling to be as much as 50% cheaper than using internal labelers.5 For early prototyping needs, crowdsourcing provides research & development capabilities without sizable investments.
You also avoid costs like office space, equipment, benefits that come with permanent hires. Paying only for actual work rather than salaried time can optimize spend, especially for periodic projects.
Faster Results
Crowdsourcing ML tasks accelerates development by tapping into an on-demand parallel workforce. Subject matter experts can focus solely on your project without other distractions.
One crowdsourcing platform Gengo AI reported that clients achieved 10x faster results compared to internal teams. By spinning up more crowd workers, you can scale data annotation or model training to hit targets quicker.
More Innovation
Engaging a diverse expert crowd enhances creativity. Freelancers get exposure to varied industries and use cases, bringing cross-pollinated thinking. One study found crowd workers submit novel solutions at a rate ~40% higher than company employees.6
Crowd diversity also helps reduce groupthink and identify blindspots. This amplifies innovation, leading to higher model accuracy and new breakthroughs.
The crowd provides scalable ML talent (Image source: research.marketingscoop.com)
Challenges of Crowdsourcing Machine Learning
While crowdsourcing offers advantages, it also comes with some pitfalls that need mitigation:
Quality Control
Maintaining quality levels comparable to internal teams is a common concern. Some crowdsourcing platforms conduct technical vetting and skills testing to address this. However, establishing clear deliverable expectations, milestones, and QA protocols is still essential.
In my experience, breaking projects into modular steps with formal approval stages helps align on quality standards with remote contributors. Automated testing and requiring high accuracy thresholds also improves outcomes.
Data Privacy and IP Protection
When dealing with sensitive data, adequate security protocols and controls are critical. Most crowdsourcing platforms provide encryption and access controls to protect customer data and IP. Legally binding non-disclosure agreements add another layer of protection.
Incentive Structures and Fair Pay
You need to offer competitive compensation to attract specialized, high-performing talent. However, designing the right incentive models is key — overly large prizes sometimes encourage shortcuts rather than excellence. I‘ve found that a bonus structure based on accuracy and completeness of work can help balance incentives.
Project Management Overhead
Overseeing remote freelancers takes extra work compared to office teams when it comes to planning requirements, tracking progress, communication, and issue resolution. Using collaborative project management tools and setting expectations upfront helps minimize friction.
Best Practices for Crowdsourced ML Projects
Here are some tips to maximize the value of crowdsourced initiatives:
- Start Small – Validate new suppliers via pilots before committing to large efforts.
- Provide Clear Goals – Eliminate ambiguity by detailing requirements, quality metrics, workflows, compliance, and responsibilities.
- Offer Competitive Rates – Benchmark fair market wages when pricing projects to attract top-tier talent.
- Highlight Impact – Articulate how the work contributes to important outcomes to boost engagement.
- Define Milestones – Break projects into phases with deliverables, validation, and payment terms.
- Leverage Collaboration Tools – Use platforms like Slack, Trello, and GitHub to align on work status.
- Give Feedback – Provide frequent, constructive feedback to maintain quality standards.
- Iterate and Improve – Apply lessons learned to continuously refine processes and training.
Real-World Examples of Crowdsourced ML
Here are a few case studies that demonstrate the tangible value of crowdsourcing for AI initiatives:
- Mercedes-Benz used crowd annotation to label 1.2 million 3D points on thousands of car images for developing self-driving vehicle perception algorithms, reducing costs by 50%7
- Scale AI offered a bounty challenge to improve toxicity detection in online comments, achieving 10% higher accuracy than existing models8
- Kaggle crowdsourced a solution predicting user clicks on ads that lifted revenue by $256 million a year for Google9
- Figure Eight leveraged crowd data labeling to train a deep learning model identifying mature content, reaching 99% accuracy10
Crowdsourcing is used widely for time series forecasting (Image source: Tableau)
Ethical Considerations for Crowdsourced ML
Some ethical risks to be aware of include:
- Data Privacy – Ensure responsible data handling protocols are in place.
- Fair Pay – Compensate adequately based on effort, complexity and local costs.
- Transparency – Disclose project goals and get informed consent where applicable.
- Inclusive Sourcing – Equitably distribute work to mitigate biases.
- Model Governance – Monitor crowd contributions for quality and algorithmic fairness.
Platforms should enable equitable access to opportunities for crowd workers worldwide. Contributors should be informed on how their work is being used for model development.
The Future of Crowdsourced Machine Learning
Looking ahead, a few trends may shape crowdsourcing:
- Demand explosion – As more companies pursue ML initiatives, demand for crowd talent will surge. Deloitte expects the crowdsourcing market to [grow to $25 billion by 2025].11
- No-code ML – Automation tools like autoML could enable less technical domain experts to contribute directly to ML projects with minimal coding.
- Blockchain – Blockchain-based crowdsourcing networks may provide more transparency and control over data rights.
- Human-AI collaboration – Hybrid models blending crowdsourced human intelligence with AI could achieve superior results than either alone.
While still relatively early, crowdsourcing provides immense value today. As processes and tools improve, its scope will expand even further.
Getting Started With Crowdsourced Machine Learning
For those exploring crowdsourced machine learning, here are some recommendations:
- Identify Use Cases – Assess if on-demand talent matches your needs in terms of timelines, budgets, data sensitivity, and capabilities.
- Select Your Platform – Vet a few providers based on reviews, specialties, talent quality, and costs.
- Start Small – Run pilots to validate capabilities before pursuing larger commitments.
- Streamline Workflows– Map out processes for task submission, work tracking, deliverable review, and payment.
- Onboard Talent – Set expectations on requirements, success metrics, workflows, compliance, and communications.
- Refine and Scale – Apply lessons from initial projects to optimize all aspects as you expand initiatives.
Conclusion
Crowdsourcing provides a flexible means to augment machine learning expertise. While managing distributed teams takes effort, the benefits of targeted on-demand talent are immense. Following best practices helps ensure high quality outcomes.
As machine learning becomes further democratized, crowdsourcing will grow as a strategic capability for enterprises. Unlocking collective intelligence can accelerate innovation and value creation.
To further discuss crowdsourced machine learning opportunities, feel free to contact our team of AI advisors.
Sources
- Marek, Lindsey. “Demand for AI talent still far outstrips supply.” LinkedIn, 2021.
- Panetta, Kasey. “Gartner‘s 2021 Hype Cycle Shows Most Technologies Will Take 10 Years for Enterprises to Adopt.” Gartner, 2021.
- Columbus, Louis. “Where‘s The AI Talent? Machine Learning Leaders Are Hard To Find.” Forbes, 2022.
- Metrick, Brian. “2022 Machine Learning Engineer Salary Survey.” Clutch, 2022.
- Varma, Paroma et al. “AI for Social Good: Unlocking the Opportunity for Positive Impact.” Google AI, 2018.
- Jeppesen, Lars Bo and Karim R. Lakhani. “Marginality and Problem-Solving Effectiveness in Broadcast Search.” Organization Science, 2010.
- Rolnick, David et al. “Tackling Climate Change with Machine Learning.” arXiv, 2019.
- “Toxic Comment Classification | Kaggle.” Kaggle, 2022, https://www.kaggle.com/c/jigsaw-toxic-comment-classification.
- Orr, Joel. “How Machine Learning Changed Online Ad Revenue at Google.” Google Cloud, 2022, https://cloud.google.com/blog/products/ai-machine-learning/how-machine-learning-changed-online-ad-revenue-at-google
- “Success Stories.” Figure Eight, https://www.figure-eight.com/success-stories/.
- Columbus, Louis. “How Crowdsourcing Is Shaping The Future Of Work.” Forbes, 2019.
