Crowdsource Machine Learning: A Complete Guide in 2024

The market for machine learning is projected to grow at a 42% CAGR between 2018 and 2024.¹ However, only 15% of organizations are advanced ML users.² Finding and employing top-tier machine learning expertise and knowledgeable professionals who can come up with original solutions to challenging issues in machine learning can be a difficult and expensive endeavor.

Crowdsourcing has gained popularity recently as a method for businesses and organizations to produce cutting-edge machine-learning solutions by utilizing the skills and knowledge of a vast network of remote data scientists.

We have prepared this guide for businesses about crowdsourcing talents to solve complex machine-learning problems.

What is crowdsourcing?

Crowdsourcing generally means breaking a complex task or problem down into smaller, more manageable components and then distributing these to a large and diverse group of people known as the “crowd.” This crowd may include people from various backgrounds, locations, and levels of expertise.

Crowdsourcing can be used to accomplish a wide range of tasks, including:

• Data collection for market research
• Content creation
• Logo design
• Complex problem-solving.

Source: DailyCrowdsource

Figure 1. Types of crowdsourcing

What is crowdsourcing experts for machine learning contests?

Having the greatest analytical experts on a team is essential if you want to win machine learning competitions. However, it can be challenging to find the appropriate experts with the required coding expertise and experience to create successful models. Crowdsourcing can be a useful technique in this situation.

Crowdsourcing the analytical experts to solve machine learning problems is called crowdsourcing machine learning. A wide range of talents and perspectives might provide teams a competitive advantage in these competitions, which often demand participants to create the most accurate models for a given dataset using state-of-the-art approaches.

Top 3 benefits of crowdsourcing talents for machine learning contests

1- Access to a diverse pool of talent

With access to a diverse talent pool, businesses can reach people with various experiences, perspectives, and backgrounds, which brings new and unique ideas.

2- Increase speed and scalability

Crowdsourcing, with its large pool of coders, can assist organizations in rapidly scaling their workforce and completing tasks or projects in a shorter timeframe.

3- Enhance engagement and participation

Crowdsourcing can help engage coders who might not have been involved in a task or project, leading to increased participation and engagement.

Top 5 challenges & solutions of crowdsourcing talents for machine learning contests

1- Ensuring the quality of the workers

Maintaining the quality of the tasks while using crowdsourced coders is one of the main issues. Low-quality codes pose the risk of resulting in delays or errors. In addition, there are different variations of the contributors’ experience and expertise in data cleaning, preprocessing, and labeling. To overcome this, a thorough review and feedback mechanism must be in place, together with clear norms and quality standards, to overcome this difficulty.

2- Providing the necessary tools and infrastructure for the crowd

This could involve having access to advanced computing tools, specialist software and hardware, and learning materials. Companies that can make these resources available to the crowd are more likely to draw top people and deliver high-quality outcomes.

3- Having seamless project management

A project that uses crowdsourcing must have effective project management. However, it can be difficult and time-consuming to manage a big and diverse group of contributors. A clear project strategy, clearly defined milestones, and efficient tracking and reporting procedures are necessary to handle this difficulty.

4- Protecting intellectual property

The intellectual property of organizations, including data sets and machine learning models, must be protected carefully. The rights and obligations of participants regarding ownership and use of the data and models must be made clear to them.

5- Providing incentives

Organizations must give participants the right incentives and rewards to draw in and keep the top talent. This could involve monetary awards, praise, and other benefits. On the other hand, offering an excessively generous incentive may draw players more focused on winning the prize than creating a precise model.

5 use cases/applications of crowdsourcing experts for machine learning

Although there is a wide range of possibilities that crowdsourced experts can develop models using machine learning techniques, here we provide top use cases.

1- Sentiment analysis

The process of determining the emotional tone in content, such as a social media post or a product review, is known as sentiment analysis. Crowdsourcing experts offer to create machine learning algorithms that analyze the sentiment in texts, audio, or videos as positive, negative, or neutral. Such models can assist businesses in better monitoring their online reputation and responding to customer feedback.

Sponsored

Clickworker is a service provider that delivers crowdsourced services through their 6+ million workforce worldwide, and they offer services such as sentiment analysis and data annotation. Their crowd works in projects that aim to identify the sentiment/mood based on contextual meaning, rather than automatically assigning sentiments to individual words.

They provide crowdsourced data as training data for machine learning that increases the accuracy of the models. For more information, watch their short video clip:

For those interested, here is our data-driven list of sentiment analysis services.

2- Time series forecasting

Source: Tableau

Figure 2. An example of time series forecasting

Forecasting future trends in a dataset based on past trends is what time series forecasting is all about. This is a critical task in many industries, including finance, energy, and transportation. Crowdsourced experts can conduct machine learning research to predict trends in time series data, such as stock prices or energy demand. These models can be trained using historical data and can take into account a variety of factors, such as weather patterns, economic indicators, and other external factors.

3- Object recognition

Object recognition is a critical task in computer vision, with many potential applications in surveillance, self-driving cars, and robotics. Crowdsourced experts can create machine learning systems that can detect and classify objects in images, such as identifying cars or buildings in satellite images or recognizing faces in photographs. These models can be trained on large datasets, which increases their accuracy and reliability.

4- Medical diagnosis

Machine learning has the potential to transform medical diagnosis by accurately identifying diseases and conditions that human doctors may find difficult to diagnose. Crowdsourcing can be used to create machine learning models to diagnose diseases based on medical imaging or clinical data, such as cancer prediction using X-ray or MRI scans. Such models can assist doctors in making faster and more accurate diagnoses and detecting diseases before they become more serious.

For more use cases of AI in healthcare, check our article.

5- Machine translation

The process of automatically translating text from one language to another is a machine translation. This task is essential for global businesses and individuals who want to communicate with people from other countries. Crowdsourcing can be used to create machine learning models that can translate text between languages, such as documents or movie subtitles. These models can be trained on large datasets of the translated text and can use techniques like neural machine translation to improve their accuracy and fluency.

You can check our data-driven list of machine translation tools.

If you need help finding a vendor or have any questions, feel free to contact us:

External Links

• 1. Sanghavi, Aayushi. (April 6, 2021). “51 Machine Learning Statistics to Get You Thinking.” G2. Retrieved February 14, 2023.
• 2. Sanghavi, Aayushi. (April 6, 2021). “51 Machine Learning Statistics to Get You Thinking.” G2. Retrieved February 14, 2023.