A new way to build AI models

An exciting feature of the competitions is where the winning models and algorithms are not exclusively used by one party and the rewards don’t stop at the prize pool. Instead, these winning AI models will be integrated into our AI marketplace.

This marketplace acts as a hub for algorithm creators to showcase their work, while small businesses, enterprises, and community members can access these powerful solutions.

AI competition has the following benefits which directly impacts on business ROI:

1. Reduction in personnel costs for machine learning model development:

Typically, hiring dedicated machine learning engineers is necessary for developing machine learning models. In recent years, there has been an increase in the application of machine learning within companies to improve services and streamline operations. Additionally, the commercialisation of data science has expanded. Acquiring and retaining machine learning engineers involves significant costs. Furthermore, models built by these engineers may carry the risk of not being the best possible models developed at that time, as they are influenced by the resources available to the engineer and their own experience. Conversely, if data collection and preprocessing for building machine learning models have already been completed (bitgrit provides consulting services from data selection for competitions to data collection and preprocessing), organizing a competition immediately can allow for the development of the best model from a large number of participants compared to development by in-house dedicated engineers. This enables a significant reduction in the costs associated with hiring machine learning engineers and managing resources, while also obtaining the best model.

Additionally, conducting interviews with developers of models adopted through competitions as a secondary effect allows for gaining insights into the process of model development and acquiring knowledge for future in-house model development.

2. Recruiting machine learning engineers:

Data science competitions provide a platform to connect with and potentially hire engineers who rank highly on the competition leaderboard. Being ranked among the top performers in a competition with numerous participants indicates a high level of quality and makes it easier to screen for qualities required in potential hires, such as their areas of interest and technical knowledge.

Hackathons, which often involve engineers competing against each other to solve specific problems, are commonly held for the purpose of recruiting engineers who excel in certain domains. In this sense, data science competitions can also be seen as a platform for gathering engineers well-versed in specific fields of machine learning. Additionally, many competition participants include competition achievements on their resumes, which can facilitate the hiring process as there is alignment of interests between the sponsoring company and the winners.

Competition Topics

There are two ways in which bitgrit prepares challenges for data science competitions. One is when a sponsor company already has a problem they want to solve. The other is when bitgrit independently sets challenges that are either currently needed or likely to be needed in society.

When a sponsor company already has a problem they want to solve, the process is straightforward. The challenge is used in the competition as it is, or with modifications (if the company does not want to publicly disclose their own challenges), according to the needs of the sponsor. On the other hand, when bitgrit independently sets challenges, they are based on discussions among members with diverse backgrounds such as the marketing department and machine learning engineers. The goal is to set challenges that are likely to become popular topics or will be needed in society in the future, and the data is prepared through unique channels.

The steps to join the competition

The steps for a data scientist to participate in a competition are fairly straightforward. First, they register their email address on bitgrit's data science platform. Once the registration is complete, they can access the competition data by accepting the NDA for the ongoing competition and participate. Please note that it is not possible to register multiple accounts using the same email address, and participating in competitions with multiple accounts using multiple email addresses is strictly prohibited. Additional security measures are listed as part of the “Anti-exploit measures” section.

In order to enter a competition, each Data Scientist wishing to participate would be required to deposit or stake a set amount of BGR tokens. After the end of each competition, each participating data scientist will receive a portion of their staked BGR back by way of a tiered ranking system. The amount of BGR returned will vary per competition.


The "leaderboard" in a data science competition is a ranking system that publicly displays the performance of participants' models based on specified evaluation metrics such as accuracy or F1 score. The leaderboard indicates how well participants' models perform compared to other participants. The benefits of using a leaderboard in a competition are as follows:

1. Motivation: The leaderboard stimulates competitiveness and provides participants with the motivation to strive for better results. Seeing their own rank and its changes on the leaderboard serves as a driving force for participants to invest time and effort into improving their position.

2. Visibility: High ranks on the leaderboard enhance visibility and recognition within the data science community. Participants' names may gain attention and they may be recognized as exceptional data scientists by companies and potential employers, contributing to the activation of both participants and the community as a whole.

3. Eligibility for rewards: In bitgrit competitions, participants who achieve top rankings are eligible for rewards. The leaderboard plays a critical role in determining the winners (as well as detecting fraudulent activities) and deciding the eligibility for rewards. This aspect is important for participants to compete either for recognition or monetary rewards.

As a new feature to further enhance data science competitions in the future, it is also being considered to provide a platform for participants to exchange information. In such a platform, participants who have achieved top rankings can engage in discussions with one another, allowing them to learn approaches and techniques they may not have been aware of. Such an exchange platform provides a cooperative environment where participants can discuss strategies, share ideas, and learn from each other.

Competition Platform

Innovative solutions often emerge from competitive environments. bitgrit's Competition Platform is designed to harness this principle, enabling real-world data skill assessments and engaging both internal teams and external expert communities with pressing business data challenges. Collaborating with partners, bitgrit offers bespoke, white-labelled or community driven data science competitions.

These competitions empower businesses to:

  • Identify pivotal AI applications within their operations;

  • Source AI models from a vast community of over 30,000 data scientists; and

  • Seamlessly integrate these solutions without the complexities of establishing an in-house data team.

For every challenge presented, a multitude of data scientists converge to design optimal AI models tailored to address specific business issues. bitgrit meticulously selects and delivers the most effective solutions from these endeavours.

Our Crowd-based AI as a Service (CAIaaS) model ensures clients receive the crème de la crème from thousands of submissions, facilitating swift scalability at a fraction of the cost of traditional team recruitment. Top-tier models are subsequently listed on our AI Marketplace, allowing other businesses with analogous challenges to benefit, while simultaneously generating revenue for the contributing Data Scientists.

In summary, bitgrit's CAIaaS is the epitome of efficiency in custom AI model development.

Small Business and Enterprise Competitions

These competitions will operate on fiat rails only, where companies pay the prize money to the winning data scientists in USD (or the stablecoin equivalent) in exchange for the winning AI models, of which bitgrit will receive a fee as well.

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