Limited access to computing resources and training data poses significant challenges for individuals and groups aiming to train and utilize predictive machine learning models. Although numerous publicly available machine learning models exist, they are often unhosted, necessitating end-users to establish their computational infrastructure. Alternatively, these models may only be accessible through paid cloud-based mechanisms, which can prove costly for general public utilization. Moreover, model and data providers require a more streamlined approach to track resource usage and capitalize on subsequent usage by others, both financially and otherwise. An effective mechanism is also lacking to contribute high-quality data for improving model performance. We propose a blockchain-based marketplace called "PredictChain" for predictive machine-learning models to address these issues. This marketplace enables users to upload datasets for training predictive machine learning models, request model training on previously uploaded datasets, or submit queries to trained models. Nodes within the blockchain network, equipped with available computing resources, will operate these models, offering a range of archetype machine learning models with varying characteristics, such as cost, speed, simplicity, power, and cost-effectiveness. This decentralized approach empowers users to develop improved models accessible to the public, promotes data sharing, and reduces reliance on centralized cloud providers.

翻译：计算资源和训练数据的有限获取对个人和群体训练及使用预测性机器学习模型构成了重大挑战。尽管存在许多公开可用的机器学习模型，但它们通常未被托管，需要最终用户建立自己的计算基础设施。或者，这些模型可能仅通过付费的云端机制访问，这对公众的普遍使用而言成本高昂。此外，模型和数据提供者需要更简化的方法来跟踪资源使用情况，并利用他人对资源的后续使用（无论从财务还是其他方面）获利。目前还缺乏有效的机制来贡献高质量数据以改进模型性能。为解决这些问题，我们提出了一个基于区块链的市场——"PredictChain"，用于预测性机器学习模型。该市场使用户能够上传数据集以训练预测性机器学习模型，请求对先前上传的数据集进行模型训练，或向已训练的模型提交查询。区块链网络中配备可用计算资源的节点将运行这些模型，提供一系列具有不同特征（如成本、速度、简洁性、性能和性价比）的原型机器学习模型。这种去中心化方法赋能用户开发可供公众访问的改进模型，促进数据共享，并减少对集中式云服务提供商的依赖。