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.

基于区块链的市场——“PredictChain”，用于解决有限访问、预测机器学习模型和数据分享方面的挑战。通过该市场，用户可以上传数据集来训练机器学习模型，请求对先前上传的数据集进行模型训练或查询训练好的模型，并由区块链网络中的节点提供计算资源以便操作这些模型，以满足不同特点、如成本、速度、简单性、性能和成本效益等的原型机器学习模型需求。这种去中心化的方法赋予用户开发改进模型的能力，促进数据共享，并减少对集中式云提供商的依赖。

PredictChain: 在分散式基於區塊鏈的市場中賦予協作和數據可訪問性的人工智能