Anacostia-blockchain
Decentralized Machine Learning Operations
About
The problem it solves Problems : MLOps is complex and requires massive amount of time and resources Lack of Security in Model Sharing: Traditional methods of sharing ML models between organizations or departments can be insecure, exposing intellectual property to the risk of tampering or theft. Inefficient Model Versioning and Traceability: Keeping track of different versions of ML models and their deployment history is challenging, making it difficult to audit and ensure integrity over time. Complexities in Compliance and Governance: Adhering to evolving regulatory requirements for AI and ML applications is cumbersome, often requiring significant manual effort to ensure compliance. Vulnerabilities in Model Deployment: Deploying ML models securely is critical, yet traditional deployment methods can be susceptible to unauthorized access and cyber-attacks. Opaque Model Marketplace: Buying, selling, or licensing ML models in a centralized marketplace lacks transparency, often leading to issues with trust and fairness. Solutions: Enhanced Security and Trust: Utilizes blockchain's immutable ledger for secure model sharing and transparent versioning, increasing trust among stakeholders by ensuring model integrity and authenticity. Streamlined Compliance: Automates compliance checks and governance tasks via smart contracts, significantly simplifying adherence to regulatory standards and reducing operational risks. Decentralized Marketplace: Facilitates a transparent and fair marketplace for ML models, where transactions are automated and secured through blockchain, ensuring creators are fairly compensated. Immutable Record Keeping: Provides an unalterable, time-stamped history of model development, deployment, and updates, making audits and compliance checks more straightforward. Cost Efficiency and Speed: Reduces costs related to model governance and compliance while enabling faster model deployment and updates, leading to quicker time-to-market for ML-driven applications.
How it's made
Challenges we ran into Making the ML pipeline run under the demo time, integration of IPFS and Smart contracts to a python tech stack and htmx