The problem Anacostia-blockchain 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.

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