e2eDML

Inspiration

As machine learning continues to evolve, there is a growing interest in decentralizing the process to enhance privacy, security, scalability, and collaboration.

What it does

End-to-end decentralized machine learning (e2eDML) aims to use bacalhau for distributed machine learning, enabling training and inference without relying on centralized servers. End to End Decentralised Machine Learning. 1. Move Training Data to IPFS

2. Create a Docker Container for training data

3. Train Data decentrally using bacalhau

4. Create a Docker container for inference

5. Deploy Smart Contract on Filecoin's Caliberation Testnet

6. Call Machine Learning Model Frm Smart Contract.

How we built it

Tech Stack - Solidity - IPFS - Python - Docker - Javascript

Challenges we ran into

Accomplishments that we're proud of

Calling Machine Learning Model from Smart Contract.

What we learned

What's next for e2eDML

A Dapp for Data Scientist for training Decentralised Machine Learning Model . A Dapp for users to use different Machine Learning Models while paying a token.

Built With

• bacalhau
• docker
• filecoin
• ipfs
• javascript
• lilypad
• python
• solidity