This document devoted to the data that Lambda can provide for Giza hackathon.

The main purpose is to

Motivation

For Giza:

• enrich Giza datasets for ML training with staking data from Lambda
• support one more use-case: slashing prediction for deFi risk management

For Lambda:

• test ML developers as users on the platform
• get insights about data could be useful for ML purposes in DeFi

Use-cases: Slashing risk prediction

Problem. Staking is one of the primary methods for generating income for tokens such as ETH. Currently, about 60% of ETH is used inside of LST/LRT protocols. This means that the risk of slashing is becoming increasingly significant for Ethereum tokenomics. DeFi protocols like Lido monitor slashing events on the Beaconchain and use Lambda for this purpose. However, predicting such events is still an unresolved issue that could help act proactively and alert node operators to potential critical decreases in their performance score.

Solution. Slashing is a dangerous but still very rare event. Therefore, for the purpose of managing the validator set, we propose predicting the performance score of validator nodes, which includes aggregated statistics on how well the nodes execute their duties in the Beacon chain (Consensus layer of Ethereum). We suggest to use Lambda’s timeseries dataset with scores for each validator node in every epoch (32 blocks or approximately 6.4 minutes) to train you model and predict performance score.

Data access

Access is free for now:

• Visit lambda-demo.p2p.org (BI tool to present data for customers)
• Login with Google / Git
• Go to SQL -> SQL lab
• Choose Lambda GCP
• Schema: dma_beacon_chain