A business network is far more than the business itself; it’s a collection of many connected things—customers, suppliers, partners, and more. The transferring of assets to build value for business networks is happening continuously with participants, transactions, and contracts.
There is always a need for a shared ledger across the business network to operate efficiently with broader participation to reduce cost, reduce risk and fraud, and increase trust. Blockchain technology provides a distributed database of records that have been executed and shared among participating parties.
They have these attributes:
- Business network participation
- Consensus for transaction validation
- Provenance for audit trials
- Immutability regardless of space and time
- Finality to the absolute
Blockchain can operate in two modes—private and public. Blockchain is essentially an append-only distributed system of records shared across a business network where no one owns, anyone can add, and no one can delete it. And every transaction is secure, authenticated, and verifiable with appropriate visibility.
Machine learning use cases for blockchain technology
Blockchain technology has great potential beyond the financial industry. There are many profound applications of this technology across many industries, most importantly in the artificial intelligence and machine-learning aspects.
In a shared ledger system, there are two patterns of machine learning use cases:
- Silo machine learning and predictive models addressing a particular segment of the chain
- Model chains addressing a segment or the whole chain
The silo machine learning or predictive model is no different from what we do today with data at hand. Model chains are more complex, since they must learn and adjust on the fly given chain dependence.