Machine Learning and Blockchain

How can these two technologies work together?

Both machine learning and blockchain have become increasingly popular in the past few years. However, they are mostly seen as separate entities and not something that can work together. It seems that the two will start working together, something that might end up being extremely helpful.

Robot Swarm
Robot Swarm

Machine learning

The main focus of machine learning is to offer machines a way to function without human attention. It’s important for us because it helps remove the hassle and it makes it easier to focus on the day to day stuff. Machine learning is also designed to use large amounts of data in order to find patterns. Once they do that, they will have a much more efficient system since it learns from mistakes. There are many machine learning systems out there like fitness tracking hardware, speech recognition tools, and so on.

There are also machine learning algorithms in the works that will help identify financial risks before anything might happen. It helps companies make better decisions and figure out any kind of risks that might appear without even knowing!


The blockchain is designed to be one of the most secure databases in the world. It helps save data instances in a ledger, all of which are decentralized. It relies on a digital signature to ascribe ownership of entities. As a result, you have a decentralized ledger that’s very resistant to any type of censorship. That’s why you can store data safely on the blockchain because the risks are pretty much zero. However, the blockchain is great if you want to store information that’s not going to change anytime soon.

Machine learning with the blockchain

Different blockchain platforms such as Cardano or Ethereum provide the tools needed to interact with both, items in the physical world and a tokenized version inside a blockchain (representing fractions of ownership or a value stake). Machine Learning focuses on the use of large data quantities so it can create accurate prediction models of the data stored in any blockchain.

Machine Learning Analytics can be used to:

  • Predict future behaviors of a data series recorded in the blockchain (temperature inside an industrial oven, for example)

  • Find and better understand Supply Network flows.

  • Detect abnormal behaviors or outliers in data (Finance movements for fraud detection)

  • Search for clusters of agents acting together. Transaction groups or sequences.

  • Classify transactions according to some metrics to help Anti-Money-Laundering (AML) policies.

  • Find similarities in different datasets or timeframes.

  • Analyze how a Smart Contract is used and find logical weaknesses inside it.

  • Find causality between different blockchain recordings: Intellectual property analysis to verify Mathematical proof authorship, for example.

However, there’s a lot of work needed since the data needs to be acquired, processed, and then audited. ML

With help from blockchain technology and smart contracts, data can be transferred quickly. Normally you will have data acquired by trackers, then it’s sent to a facility where auditors go through it to see if it’s authentic and then data scientists receive it. With smart contracts, things are better because digital signatures improve the overall speed and efficiency. Smart contracts can be programmed to share data directly with scientists so they can create machine learning models. Companies like Tikblue, in Spain, are implementing Blockchain and Machine Learning techniques in their products aimed to ease Digital Transformation for their clients.


The combination of machine learning and blockchain can revolutionize the business world. It helps acquire, process, and analyze data a lot faster. It can be great for just about any field, especially the financial world where you can figure out and stop any signs of fraud. That’s why we need to focus on combining blockchain and machine learning as much as possible because the potential is huge here!

  • Facebook
  • Instagram
  • TikTok
  • Twitter
  • LinkedIn