Data Science at Home

In the attempt of democratizing machine learning, data scientists should have the possibility to train their models on data they do not necessarily own, nor see. A model that is privately trained should be verified and uniquely identified across its entire life cycle, from its random initialization to setting the optimal values of its parameters.

In the attempt to democratize machine learning, data scientists should have the possibility to train their models on data they do not necessarily own, nor see. A model that is privately trained should be verified and uniquely identified across its entire life cycle, from its random initialization to setting the optimal values of its parameters.
How does blockchain allow all this? Fitchain is the decentralized machine learning platform that provides models an identity and a certification of their training procedure, the proof-of-train

I know, I have been away too long without publishing much in the last 3 months.

If you want to collaborate on the project or just think it's interesting, drop me a line on the contact page at fitchain.io

I know, I have been away too long without publishing much in the last 3 months.
But, there's a reason for that. I have been building a platform that combines machine learning with blockchain technology.
Let me introduce you to fitchain and tell you more in this episode.

If you want to collaborate on the project or just think it's interesting, drop me a line on the contact page at fitchain.io

Francesco Gadaleta introduces Fitchain, a decentralized machine learning platform that combines blockchain technology and AI to solve the data manipulation problem in restrictive environments such as healthcare or financial institutions.Francesco Gadaleta is the founder of Fitchain.io and senior advisor to Abe AI. Fitchain is a platform that officially started in October 2017, which allows data scientists to write machine learning models on data they cannot see and access due to restrictions imposed in healthcare or financial environments. In the Fitchain platform, there are two actors, the data owner and the data scientist. They both run the Fitchain POD, which orchestrates the relationship between these two sides. The idea behind Fitchain is summarized in the thesis “do not move the data, move the model – bring the model where the data is stored.”

The Fitchain team has also coined a new term called “proof of train” – a way to guarantee that the model is truly trained at the organization, and that it becomes traceable on the blockchain. To develop the complex technological aspects of the platform, Fitchain has partnered up with BigChainDB, the project we have recently featured on Crypto Radio.

Fitchain team is currently validating the assumptions and increasing the security of the platform. In the next few months, they will extend the portfolio of machine learning libraries and are planning to move from a B2B product towards a Fitchain for consumers.

By June 2018 they plan to start the Internet of PODs. They will also design the Fitchain token – FitCoin, which will be a utility token to enable operating on the Fitchain platform.

Data is a complex topic, not only related to machine learning algorithms, but also and especially to privacy and security of individuals, the same individuals who create such data just by using the many mobile apps and services that characterize their digital life.

In this episode I am together with B.J.n Mendelson, author of “Social Media is Bullshit” from St. Martin’s Press and world-renowned speaker on issues involving the myths and realities involving today’s Internet platforms.  B.J. has a new a book about privacy and sent me a free copy of "Privacy, and how to get it back" that I read in just one day. That was enough to realise how much we have in common when it comes to data and data collection.