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AITech interview with Dmitry Petrov, Co-Founder & CEO at Iterative.ai
Could you tell us more about Iterative.ai’s and what was the inspiration behind starting this company?
As a data scientist at Microsoft, I saw first hand the problems that occur when the data science and software engineering environments don’t work together. There is essentially a wall between the two worlds.

AITech interview with Dmitry Petrov, Co-Founder & CEO at Iterative.ai

Could you tell us more about  Iterative.ai’s and what was the inspiration behind starting this company?

As a data scientist at Microsoft, I saw first hand the problems that occur when the data science and software engineering environments don’t work together. There is essentially a wall between the two worlds. I wanted to build a company that would make tools to remove this wall so that the machine learning world and software engineering world could live together and be more productive. This way, machine learning models that are built can be easily put into production apps and services.

2. Could you elaborate on your entrepreneurial journey?

I knew I always wanted to build a company.  My journey from a remote area of Russia, through my computer science education, move to the States with a Data Science position at Microsoft, boot-strapping a start-up and becoming and American citizen has been long, but so rewarding.  I’m proud of the products we are building and the team that is building them.

3. Can you tell us about the products and services that Iterative.ai offers? 

Iterative’s mission is to deliver the best developer experience for machine learning teams by creating an ecosystem of open, modular ML tools.  This mission was born out of the need for harmoniously joining the best practices of software engineering with machine learning projects that are driving a growing number of software applications today.  DVC (Data Version Control), the first tool of the suite, extends the versioning power of Git to the large and often unstructured datasets used in ML projects.  For the first time data, pipeline and experiments could be versioned reliably and reproducibly, supporting the data-centric movement and the growing need for data governance in ML projects.