Razi Raziuddin, Co-Founder & CEO of FeatureByte – Interview Sequence

Razi Raziuddin is the Co-Founder & CEO of FeatureByte, his imaginative and prescient is to unlock the final main hurdle to scaling AI within the enterprise.  Razi’s analytics and development expertise spans the management workforce of two unicorn startups. Razi helped scale DataRobot from 10 to 850 staff in underneath six years. He pioneered a services-led go-to-market technique that turned the hallmark of DataRobot’s fast development.

FeatureByte is on a mission to scale enterprise AI, by radically simplifying and industrializing AI knowledge. The characteristic engineering and administration (FEM) platform empowers knowledge scientists to create and share state-of-the-art options and production-ready knowledge pipelines in minutes — as an alternative of weeks or months.

What initially attracted you to laptop science and machine studying?

As somebody who began coding in highschool, I used to be fascinated with a machine that I may “speak” to and management by way of code. I used to be immediately hooked on the infinite prospects of latest functions. Machine studying represented a paradigm shift in programming, permitting machines to study and carry out duties with out even specifying the steps in code. The infinite potential of ML functions is what will get me excited day-after-day.

You had been the primary enterprise rent at DataRobot, an automatic machine studying platform that permits organizations to grow to be AI pushed. You then helped to scale the corporate from 10 to 1,000 staff in underneath 6 years. What had been some key takeaways from this expertise?

Going from zero to at least one is difficult, however extremely thrilling and rewarding. Every stage within the firm’s evolution presents a unique set of challenges, however seeing the corporate develop and succeed is an incredible feeling.

My expertise with AutoML opened my eyes to the unbounded potential of AI. It is fascinating to see how this know-how can be utilized throughout so many alternative industries and functions. On the finish of the day, creating a brand new class is a uncommon feat, however an extremely rewarding one. My key takeaways from the expertise:

  • Construct an incredible product and keep away from chasing fads
  • Don’t be afraid to be a contrarian
  • Deal with fixing buyer issues and offering worth
  • All the time be open to innovation and attempting new issues
  • Create and inculcate the correct firm tradition from the very begin

Might you share the genesis story behind FeatureByte?

It is a well-known truth within the AI/ML world – that Nice AI begins with nice knowledge. However making ready, deploying and managing AI knowledge (or Options) is advanced and time-consuming. My co-founder, Xavier Conort, and I noticed this drawback firsthand at DataRobot. Whereas modeling has grow to be vastly simplified due to AutoML instruments, characteristic engineering and administration stays an enormous problem. Based mostly on our mixed expertise and experience, Xavier and I felt we may really assist organizations remedy this problem and ship on the promise of AI all over the place.

Characteristic engineering is on the core of FeatureByte, may you clarify what that is for our readers?

In the end, the standard of information drives the standard and efficiency of AI fashions. Information that’s fed into fashions to coach them and predict future outcomes known as Options. Options signify details about entities and occasions, equivalent to demographic or psychographic knowledge of shoppers, or distance between a cardholder and service provider for a bank card transaction or variety of gadgets of various classes from a retailer buy.

The method of reworking uncooked knowledge into options – to coach ML fashions and predict future outcomes – known as characteristic engineering.

Why is characteristic engineering some of the sophisticated features of machine studying initiatives?

Characteristic engineering is tremendous necessary as a result of the method is immediately chargeable for the efficiency of ML fashions. Good characteristic engineering requires three pretty impartial abilities to return collectively – area information, knowledge science and knowledge engineering. Area information helps knowledge scientists decide what alerts to extract from the info for a selected drawback or use case. You want knowledge science abilities to extract these alerts. And at last, knowledge engineering helps you deploy pipelines and carry out all these operations at scale on giant knowledge volumes.

Within the overwhelming majority of organizations, these abilities dwell in numerous groups. These groups use completely different instruments and don’t talk effectively with one another. This results in loads of friction within the course of and slows it all the way down to a grinding halt.

Might you share some perception on why characteristic engineering is the weakest hyperlink in scaling AI?

In line with Andrew Ng, famend skilled in AI, “Utilized machine studying is mainly characteristic engineering.” Regardless of its criticality to the machine studying lifecycle, characteristic engineering stays advanced, time consuming and depending on skilled information. There’s a severe dearth of instruments to make the method simpler, faster and extra industrialized. The hassle and experience required holds enterprises again from with the ability to deploy AI at scale.

Might you share a few of the challenges behind constructing a data-centric AI resolution that radically simplifies characteristic engineering for knowledge scientists?

Constructing a product that has a 10X benefit over the established order is tremendous onerous. Fortunately, Xavier has deep knowledge science experience that he’s using to rethink the complete characteristic workflow from first. We’ve a world-class workforce of full-stack knowledge scientists and engineers who can flip our imaginative and prescient into actuality. And customers and improvement companions to advise us on streamlining the UX to finest remedy their challenges.

How will the FeatureByte platform pace up the preparation of information for machine studying functions?

Information preparation for ML is an iterative course of that depends on fast experimentation. The open supply FeatureByte SDK is a declarative framework for creating state-of-the-art options with just some traces of code and deploying knowledge pipelines in minutes as an alternative of weeks or months. This enables knowledge scientists to concentrate on inventive drawback fixing and iterating quickly on dwell knowledge, fairly than worrying concerning the plumbing.

The consequence shouldn’t be solely sooner knowledge preparation and serving in manufacturing, but in addition improved mannequin efficiency by way of highly effective options.

Are you able to talk about how the FeatureByte platform will moreover provide the power to streamline varied ongoing administration duties?

The FeatureByte platform is designed to handle the end-to-end ML characteristic lifecycle. The declarative framework permits FeatureByte to deploy knowledge pipelines routinely, whereas extracting metadata that’s related to managing the general surroundings. Customers can monitor pipeline well being and prices, and handle the lineage, model and correctness of options all from the identical GUI. Enterprise-grade role-based entry and approval workflows guarantee knowledge privateness and safety, whereas avoiding characteristic sprawl.

Is there anything that you just want to share about FeatureByte?

Most enterprise AI instruments concentrate on enhancing machine studying fashions. We have made it a mission to assist enterprises scale their AI, by simplifying and industrializing AI knowledge. At FeatureByte, we tackle the most important problem for AI practitioners: Offering a constant, scalable option to prep, serve and handle knowledge throughout the complete lifecycle of a mannequin, whereas radically simplifying the complete course of.

When you’re a knowledge scientist or engineer fascinated by staying on the innovative of information science, I’d encourage you to expertise the ability of FeatureByte free of charge.

Thanks for the good interview, readers who want to study extra ought to go to FeatureByte.

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