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

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


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 progress expertise spans the management workforce of two unicorn startups. Razi helped scale DataRobotic from 10 to 850 staff in below six years. He pioneered a services-led go-to-market technique that grew to become the hallmark of DataRobotic’s speedy progress.

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

What initially attracted you to pc science and machine studying?

As somebody who began coding in highschool, I used to be fascinated with a machine that I might “talk” to and management via code. I used to be immediately hooked on the limitless prospects of recent functions. Machine studying represented a paradigm shift in programming, permitting machines to be taught and carry out duties with out even specifying the steps in code. The infinite potential of ML functions is what will get me excited every single day.

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

Going from zero to 1 is difficult, however extremely thrilling and rewarding. Each 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’s fascinating to see how this expertise can be utilized throughout so many alternative industries and functions. At the top of the day, creating a brand new class is a uncommon feat, however an extremely rewarding one. My key takeaways from the expertise:

  • Build an incredible product and keep away from chasing fads
  • Don’t be afraid to be a contrarian
  • Focus on fixing buyer issues and offering worth
  • Always be open to innovation and making an attempt new issues
  • Create and inculcate the suitable firm tradition from the very begin

Could you share the genesis story behind FeatureByte?

It’s a widely known reality within the AI/ML world – that Great AI begins with nice information. But making ready, deploying and managing AI information (or Features) is complicated and time-consuming. My co-founder, Xavier Conort, and I noticed this drawback firsthand at DataRobotic. While modeling has turn out to be vastly simplified because of AutoML instruments, function engineering and administration stays an enormous problem. Based on our mixed expertise and experience, Xavier and I felt we might actually assist organizations remedy this problem and ship on the promise of AI in every single place.

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

Ultimately, the standard of knowledge drives the standard and efficiency of AI fashions. Data that’s fed into fashions to coach them and predict future outcomes is known as Features. Features signify details about entities and occasions, reminiscent of demographic or psychographic information of customers, 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 course of of remodeling uncooked information into options – to coach ML fashions and predict future outcomes – is known as function engineering.

Why is function engineering one of the sophisticated facets of machine studying initiatives?

Feature engineering is tremendous essential as a result of the method is immediately accountable for the efficiency of ML fashions. Good function engineering requires three pretty unbiased abilities to come back collectively – area information, information science and information engineering. Domain information helps information scientists decide what alerts to extract from the info for a specific drawback or use case. You want information science abilities to extract these alerts. And lastly, information engineering helps you deploy pipelines and carry out all these operations at scale on massive information volumes.

In 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 numerous friction within the course of and slows it all the way down to a grinding halt.

Could you share some perception on why function engineering is the weakest hyperlink in scaling AI?

According to Andrew Ng, famend knowledgeable in AI, “Applied machine learning is basically feature engineering.” Despite its criticality to the machine studying lifecycle, function engineering stays complicated, time consuming and depending on knowledgeable information. There is a severe dearth of instruments to make the method simpler, faster and extra industrialized. The effort and experience required holds enterprises again from having the ability to deploy AI at scale.

Could you share among the challenges behind constructing a data-centric AI resolution that radically simplifies function engineering for information scientists?

Building a product that has a 10X benefit over the established order is tremendous laborious. Thankfully, Xavier has deep information science experience that he’s using to rethink the complete function workflow from first rules. We have a world-class workforce of full-stack information scientists and engineers who can flip our imaginative and prescient into actuality. And customers and growth companions to advise us on streamlining the UX to greatest remedy their challenges.

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

Data preparation for ML is an iterative course of that depends on speedy experimentation. The open supply FeatureByte SDK is a declarative framework for creating state-of-the-art options with only a few traces of code and deploying information pipelines in minutes as a substitute of weeks or months. This permits information scientists to deal with artistic drawback fixing and iterating quickly on dwell information, relatively than worrying in regards to the plumbing.

The outcome will not be solely quicker information preparation and serving in manufacturing, but additionally improved mannequin efficiency via highly effective options.

Can you focus on how the FeatureByte platform will moreover provide the flexibility to streamline numerous ongoing administration duties?

The FeatureByte platform is designed to handle the end-to-end ML function lifecycle. The declarative framework permits FeatureByte to deploy information pipelines mechanically, whereas extracting metadata that’s related to managing the general surroundings. Users 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 information privateness and safety, whereas avoiding function sprawl.

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

Most enterprise AI instruments deal with enhancing machine studying fashions. We’ve made it a mission to assist enterprises scale their AI, by simplifying and industrializing AI information. At FeatureByte, we handle the largest problem for AI practitioners: Providing a constant, scalable approach to prep, serve and handle information throughout the complete lifecycle of a mannequin, whereas radically simplifying the complete course of.

If you’re a knowledge scientist or engineer fascinated by staying on the leading edge of knowledge science, I’d encourage you to expertise the ability of FeatureByte totally free.

Thank you for the good interview, readers who want to be taught extra ought to go to FeatureByte.

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