Razi Raziuddin is the Co-Founder & CEO of FeatureByte, his vision is to unlock the last major hurdle to scaling AI in the enterprise. Razi’s analytics and growth experience spans the leadership team of two unicorn startups. Razi helped scale DataRobot from 10 to 850 employees in under six years. He pioneered a services-led go-to-market strategy that became the hallmark of DataRobot’s rapid growth.
FeatureByte is on a mission to scale enterprise AI, by radically simplifying and industrializing AI data. The feature engineering and management (FEM) platform empowers data scientists to create and share state-of-the-art features and production-ready data pipelines in minutes — instead of weeks or months.
What initially attracted you to computer science and machine learning?
As someone who started coding in high school, I was fascinated with a machine that I could “talk” to and control through code. I was instantly hooked on the endless possibilities of new applications. Machine learning represented a paradigm shift in programming, allowing machines to learn and perform tasks without even specifying the steps in code. The infinite potential of ML applications is what gets me excited every day.
You were the first business hire at DataRobot, an automated machine learning platform that enables organizations to become AI driven. You then helped to scale the company from 10 to 1,000 employees in under 6 years. What were some key takeaways from this experience?
Going from zero to one is hard, but incredibly exciting and rewarding. Each stage in the company’s evolution presents a different set of challenges, but seeing the company grow and succeed is an amazing feeling.
My experience with AutoML opened my eyes to the unbounded potential of AI. It’s fascinating to see how this technology can be used across so many different industries and applications. At the end of the day, creating a new category is a rare feat, but an incredibly rewarding one. My key takeaways from the experience:
- Build an amazing product and avoid chasing fads
- Don’t be afraid to be a contrarian
- Focus on solving customer problems and providing value
- Always be open to innovation and trying new things
- Create and inculcate the right company culture from the very start
Could you share the genesis story behind FeatureByte?
It’s a well-known fact in the AI/ML world – that Great AI starts with great data. But preparing, deploying and managing AI data (or Features) is complex and time-consuming. My co-founder, Xavier Conort, and I saw this problem firsthand at DataRobot. While modeling has become vastly simplified thanks to AutoML tools, feature engineering and management remains a huge challenge. Based on our combined experience and expertise, Xavier and I felt we could truly help organizations solve this challenge and deliver on the promise of AI everywhere.
Feature engineering is at the core of FeatureByte, could you explain what this is for our readers?
Ultimately, the quality of data drives the quality and performance of AI models. Data that is fed into models to train them and predict future outcomes is called Features. Features represent information about entities and events, such as demographic or psychographic data of consumers, or distance between a cardholder and merchant for a credit card transaction or number of items of different categories from a store purchase.
The process of transforming raw data into features – to train ML models and predict future outcomes – is called feature engineering.
Why is feature engineering one of the most complicated aspects of machine learning projects?
Feature engineering is super important because the process is directly responsible for the performance of ML models. Good feature engineering requires three fairly independent skills to come together – domain knowledge, data science and data engineering. Domain knowledge helps data scientists determine what signals to extract from the data for a particular problem or use case. You need data science skills to extract those signals. And finally, data engineering helps you deploy pipelines and perform all those operations at scale on large data volumes.
In the vast majority of organizations, these skills live in different teams. These teams use different tools and don’t communicate well with each other. This leads to a lot of friction in the process and slows it down to a grinding halt.
Could you share some insight on why feature engineering is the weakest link in scaling AI?
According to Andrew Ng, renowned expert in AI, “Applied machine learning is basically feature engineering.” Despite its criticality to the machine learning lifecycle, feature engineering remains complex, time consuming and dependent on expert knowledge. There is a serious dearth of tools to make the process easier, quicker and more industrialized. The effort and expertise required holds enterprises back from being able to deploy AI at scale.
Could you share some of the challenges behind building a data-centric AI solution that radically simplifies feature engineering for data scientists?
Building a product that has a 10X advantage over the status quo is super hard. Thankfully, Xavier has deep data science expertise that he’s employing to rethink the entire feature workflow from first principles. We have a world-class team of full-stack data scientists and engineers who can turn our vision into reality. And users and development partners to advise us on streamlining the UX to best solve their challenges.
How will the FeatureByte platform speed up the preparation of data for machine learning applications?
Data preparation for ML is an iterative process that relies on rapid experimentation. The open source FeatureByte SDK is a declarative framework for creating state-of-the-art features with just a few lines of code and deploying data pipelines in minutes instead of weeks or months. This allows data scientists to focus on creative problem solving and iterating rapidly on live data, rather than worrying about the plumbing.
The result is not only faster data preparation and serving in production, but also improved model performance through powerful features.
Can you discuss how the FeatureByte platform will additionally offer the ability to streamline various ongoing management tasks?
The FeatureByte platform is designed to manage the end-to-end ML feature lifecycle. The declarative framework allows FeatureByte to deploy data pipelines automatically, while extracting metadata that is relevant to managing the overall environment. Users can monitor pipeline health and costs, and manage the lineage, version and correctness of features all from the same GUI. Enterprise-grade role-based access and approval workflows ensure data privacy and security, while avoiding feature sprawl.
Is there anything else that you would like to share about FeatureByte?
Most enterprise AI tools focus on improving machine learning models. We’ve made it a mission to help enterprises scale their AI, by simplifying and industrializing AI data. At FeatureByte, we address the biggest challenge for AI practitioners: Providing a consistent, scalable way to prep, serve and manage data across the entire lifecycle of a model, while radically simplifying the entire process.
If you’re a data scientist or engineer interested in staying at the cutting edge of data science, I’d encourage you to experience the power of FeatureByte for free.
Thank you for the great interview, readers who wish to learn more should visit FeatureByte.
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