Enterprise AI development has primarily been model-centric over the past decade. Data Scientists have benefited from advances in algorithms, AutoML, and MLOps tools for developing, deploying, and monitoring models. Yet, data science remains challenging and highly specialized. And scaling AI remains a distant dream for most enterprises.
Ask any data scientist, and they will tell you why. The process of preparing, deploying, managing, and monitoring data that feeds into these models, or “Feature Engineering”, is manual and very time-consuming.
It requires the confluence of three unique skills – domain knowledge, data science, and data engineering. Even in organizations with mature AI practices, these areas of expertise live in silos. And at the intersection of these silos lies a ton of friction.
At FeatureByte, our team has lived this problem, working with hundreds of enterprises on their AI journeys. We believe scaling feature engineering is a necessary step to achieving AI scale. We’re building a data-centric AI solution that radically simplifies feature engineering for data scientists.
If you are passionate about feature engineering or interested in learning more, please reach out at firstname.lastname@example.org. We love chatting about feature engineering and are always curious to learn from others’ experiences.
Interested in trying it out? We’re starting an invite-only Beta program shortly.
Xavier Conort, Co-founder & CPO
Xavier’s vision for FeatureByte is to simplify and democratize feature engineering for every data scientist. Xavier’s passion for data spans 25 years, first as an actuary in the Insurance industry, and then as a visionary Data Scientist, including being the top ranked (#1) on Kaggle.
Prior to starting FeatureByte, Xavier was the Chief Data Scientist of DataRobot, an AI unicorn. He built a world-class R&D data science team from day one, and was responsible for the ideation and execution of the Data Science roadmap.
Razi Raziuddin, Co-founder & CEO
Razi enjoys the challenge of bringing disruptive products to market that wow customers. His vision for FeatureByte 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 6 years. He pioneered a services-led go-to-market strategy that became the hallmark of DataRobot’s rapid growth. He’s passionate about building world-class teams and a culture of transparency and trust.
Kenny Chua, CTO
As CTO of FeatureByte, Kenny is building a world-class engineering team that transforms our vision for the future of feature engineering into reality.
Kenny was Head of R&D at DataRobot. He built and led teams that created key capabilities such as time series and data management. Kenny thrives on the intersection of data science and engineering. He’s passionate about building great teams and engineering category-defining products.
Sergey Yurgenson, Head of Semantic Data Science
Sergey is a world-renowned data scientist, a Kaggle Grandmaster and holder of the top rank (#1) on Kaggle. His goal at FeatureByte is to leverage data and domain semantics to change how feature engineering gets done.
Prior to FeatureByte, Sergey was a member of the founding team at DataRobot. He created and led a professional services team that helped grow DataRobot into a unicorn. He has been mentioned in various publications as one of the top data scientists in the world. Sergey is passionate about all things machine learning, predictive modeling and innovative feature engineering.