I recently caught up with Razi Raziuddin, CEO and Co-founder of FeatureByte, to discuss how his startup with a data-centric AI solution simplifies feature engineering for data scientists. FeatureByte helps data scientists break down silos in AI practices, scale up feature engineering and scale up their AI. The company has $5.7M in funding, and an experienced team of former DataRobot execs, including a Kaggle Grandmaster (the #1 rank on Kaggle).
insideBIGDATA: Please introduce FeatureByte to our readers. Briefly, what is your mission statement for providing feature engineering solutions?
Razi Raziuddin: It’s a well known fact in the AI/ML world – that great AI starts with great data. But the process of getting the data prepared for modeling, and then deploying and managing it is very complex. That’s where data scientists spend a majority of their time. Unless it’s solved and simplified, the promise of AI everywhere in enterprises will remain just that – a promise. And that’s the problem we’re solving.
FeatureByte is an AI startup, headquartered in Boston and was founded in 2022. Our team includes several former executives from DataRobot and multiple Kaggle Grandmasters. We’re building a self-service feature platform that radically simplifies the entire feature lifecycle to scale and accelerate enterprise AI. The platform allows data scientists and ML engineers to create and share state-of-the art features and production-ready data pipelines in minutes, instead of weeks or months. By extending the modern data stack to streamline AI data pipelines, FeatureByte accelerates innovation while reducing compute and resources by 5X.
insideBIGDATA: Past Kaggle Grandmasters have said their path to success is tied to “clever feature engineering.” Why is this aspect of data science so important?
Razi Raziuddin: Despite the sexiness of algorithms and modeling, the fact remains that the quality of data drives the quality and performance of AI models. The process of transforming raw data into data that models can learn from and make predictions is called feature engineering. Features are attributes of entities and are data-based representations of the real world. The better the features capture information about real-world entities, the better the models learn and predict future events. That’s where clever feature engineering helps – to capture complex attributes such as purchase behaviors and patterns or similarities and differences between the interaction patterns of a particular age group or demographic.
Doing good feature engineering and deploying features in production requires three different skills – domain expertise, data science and data engineering. Bringing these skill sets together and building the expertise needed is a huge challenge for even the more technical organizations. That’s where FeatureByte comes in – we simplify and accelerate a process that is normally very complex, time consuming and expensive.
insideBIGDATA: How do you differentiate FeatureByte from companies in the feature store space like Tecton, SageMaker, Hopsworks and others?
Razi Raziuddin: Unlike feature stores that are designed for data engineers, FeatureByte is specifically designed for data scientists and ML engineers to manage the entire feature lifecycle in a self-service manner. Feature stores are like databases, whereas we’re building the equivalent of a CRM system for data science teams.
While feature stores simplify deploying feature pipelines, data science and ML engineering teams still contend with feature creation and reuse, slow experimentation and management of non-standard features. The handoff from data scientists to data engineering teams to implement features results in a lot of back and forth, introducing significant latency and cost. And without any standard process around creation and management of features, there is practically no reuse of features and collaboration across teams.
With our integrated self-service approach, FeatureByte allows data scientists to create state-of-the-art features with just a few lines of Python code and combine them with feature embeddings. These standardized features are easily shareable and reusable across data science and ML engineering teams via a self-organized catalog. Data scientists can access historical data immediately through automatic backfilling, allowing them to experiment rapidly. Deploying pipelines is just a matter of promoting features to ‘deployed.’ Enterprise-level security and governance workflows ensure that all the data and features are managed centrally. All the compute is pushed into the data platform itself, making the management and governance of data straightforward. The integration of platform and process simplifies the entire feature lifecycle for data science teams.
insideBIGDATA: Can you give us a glimpse of what’s on the horizon at FeatureByte?
Razi Raziuddin: This week we announced the availability of our enterprise platform. This self-service feature platform reduces the need for compute and personnel resources by up to 5X, reducing costs while improving data science productivity. Data science teams can derive a number of benefits from the platform – speed, efficiency, model performance, autonomy, governance and scale. This is just the beginning for us. We have a number of exciting capabilities planned on the roadmap that will further simplify and automate the entire feature lifecycle, greatly enhancing the productivity of data science and ML engineering teams.
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