Seattle-based startup Kaskada unveils a new platform that opens up machine learning to small and mid-sized companies
Artificial intelligence and machine learning are rapidly changing how companies do business and serve their customers. These opportunities, however, tend to be exploited most by large technology companies with significant resources invested in data collection, data processing, and productionization of machine learning, while others often struggle to achieve the same level of results. A key missing piece of getting to success is a data infrastructure that bridges the gap between model training and live serving of machine learning results in production environments.
To bridge this gap and level the playing for businesses of all sizes, Kaskada, a Seattle-based machine learning startup, announced today the launch and general availability of a new engineering platform that opens up machine learning to small and mid-sized companies and not just enterprise. … and helps organizations of all sizes make better predictions for fraud identification, personalization, recommendations, and more.
The launch is the culmination of series of beta testing with early adopters. The platform is now ready and available for data science teams to use for a wide variety of use cases, including fraud, personalization, and recommendation engines.
Founded in 2018 by Ben Chambers and Davor Bonaci, the Seattle-based Kaskada empowers data scientists to collaborate on the feature engineering process and to achieve repeatable success with models running in production. Kaskada’s feature engineering platform provides a collaborative interface for data scientists and robust data infrastructure for computing, storing, and serving features in production.
Commenting on the launch, Davor Bonaci, Kaskada co-founder and CEO, said: “Kaskada’s feature engineering platform is designed to make truly hard data problems in machine learning easy. Data science teams can now work better together, build better features and deliver results at a whole new level. I cannot wait to see what kind of impact they’ll accomplish in the months and years to come.”
Some of the most impactful machine learning models use real-time, event-based data, which provides valuable insights into how behavior changes over time. This data type is one of the most difficult to handle because of the lack of efficient data infrastructure needed to calculate features at arbitrary points in time and to deliver such features to both training and production environments.
“The biggest obstacle for data scientists today isn’t building the fanciest models,” said Max Boyd, Data Science Lead at Kaskada. “It is the inability of current data platforms to bridge the gap between training and production, particularly with the computation of features derived from event-based data. In past roles, we struggled to use event-based data to its full potential because of infrastructure limitations and spent a lot of time hacking around the problem for minimal gains. Kaskada is a game-changer for building and operating quality machine learning models with event-based data.”
“Unlike many data products, Kaskada is available to individual data scientists and companies alike. It is free for many scenarios and requires no setup,” added Bonaci. “We invite data scientists with fraud, dynamic pricing, personalization, and similar event-based use cases to sign up, onboard, and join our growing data science community.”
Catch Kaskada in action at the Feature Stores for ML Global Meetup, a free event on March 16, 2021.
The Kaskada Feature Engineering Platform is available starting today. The platform is free to start and data scientists have the option to pay to add additional users, manage more data, and access additional features. Sign up and onboard at https://kaskada.com/.