Kaskada is an innovative, Seattle-based, machine learning company that is leveling up the data science and machine learning industries. We are the company that first solved temporal streaming join, helping users create and operate predictive models with event-based data. Now, users can build models that weren't previously possible, that will actually work once put in production – without leakage.
With Kaskada, you can choose to calculate all feature values at any point in time. Or, you can calculate the feature values for each entity at the time an event occurred. For instance, calculate all features at the exact time a user made a large purchase, when a customer churned out 30 days after their planned subscription date, or at the time of a fraudulent transaction.
Use these point-in-time and event-driven feature values to train models without risk of leakage. When you're ready, you can compute the same feature values with a time of "now" to make new predictions using a live model in production.
Quickly try ideas on historical data by computing the prediction and label times for each training example directly from event times and fields. Iteration enables exploration and discovery. Check out Kaskada in action on industry-specific solutions and try it yourself!
With Kaskada, you can choose to calculate all feature values at any point in time. Or, you can calculate the feature values for each entity at the time an event occurred. For instance, calculate all features at the exact time a user made a large purchase, when a customer churned out 30 days after their planned subscription date, or at the time of a fraudulent transaction.
Use these point-in-time and event-driven feature values to train models without risk of leakage. When you're ready, you can compute the same feature values with a time of "now" to make new predictions using a live model in production.
Quickly try ideas on historical data by computing the prediction and label times for each training example directly from event times and fields. Iteration enables exploration and discovery. Check out Kaskada in action on industry-specific solutions and try it yourself!
Location: United States, Washington, Seattle
Employees: 11-50
Total raised: $8M
Founded date: 2016
Investors 3
Funding Rounds 1
| Date | Series | Amount | Investors |
| 04.02.2020 | Series A | $8M | - |
Mentions in press and media 19
| Date | Title | Description |
| 12.01.2023 | The Real-Time AI Data Race Is On | TOPSHOT - Corgi dogs race during the Southern California "Corgi Nationals" championship at the Santa ... [+] Anita Horse Racetrack in Arcadia, California on May 26 2019. - The event saw hundreds of Corgi dogs compete for the covet... |
| 28.10.2021 | Kaskada Brings New Method of Time Travel to Feature Engineering | Kaskada announced the broad availability of the first-ever feature engine with time travel. The company’s approach to this methodology is vastly different from competitors, and current customers are already benefiting from improved data mod... |
| 16.10.2021 | The 2021 machine learning, AI, and data landscape | We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today! It’s been a hot, hot year in the world of data, m... |
| 16.10.2021 | The 2021 machine learning, AI, and data landscape | It’s been a hot, hot year in the world of data, machine learning, and AI. Just when you thought it couldn’t grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project laun... |
| 04.10.2021 | Как поссорились Инженер и Ученый | Статья про данные для ML и FeatureStore Это материал из цикла статей о ModelOps от команды Advanced Analytics GlowByte. В предыдущих статьях мы уже рассказывали про: 5 столпов MLOps Как контейнеризировать среды ML разработки и не посадить н... |
| 08.08.2021 | 5 tips for improving your data science workflow | All the sessions from Transform 2021 are available on-demand now. Watch now. The biggest wastes in data science and machine learning don’t stem from inefficient code, random bugs, or incorrect analysis. They stem from flaws in planning and ... |
| 08.08.2021 | 5 tips for improving your data science workflow | We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today! The biggest wastes in data science and machine le... |
| 02.03.2021 | Kaskada launches platform to prep data for AI models | We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today! Feature engineering, or the process of using doma... |
| 02.03.2021 | Kaskada launches platform to prep data for AI models | Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more. Feature engineering, or the process of using domain knowledge to extract features from data, is essential to tuning AI and machine learning performan... |
| 21.11.2020 | Practical strategies to minimize bias in machine learning | We’ve been seeing the headlines for years: “Researchers find flaws in the algorithms used…” for nearly every use case for AI, including finance, health care, education, policing, or object identification. Most conclude that if the algorithm... |
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