Kaskada (acquired by DataStax)

Excited to announce Kaskada has been acquired by @DataStax! Together, we’ll help businesses enable #realtimeAI, fueled with data from DataStax Astra DB. tinyurl.com/3kb74djx

Data scientists spend 60% of their time cleaning data rather than collecting or analyzing important information. Learn how data quality can impact machine learning algorithms. Read more lnkd.in/g5zFkp4R #ml #machinelearning #datascientists #data #Algorithms

Want to learn when should you use FOSS? Read our blog by Ryan Michael, VP of Engineering at Kaskada. lnkd.in/gSFdVwVj #opensource #data #dataandanalytics #MachineLearning

Why you need a feature engine 1. Accelerate the feature engineering process and iterate quickly 2. Discover how different situations impact behavior and decisions 3. Deploy new features to production with a click of a button Learn more: lnkd.in/geniEe7S #engineering #ml

Behavioral ML models are challenging to develop and deploy using traditional compute engines. Try out Kaskada to instantly explore thousands more hypothesis and reduce time-to-production by 26x. lnkd.in/gT3bhBQK #ml #machinelearning #dataandanalytics #data #mlmodels

Model Context refers to the time domain used by a model to make predictions and therefore the relative time-slices that can be used to select training data for an ML model. Learn more here: lnkd.in/gCgug9Me #ml #data #machinelearning #dataandanalytics #DataScience

Kaskada - the only feature engine designed from the ground up for time-based feature engineering. It is built by data scientists for data scientists. Learn more at kaskada.com #datascientists #features #ml #machinelearning #data #dataandanalytics

Kaskada’s feature engineering language, FENL, makes defining and calculating features on event-based data simpler and more maintainable than the equivalents in the most common languages for building features on data, such as SQL and Python Pandas. #MachineLearning #DataScience

Do you want to learn how the Kaskada Feature Engine understands time? Read our blog by Brian Godsey, Data Science Lead at Kaskada, here: lnkd.in/gCv36WwM #data #datascience #ml #machinelearning #dataengineers #datacloud #dataandanalytics

For real-time ML, choosing the appropriate model context to incorporate into your model’s training is a crucial element of your feature engineering process. Learn more at lnkd.in/gCgug9Me #dataanalytics #data #datascience #ml, #machinelearning #behavioralscience

Predicting customer behavior is not easy. Discover how different model contexts affect behavior. Read our blog: www.kaskada.comhttps://lnkd.in/gCgug9Me #bahavior #ml #machinelearning #data #DataAnalytics

Kaskada enables data scientists and engineers to make optimal decisions when choosing how to train their models. It ultimately leads to improved model performance and unmatched business growth. #growth #engineers #datascientists #ml #machinelearning #Data

Do you know that with Kaskada’s feature engine, you can better understand behavior and build high-quality features for machine learning models? Learn more kaskada.com #machinelearning #features #data #dataandanalytics #behavioralscience

Would you like to learn how Kaskada’s feature engine adds to your existing data processing tools and understand how behavioral machine learning models can be built and deployed 26x faster? Watch our demo for data engineers lnkd.in/g2gyjFhu #ml #ai #data #DataEngineering