Event processing and time-centric calculations are essential for businesses to leverage the power of real-time data for machine learning and other applications. Check out our blog and learn more https://t.co/jHCrZOIM1H
#AI#MachineLearning#realtimeai#Data
Unlock the power of Kaskada's query language! 🚀 Seamlessly navigate and manipulate data across different time points for efficient data analysis. https://t.co/e5Dutqp4xq
#datatransformation#dataanalysis#ml#ai#data
Building real-time models can be challenging. @KaskadaOSS offers tools and technologies that simplify the process, making it easier and more efficient. https://t.co/60y31JUWkv #machinelearning#AI
Kaskada OSS single, high-level, declarative query language simplifies #DataEngineering and makes building data pipelines faster and more efficient. With Kaskada OSS, you can unlock insights from your data with ease. https://t.co/EJvfPCJkgD #MachineLearning#OpenSource
Kaskada OSS – an event-processing engine built on Rust and Apache Arrow. It features a high-level, declarative query language designed specifically for reasoning about events in bulk and in real time. Learn more: https://t.co/YWPWUsdpEd #OpenSource#DataEngineering
Excited to announce Kaskada has been acquired by @DataStax! Together, we’ll help businesses enable #realtimeAI, fueled with data from DataStax Astra DB.
https://t.co/DAukRpx38x
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 https://t.co/xKvDqpCAZk
#ml#machinelearning#datascientists#data#Algorithms
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: https://t.co/d9mGvfrn4O
#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. https://t.co/SECEEQOaWt
#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: https://t.co/bXAPUxxBxq
#ml#data#machinelearning#dataandanalytics#DataScience
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
Predicting customer behavior is not easy. Discover how different model contexts affect behavior. Read our blog: www.kaskada.comhttps://https://t.co/bXAPUxxBxq
#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
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
https://t.co/Gc7UqY3G0Q
#ml#ai#data#DataEngineering