Both DataStax and Kaskada have a track record of contributing to open source communities. Datastax will open source the core Kaskada technology initially, and it plans to offer a new machine learning cloud service later this year. Most machine learning initiatives don't deliver the results that businesses need because the process is manual, complex and frustrating. Compounding this problem, many models underperform because they lack the relevance and context of real-time data. The addition of Kaskada to DataStax's portfolio of cloud services--which today includes the massively scalable Astra DB database-as-a-service built on Apache Cassandra and event streaming with Astra Streaming-- will give organizations a single environment to easily and cost-effectively deliver applications infused with real-time AI, using an advanced ML/AI model proven by industry leaders such as Netflix and Uber.

As we now apply an increasing amount of Artificial Intelligence (AI) to our machines through increasingly sophisticated Machines Learning (ML) models, our machines are getting smarter all the time. We don't just need more intelligence, we need it according to specific vectors. Key among those vectors are AI at scale, AI that is validated, secured and bias-free (aka explainable AI) and AI engines that are capable of computation analysis in real-time. The question we must now ask is: should AI specialists develop more real-time competencies, should real-time data streaming specialists work to innovate new tiers of AI, or should the responsibility fall to higher-level data platform specialists or the hyperscaler Cloud Services Providers (CSPs) themselves? DataStax thinks this challenge is a data platform play, but then - it would, the company is an enterprise DataBase-as-a-Service (DBaaS) tools and technology specialist with a foundation in the Apache Cassandra open source database.