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.

DataStax, the driving force behind the ongoing development of and commercialization of the open source NoSQL Apache Cassandra database, had been in business for nine years in 2019 when it made a hard shift to the cloud. The company had already been working with organizations whose businesses already stretched into hybrid and multicloud environments, but its "cloud first" strategy was designed to make it easier for the company to grow and easier for customers to consume Cassandra. This cloud first approach is shared by many established and startup software companies alike. Back then, DataStax had just unveiled Constellation, a cloud data platform for developers to build newer application and operations teams to manage them, with the first offering on the platform being DataStax Apache Cassandra as a Service. A year later, the company announced its Astra database cloud service and in 2021 released a new version of Astra for serverless deployments. The transition to the cloud was important in making it easier for enterprises to use Cassandra, according to Ed Anuff, chief product officer at DataStax.

More than a year after announcing plans to automate the feature engineering phase of artificial intelligence projects, Seattle-based startup Kaskada Inc. is bringing its first product to market. Kaskada says it aims to democratize feature engineering, an often laborious process that requires data scientists to select, clean and validate the data to be fed into machine learning training models prior to moving them into production. A model intended to predict housing prices, for example, would be feature engineered with predictor data such as the square footage of properties, number of bedrooms and location. The larger and more complete the training data set, the better the results. The resources required to collect data and move machine learning models into production can be so significant that the capabilities are out of reach of all but the largest companies.

In this special guest feature, Emily Kruger, Vice President of Product at Kaskada, discusses the topic that is on the minds of many data scientists and data engineers these days, maximizing the impact of machine learning in production environments. Kaskada is a machine learning company that enables collaboration among data scientists and data engineers. Kaskada develops a machine learning studio for feature engineering using event-based data. Kaskada's platform allows data scientists to unify the feature engineering process across their organizations with a single platform for feature creation and feature serving. Machine learning is changing the way the world does business.

In this regular insideBIGDATA feature we highlight our industry's movers and shakers, companies that are pushing technology forward, and setting trends for innovation. We look at companies with a focus on big data, data science, machine learning, AI and deep learning – some new, some old, always leading, always dynamic. We also take deep dives into new technology promoted (or hyped) as "AI" or my favorite "AI-powered" to provide transparency for what's really going on under the hood. In this installment of "AI Under the Hood" I introduce Kasakda, Inc., a Seattle-based early stage company founded in January 2018. Kaskada is a machine learning platform for feature engineering using event-based data.