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.

It's high time to treat HR as every bit as important to your company's artificial intelligence strategy as IT. Alongside all the evidence that getting your developers working on AI is good for your business, there's mounting proof that even providing the opportunity to work on--and work with--AI has a positive effect on job satisfaction, recruitment, and retention. Getting this right matters a lot today. In 2022, McKinsey's State of AI Report notes that "[s]oftware engineers emerged as the AI role that survey responses show organizations hired most often in the past year, more often than data engineers and AI data scientists … another clear sign that many organizations have largely shifted from experimenting with AI to actively embedding it in enterprise applications." And the stakes are high.

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.

The acquisition is a natural step for the data vendor, according to Carl Olofson, an analyst at IDC. Since Cassandra is an SQL database, it has considerable flexibility, including the ability to set up training data for ML, Olofson said. However, many users who are not ML experts may need help with using it to set up training data for ML. This is where Kaskada fits in. The Seattle-based vendor provides users with tools to train behavioral ML models directly from event-based data produced from the tracking and analysis of the interactions between customers and a product.

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.

We're just now sending invites and beginning to confirm speakers for the upcoming edition in January. If you'd like to join us as a speaker, take a look at our Proposals page. As Vice President of Developer Experience at Treeverse, Adi Polak shapes the future of data & ML technologies for hands-on builders. She also contributes to the lakeFS open-source, a git-like interface for object stores. In her work, she brings her vast industry research and engineering experience to bear in educating and helping teams design, architect, and build cost-effective data systems and machine learning pipelines that emphasize scalability, expertise, and business goals.

DataStax, the real-time data company, announced the general availability (GA) of Astra Streaming, an advanced, fully-managed messaging and event streaming service built on Apache Pulsar. Now featuring built-in API-level support for Kafka, RabbitMQ and Java Message Service (JMS), Astra Streaming makes it easy for enterprises to get real-time value from all their data-in-motion. "Because business happens in real time, continuously processing streams of data is imperative for enterprises to optimize decisions, actions and experiences. Streaming data can be a game changer for companies to make predictive business decisions and gain competitive advantages." "Many enterprises are struggling with fragmented and complex streaming architectures, with most of their data-in-motion still siloed in legacy messaging and queuing middleware like JMS and RabbitMQ," said Chris Latimer, vice president of product management at DataStax.

One of the most common questions I get asked is, 'What is the best database for Machine Learning?' In reality, the answer I give is nearly always, 'It depends', before bombarding the enquirer with a series of follow-up questions. But because'It depends' is never fun in blog form I have put together this list. Machine Learning has now penetrated every aspect of our lives whether you realise it or not. From the video recommendations you see on YouTube, the systems keeping you safe while you bank or shop online all the way to the processing of images on your smartphone.

November 1, 2021 -- MindsDB, an open-source machine learning (ML) startup that brings machine learning (ML) to databases, announced today an investment from Walden Catalyst Ventures, closing out MindsDB's total seed round to $7.6M. Walden Catalyst Ventures joins YCombinator, OpenOcean (the venture fund launched by the creators of MySQL and MariaDB), SpeedInvest, and the University of California Berkeley SkyDeck fund. MindsDB's mission is to democratize ML by giving enterprise databases "a brain," driving better, data-driven business decisions without enterprises needing to become AI developers or experts. With MindsDB's platform, companies can leverage machine learning capabilities with standard SQL knowledge, allowing enterprises to lower development costs while accelerating machine learning capabilities. The platform is already used by several thousand open-source developers for everything from forecasting heart disease risk to insurance premium forecasts.

Imagine a world without government, schools, a legal system, law enforcement, or companies. It's a world unlike the one we currently live in--but based on the evolution of technology and how we use it--representative of what the world may become. Imagine a computer infrastructure that could--with global knowledge and the ability to enact precise tweaks to the social and economic structure--drive the evolution of society. This is the idea behind the Universal Graph. In mathematics, this is a graph (or network) in which a piece of information can be connected with other pieces of information until all finite information is integrated.