Domino Data Lab is launching the company's first partner program today as the data science and MLOps software developer looks to scale up its work with service and technology partners and provide them with a structured program with more resources and benefits. The new Domino Partner Network will provide structure for what has largely been ad hoc partner processes, according to Domino executives. It will help the company expand and scale its work with partners and provide them with needed training, go-to-market resources and incentives. "Our push to formalize this partner program and to work with a wider range of partners is really being driven by our customers," CEO Nick Elprin said in an interview with CRN. "At a high level, what we're doing is formalizing an approach and a structure for how we work with partners."
Artificial intelligence startup Domino Data Lab Inc. said Tuesday it raised $100 million in new funding amid increased business interest in tools that help data scientists build and deploy AI applications. The funding will be used to scale its sales organization and build out its machine-learning platform's features and functions, said Nick Elprin, Domino Data's chief executive and one of its co-founders. Domino Data has raised $228 million since its founding in 2013. Private-equity firm Great Hill Partners led the series F round with participation from graphics-chip maker Nvidia Corp. and existing investors Coatue Management, Highland Capital Partners and Sequoia Capital. The company didn't disclose its valuation.
We knew AI was hot, but the last week has brought several announcements that show the industry is trying to bring AI and machine learning (ML) into the enterprise mainstream. With two AI acquisitions by big names in the analytics space and a $100M funding round for an MLOps specialist, this has been a week of AI acceleration. Today's news is that Databricks is acquiring German company 8080 Labs, makers of bamboolib, a UI-based data science tool that generates code, but doesn't require users to write any. And this follows the announcement earlier this year from Databricks that it had added AutoML capabilities to its data "lakehouse" platform. Clemens Mewald, Databricks' Director of Product Management, Data Science and Machine Learning, spoke to ZDNet and explained that bamoboolib is presently offered as a Jupyter notebook plug-in and will, logically, be integrated with Databricks' own notebooks. Afterwards, it will also be integrated into the Databricks workspace user interface, to make it available to less technical users, often referred to as "citizen data scientists."
Solutions Review's listing of the best advanced analytics software, applications, and tools is an annual sneak peek of the top tools included in our Buyer's Guide for Data Science and Machine Learning Platforms. Information was gathered via online materials and reports, conversations with vendor representatives, and examinations of product demonstrations and free trials. The editors at Solutions Review have developed this resource to assist buyers in search of the best advanced analytics software, applications, and tools to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process -- one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we've profiled the best advanced analytics software providers all in one place.
Nearly every American executive is leaning on data science to gain a competitive edge and boost profit, according to a newly released survey by Domino Data Lab. But flaws in the people, processes, and tools–not to mention a focus on "splashy" projects that lack substance–are holding back those data science dreams, the survey found. Domino's new study, dubbed the "Data Science Needs to Grow Up: The 2021 Domino Data Lab Maturity Index," found that 71% of the 300 data execs at large companies it surveyed are counting on data science to drive revenue growth. In fact, 25% of the execs expect double-digit growth. Despite the lofty ambitions, the companies are simply not making the investments necessary to achieve those results.