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.

MindsDB announces an integration with MariaDB to enable machine learning to the wildly popular open source relational database, furthering the mission to democratize machine learning. MindsDB, the open source AI layer for existing databases, today announced their official integration with the widely used open source relational database, MariaDB Community Server. This integration fills a longstanding demand of database users for the ability to bring machine learning capabilities to the database and democratize ML use. MindsDB helps apply machine learning models straight in the database by providing an AI layer that allows database users to deploy state-of-the-art machine learning models using standard SQL queries. The use of AI-Tables helps database users leverage predictive data inside the database for easier and more effective machine learning projects.

Amazon Web Services Inc. today launched a new open-source library to help developers write, with just a few lines of code, machine learning-based applications that use image, text or tabular data sets. Building machine learning apps that rely on such data isn't an easy task. For example, developers need to know how to tune the "hyperparameters" that represent the choices made when constructing an AI model. They also need to grapple with issues such as neural architecture search, which enables them to find the best architecture design for their machine learning models. AutoGluon automates many of these complicated tasks and can create a new machine earning model with as little as three lines of code by automatically tuning choices within default ranges that are known to perform well for a given task.

Spreading the use of machine learning tools is one of the goals of Google's PAIR (People AI Research) initiative, which was introduced in early July. Last week the cloud giant released deeplearn.js Writing on the Google Research blog last Friday, software engineers Nikhil Thorat and Daniel Smilkov noted, "There are many reasons to bring machine learning into the browser. A client-side ML library can be a platform for interactive explanations, for rapid prototyping and visualization, and even for offline computation. And if nothing else, the browser is one of the world's most popular programming platforms."

Why is it important to democratize machine learning? Why is it important to democratize machine learning? For two reasons: first, because machine learning and artificial intelligence have tremendous potential for value creation, and we should not let any of that potential go to waste. People with the drive and intelligence required to advance the field of ML or to use AI to create great things can come from anywhere, and we have a responsibility to make sure that these people can readily access the knowledge and tools they will need to realize their full potential. What's more, we should do our best to inspire capable people the world over so that they will want to dedicate their talents to value creation through AI.

Leveraging the power of machine learning in healthcare to improve outcomes has primarily rested in the hands of data scientists--until now. Healthcare.ai--open source predictive analytics software--is on a mission to democratize machine learning--to make it accessible to everyone in healthcare (not just data scientists) with the right technical skillset and tools (e.g., BI developers, SQL developers, data architects, and project managers). Machine learning and artificial intelligence (AI) are transforming healthcare. Health systems are increasingly using predictive analytics to better prioritize at-risk patients and optimize care decisions. Healthcare.ai makes it easy to create predictive models on your healthcare data--and is unlike any other machine learning tool in the industry.