MindsDB wants to give corporate databases a brain

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Databases are the lifeblood of most modern business applications, whether it’s managing payroll, tracking customer orders, or storing and retrieving just about any business critical information. With the right additional business intelligence (BI) tools, companies can pull all kinds of information from their vast expanses of data, such as establishing sales trends to inform future decisions. But when it comes to making accurate predictions from historical data, it’s a whole new ball game, requiring different skills and technologies.

This is something MindsDB is working to solve, with a platform that helps anyone leverage machine learning (ML) to envision the future with big data insights. In the company’s own words, it wants to “democratize machine learning by giving corporate databases a brain.”

Founded in 2017, Berkeley, Calif., Based MindsDB enables businesses to make predictions directly from their database using standard SQL commands and view them in the app or platform. analysis of their choice.

To further develop and market its product, MindsDB announced this week that it has raised $ 3.75 million, bringing its total funding to $ 7.6 million. The company also unveiled partnerships with some of the most recognized database brands, including Snowflake, SingleStore, and DataStax, which will bring MindsDB’s ML platform directly to these data stores.

Using the past to predict the future

There are a myriad of use cases for MindsDB, such as predicting customer behavior, reducing churn rate, improving employee retention, detecting anomalies in industry processes, scoring customer credit risk and inventory demand forecasting – this is about using existing data to understand what that data might look like at a later date.

An analyst at a large retail chain, for example, may want to know how much inventory he will need to meet demand in the future based on a number of variables. By connecting their database (for example, MySQL, MariaDB, Snowflake, or PostgreSQL) to MindsDB, then connecting MindsDB to the BI tool of their choice (for example, Tableau or Looker), they can ask questions and view what awaits them.

“Your database can give you a good picture of your inventory history, because databases are designed for it,” MindsDB CEO Jorge Torres told VentureBeat. “Using machine learning, MindsDB enables your database to become smarter to also give you predictions of what that data will look like in the future. With MindsDB, you can solve your inventory forecasting problems with a few standard SQL commands.

Above: visualization of predictions generated by the MindsDB platform

Torres said that MindsDB allows so called In-Database ML (I-DBML) to create, train and use ML models in SQL, as if they were tables in a database. of data.

“We believe that I-DBML is the best way to apply ML, and we believe all databases should have this capability, which is why we have partnered with the best database manufacturers in the world.” Torres explained. “It brings ML as close to data as possible, integrates ML models as virtual database tables, and can be queried with simple SQL statements. “

MindsDB comes in three major variations: a free, open source incarnation that can be deployed anywhere; an enterprise version that includes additional support and services; and a recently launched cloud hosted product in beta, billed per use.

The open source community has been a major focus for MindsDB so far, claiming tens of thousands of installs from developers around the world, including developers working at companies such as PayPal, Verizon, Samsung, and American Express. While this organic approach continues to be a large part of MindsDB’s growth strategy, Torres said his company is in the early stages of commercializing the product with companies across many industries, although he has not. had the freedom to reveal names.

“We are in the validation phase with several Fortune 100 clients, including financial services, retail, manufacturing and gaming companies who have highly sensitive business-critical data – and [this] prevents disclosure, ”Torres said.

The problem MindsDB seeks to solve is one that affects just about every vertical business, spanning businesses of all sizes – even the largest companies won’t want to reinvent the wheel by expanding all facets of their AI arsenal to start from nothing.

“If you have a robust and functional enterprise database, you already have everything you need to apply machine learning from MindsDB,” Torres explained. “Businesses have invested vast resources in their databases, and some of them have even spent decades of refining their data stores. Then, over the last few years, as ML capabilities began to emerge, companies naturally wanted to harness them for better forecasting and decision making.

While companies could want to To make better predictions from their data, the challenges inherent in extracting, transforming, and loading (ETL) all of that data into other systems are complex and don’t always produce great results. With MindsDB, data is left where it is in the original database.

“That way, you drastically reduce the project schedule from years or months down to hours, and in the same way, you drastically reduce points of failure and costs,” Torres said.

The Switzerland of machine learning

The competitive landscape is quite large, depending on how you view the scale of the problem. Several big players have emerged to arm developers and analysts with AI tools, such as DataRobot and H2O heavily backed by VC, but Torres sees these types of companies as potential partners rather than direct competitors. “We think we’ve found the best way to bring information directly to the database, and it’s potentially something they could tap into,” Torres said.

And then there are the cloud platform providers themselves such as Amazon, Google, and Microsoft that offer their customers machine learning as add-ons. In these cases, however, these services are really just a means to further sell their core product – compute and storage. – Torres also sees potential for partnering with these cloud giants in the future. “We are a neutral player – we are the Switzerland of machine learning,” Torres added.

MindDB’s seed funding includes investments from a number of notable funders, including OpenOcean, which claims MariaDB co-founder Patrik Backman as a partner, YCombinator (MindsDB won the Winter 2020 YC lot), Walden Catalyst Ventures, SpeedInvest, and Berkeley’s SkyDeck fund.


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