MindsDB automates and integrates top machine learning frameworks (including GPT-4) into the data stack as "AI Tables" to streamline the integration of AI into applications, making it accessible to developers of all skill levels. "AI tables" allow you to get predictions via SQL queries and continuously learn from your data. You can try MindsDB using our demo environment with sample data for most popular use cases. The prefered way is to use MindsDB Cloud free demo instance or use a dedicated instance. Follow the quickstart guide with sample data to get on-boarded as fast as possible.

Let MindsDB connect to your database. Train a Predictor using a single SQL statement (make MindsDB learn from historical data automatically) or import your ML model to a Predictor via JSON-AI. Make predictions with SQL statements (Predictor is exposed as virtual AI Tables). There's no need to deploy models since they are already part of the data layer. Check our docs and blog for tutorials and use case examples. MindsDB works with most of the SQL and NoSQL databases and data Streams for real-time ML.

Thank you so much for joining us in this interview series! Can you share with us the'backstory" of how you decided to pursue this career path in AI? I believe that there is enormous power in data. The more a company has, the more they're able to propel their businesses forward. But only if they're able to get meaningful insights from it.

Use the open-source integrated machine learning in MindsDB and the open-source data integration platform Airbyte to forecast Shopify store metrics. With the volume of data increasing exponentially, it's critical for businesses focused on e-commerce to leverage that data as quickly and efficiently as possible. Machine learning represents a disruption to increase predictive capabilities and augment human decision making for use cases like price, assortment and supply chain optimization, inventory management, delivery management, and customer support. In this'how-to' guide, we'll provide step-by-step instructions showing you how to simply and inexpensively integrate machine learning into an existing Shopify account using Airbyte, an open-source data integration platform, and MindsDB, an open-source AutoML framework that runs on top of any database. We will assume you already have Airbyte set up via Docker.

As enterprises increasingly try to put their data to work using artificial intelligence and machine learning, the landscape of vendors and open source projects can be daunting. As FirstMark partner Matt Turck has written, in 2021 the industry saw a "rapid emergence of a whole new generation of data and ML startups," and in 2022, this trend looks set to continue. AI/ML is so hot, in fact, that even with a recession looming CIOs remain loath to cut spending on AI/ML projects. So where will enterprises spend that money? To help you navigate the sometimes bewildering array of AI/ML options out there, I talked with data science professionals to get their picks on the most innovative companies in AI/ML.

A loan is money borrowed to someone (the debtor) with the intent to pay back at an agreed date. Ideally, things should go as planned but when the debtor fails to pay the person they borrowed the loan from (the creditor), the debtor is said to have defaulted on the loan. It is then important for creditors/loan companies to know/predict if a certain debtor will default or not. This is a problem that machine learning solves, this is a classification machine learning problem. As with every machine learning problem, data is the major ingredient in solving it.

This article will show how to use SingleStoreDB with MindsDB using Deepnote. We'll create integrations within Deepnote, load the Iris flower data set into SingleStoreDB, and then use MindsDB to create a Machine Learning (ML) model from the Iris data stored in SingleStoreDB. We'll also make some example predictions using the ML model. Most of the code will be in SQL, enabling developers with solid SQL skills to hit the ground running and start working with ML immediately. The notebook file used in this article is available on GitHub.

MariaDB Corporation and MindsDB, the leader in in-database machine learning, today together announced a technology collaboration that makes machine learning predictions easy and accessible to cloud database users. By using MindsDB in SkySQL, MariaDB's fully managed cloud database service, data science and data engineering teams can increase their organization's predictive capabilities to plan for and address real-world business issues. MariaDB has been downloaded over one billion times and is used by 75% of the Fortune 500, touching the lives of more than a billion people every day. MariaDB database users will now be able to add machine learning based predictions directly into their datasets stored in SkySQL. "We are excited to work with MindsDB to unlock the genius in the cloud by giving customers seamless and easy-to-use machine learning capabilities available through MariaDB SkySQL," said Jags Ramnarayan, Vice President and General Manager of SkySQL, MariaDB Corporation.

The coalescence of machine learning tools into the Python ecosystem makes sense when you consider all steps that are required to train and test models: cleaning, transformation, visualization, and so on. There's so much iteration involved in machine learning that using a data science programming language seems necessary. However, the marriage of Python and machine learning, while sensible, does have a trade off: Database professionals are more likely to speak SQL than Python. The 2020 Stack Overflow survey bears this out nicely, showing an ML cluster centered around Python, and a separate cluster linking SQL with database technologies. Based on this, if we assume that data/analytics engineers are "closest" to their company's data, then why not put tools in their hands that unlock the full potential of their domain expertise? This is where MindsDB comes in.

Let the OSS Enterprise newsletter guide your open source journey! Databases are the cornerstone of most modern business applications, be it for managing payroll, tracking customer orders, or storing and retrieving just about any piece of business-critical information. With the right supplementary business intelligence (BI) tools, companies can derive all manner of insights from their vast swathes of data, such as establishing sales trends to inform future decisions. But when it comes to making accurate forecasts from historical data, that's a whole new ball game, requiring different skillsets and technologies. This is something that MindsDB is setting out to solve, with a platform that helps anyone leverage machine learning (ML) to future-gaze with big data insights.