Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it for outliers and anomalies, assemble confidence ranges of the predictions and feature importance, and eventually deploy it to make predictions. An emerging field in AI, called Automated Machine Learning (AutoML), lowers these barriers to entry by using AI to automate much of this process.
One of the market leaders in AutoML is MindsDB. Their service lets business users and developers make predictions on top of data at its source. Rather than make expensive copies of databases, a process that creates complex infrastructures, MindsDB trains and deploys models right inside the database. The results of their ML models can be queried with standard SQL statements and integrated into other applications as easily as querying any other database.
In this episode we learn more about the progress that has been made in AutoML to simplify incorporating machine learning throughout organizations. We discuss the current features available from MindsDB, the difference their product has made for companies trying to leverage AI, and the future of AutoML and artificial intelligence generally.
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