Data scientists have had to put up with sluggish machine learning and challenges in providing truly predictive analytics. But with no other options, moving data from a database to the machine learning software and then back to the database has been the only option these data scientists have had until recently. In-database machine learning is where data analytics is headed and it's making a huge difference in our ability to provide truly predictive analytics and make data actionable at the time we receive it. Let's look at some ways that various industries are applying in-database machine learning and the impact it is having. In-database machine learning is ideal for a variety of industries and it's the future of data analytics.

In my August 2020 article, "How to choose a cloud Machine Learning platform," my first guideline for choosing a platform was, "Be close to your data." Keeping the code near the data is necessary to keep the latency low, since the speed of light limits transmission speeds. After all, machine learning -- especially deep learning -- tends to go through all your data multiple times (each time through is called an epoch). I said at the time that the ideal case for very large data sets is to build the model where the data already resides, so that no mass data transmission is needed. Several databases support that to a limited extent.