How do databases support AI algorithms? Posted on : Sep 24 - 2021

Databases have always been able to do simple, clerical work like finding particular records that match some given criteria — say, all users who are between 20 and 30 years old. Lately database companies have been adding artificial intelligence routines into databases so the users can explore the power of these smarter, more sophisticated algorithms on their own data stored in the database.

The AI algorithms are also finding a home below the surface, where the AI routines help optimize internal tasks like re-indexing or query planning. These new features are often billed as adding automation because they relieve the user of housekeeping work. Developers are encouraged to let them do their work and forget about them.

There’s much more interest, though, in AI routines that are open to users. These machine learning algorithms can classify data and make smarter decisions that evolve and adapt over time. They can unlock new use cases and enhance the flexibility of existing algorithms.

In many cases, the integration is largely pragmatic and essentially cosmetic. The calculations are no different from those that would occur if the data was exported and shipped to a separate AI program. Inside the database, the AI routines are separate and just take advantage of any internal access to the data. Sometimes this faster access can speed up the process dramatically. When the data is substantial, sometimes merely moving it can take up a large portion of the time.

The integration can also limit analysis to the algorithms that are officially part of the database. If the users want to deploy a different algorithm, they must return to the old process of exporting the data in the right format and importing it into the AI routine.

The integration can take advantage of some of the newer distributed, in-memory databases that easily spread out the load and the data storage over multiple machines. These can easily handle a large amount of data. If complex analysis is necessary, it may not be hard to increase the CPU capacity and RAM allocated to each machine.

Some AI-powered databases are also able to leverage GPU chips. Some AI algorithms use the highly parallel architecture of the GPUs to train machine learning models and run other algorithms. There are also some custom chips specially designed for AI that can dramatically accelerate the analysis.

One of the biggest advantages, though, may be the standard interface, which is often SQL, a language that’s already familiar to many programmers. Many software packages already interact easily with SQL databases. If someone wants more AI analysis, it’s no more complex than learning the new SQL instructions. View More