The machine learning industry is going through massive growth and this is only expected to continue. Machine learning can be used for a variety of things from speech recognition to fraud detection, to predicting various patterns, and dozens of others. It is a subset of AI and will certainly be of major use to businesses in all different industries over the next few years and decades.
However, while the interest and use cases for machine learning are growing, there is also a growing effort when it comes to making machine learning more efficient. A great way to do just that is to take advantage of machine learning quantization.
With that in mind, this guide is going to take a closer look at what machine learning quantization is, and how it is important for AI projects that want to succeed going forward.
What is Machine Learning Quantization?
Quantization in regards to machine learning is all about making the process more efficient by converting the input values used to smaller input values, by using a variety of techniques. So instead of inputting complex values like 1.35, 4.47, or 2.13, you will input their integers of 1, 4, and 2.
Sure, this can cause the accuracy to drop a little, but the drop is generally minuscule and many are willing to sacrifice this tiny bit of accuracy for the much greater efficiency of having to enter far fewer data. Quantization is very applicable in real-world settings and helps ensure machine learning can be as practical as possible.
There are companies like Rinf.tech doing ML quantization and the method is only going to get more popular as the need for efficiency in the space grows.
How is it Important for AI Projects?
The method of quantization is very important for AI projects for a number of reasons. First of all, quantization means that machine learning requires less computing power. It takes much less storage and fewer resources to work with integers, which often means models can be run on hardware with more constraints, such as mobile devices.
In a similar vein, using quantization can lead to faster performance. You are dealing with much less data overall, so the model should be able to progress much quicker than if you are using floating-point numbers. The exact improvement in performance depends on how much you decide to reduce data size vs. the original.
Traditionally, machine learning also consumes a ton of power which can not only be incredibly expensive but also be far from great for the environment. Quantization helps decrease the power required to run models, and even low-powered devices or computers may be able to run these models depending on their exact size.
Using quantization also allows for better security within your model. If an attacker tries to gain access to your data and learn how your model behaves, the more vague and less detailed values being used will hold them back from truly understanding how it works. Reverse engineering can be possible within the area of machine learning, and this process helps protect your model and the data you use.
Many successful AI projects have already used quantization with great results, and there will certainly be more in the future.
In conclusion, we hope this guide has helped you learn more about what machine learning quantization is, and why it is so important for AI projects going forward. As machine learning and AI in general become more popular, managing their efficiency will be something very important for companies to keep in mind.