Scaling Your AI Software Up Or Down
AI training data consists of both test cases and data collected during the training. AI and ML training data is utilized to train an artificial intelligent machine or software system. It is also used to create your AI technologies smarter, stronger and more efficient than you can possibly achieve by yourself.
The training data contains the test cases and the solutions to the problems that were posed to the software. In essence it is a database of how things are done. The data enables you to do further tests to validate that all your AI and ML programs are doing as intelligently as a real intelligent person would, in regards to how they simulate human learning, reasoning and even self-correction. Once you have tested your AI and ML algorithms on the training data, it is time to bring them into the real world and start applying them to real business scenarios. You will find that once you have applied the training data and discovered its accuracy, you will not have to tweak your AI systems anymore, it s already set and ready to use.
When it comes to using raw data from your AI training data sets, the thing that you must be cautious of is that it might contain duplicate data that could affect the final accuracy of your AI systems. This is a very big concern for many of the people who work on AI and ML projects because they fear that the majority if not all of their work might be incorrect. Some of the companies that have used raw data and had to go back and correct their AI systems have been hit hard by this problem. Even the big corporations who use this method of training their AI systems, sometimes find themselves having to turn back to the traditional classroom methods to teach their new hires how to operate the new sophisticated programs.
The way that you are going to avoid this problem is to get your data only from reputable sources. If you are working with a large computer vision company then they should be able to give you all of the information that you need to have in order to make sound decisions and minimize the risk of having an inaccurate AI algorithm. If you are a small startup company then you might be able to acquire your ai training data from smaller more private sources. Keep in mind that the more sources that you have the more sources you will have to check, therefore the more changes that you will need to make to your algorithm.
Another thing that you have to consider is whether or not you want to scale the software up or scale down. Currently ai training data consists of large image files like JPEG’s and PNG’s. These files are too large for web browsers to read and process. In addition to using up too much memory on your machine storing these files would also be a waste of time. Therefore if you plan on using a self-driving car program you might want to scale down the size of your image library. If you are planning on implementing machine learning features it makes a lot of sense to start off small and work your way up.
It is also important to consider how easy it is to change the training data once it has been pre-trained. We all know what problems can arise once an algorithm starts to learn. Things like camera placement might change over time, or the number of cameras might need to be increased. Also things like road and traffic conditions could change over time, which would throw off the accuracy of the algorithms. If you plan on making any changes to your AI neural network, you should make sure to create test versions that can be tried out on a demo platform.
