Optimizing the Storing and Processing of Data for Machine Learning Problems
All of the preceding uses for artificial intelligence rely heavily on optimized data storage and processing. Optimization is necessary for machine learning because the data size can be huge, as seen in the following examples:
- A single X-ray file can be many gigabytes in size.
- Translation corpora (large collections of texts) can reach billions of sentences.
- YouTube's stored data is measured in exabytes.
- Financial data might seem like just a few numbers; these are generated in such large quantities per second that the New York Stock Exchange generates 1 TB of data daily.
While every machine learning system is unique, in many systems, data touches the same components. In a hypothetical machine learning system, data might be dealt with as follows:
Each of these is a highly specialized piece of hardware, and although not all of them store data for long periods in the way traditional hard disks or tape backups do, it is important to know how data storage can be optimized at each stage. Let's dive into a text classification AI project to see how optimizations can be applied at some stages.