You are in a good spot if you can get your hands on hundreds or even thousands of data to use. We all know that the model’s success depends on the data quality first.
At any start of an AI-driven product business, it should be viewed as a data company. A company that leverages data from either an existing product or service, a partnership where others have access to data, or those that you hand pick and collect yourself to build your product.
The process of preparing your data is called cleaning your data. A cleaned data is one that is accurately labeled and has information that can help the algorithm learn to help build a model.
Identify what format of data you’re using
Every training process to build a model takes on a different data format. Understanding the type of data you have collected and how it will be used.
Some data can come in many formats:
Data that comes from a spreadsheet, either one or multiple spreadsheets with a row or column, are examples of tabular data. Each cell provides a reference that the model or algorithm will train to learn patterns or commonalities that can provide insights into future occurrences.
Sound recordings over a wide range of options. It could be environmental sounds that are man-made or from the natural environment. It can include speech or cries from animals or humans. These sound data are often recorded using a microphone. The quality of the data is best by having recordings from similar platforms. Microphone recording must be clear and unaltered to be 100% raw. The dataset will need recordings that reflect the real world as much as possible. That includes sound and speech with background noises, especially if the AI solution is to be used in the real world.
Images are usually digital representations of objects, things, or people. They could be xrays or digital imaging files from electronic devices other than cameras.
Images, again, need to be unaltered and easily understood by the human eye. The more pictures that look could be mistaken for something else, the better the quality of your model. Objects with different background images would improve the quality of your data. You want to train the model to determine the difference between what is and isn’t, just like with the audio database.
Text and Document
Text that includes keywords or sentences in the form of chats, documents, or emails. The quality of your text document will be determined by the problem that you’re solving. However, you want to get as much text as is reflected in how people write.
If you are using a video recording to help identify objects. The quality of your recording and the variation of images and audio will be helpful in building a model that works.
Check that you have consented to use
You will need to ensure that you are following data privacy laws. Most data may have consented to use for a specific purpose that will not allow you to reuse the data for other purposes. You want to be sure that all the data can be tracked to where the source was and where the consent was made.
You should publicly update any document highlighting how you use the data if you haven’t updated so. Transparency at the start will go a long way to making your project successful.
Decide if you’re labeling
Some algorithms work well when you have your data labeled. The labeling allows you to give more clarity and instruction to the algorithm. For those of us who have hundreds or thousands of datasets, labeling is important to use against a pre-built model as our base model to improving or fine-tuning.
If you decide not to label, you will need to increase the quantity of your dataset to allow the algorithm to see a million different things and decide what patterns and label it can create from the dataset.
Choose your data labeling platform
When working with folders of files, you may want to keep your data management process in check. Working with a structured process can help you avoid making mistakes when you have so many files to record.
Some platforms can help you with the data labeling process. Either for you to do it yourself or hire someone. Either way, you can provide rich insights into your data to help prep your data for training.
Label your data accurately
Labeling is different for each data. The label can be a single item or multiple items in each file. Regarding tabular data, the rows and columns mean the labeling is already done for us. However, for the other data file types, labeling will be associated with a file with a spreadsheet that indicates what’s inside the file. Sometimes you need to get granular details. For example, in the video or audio file, you will need to indicate at what timestamp should one be able to hear or see specific objects or things.
For documents or textbases, they are not used directly for training. They are converted into other formats. For training, you may need to break down a text into keywords or sentences and associate attributes to certain words or sentences.
Often is a good idea to hire multiple people to label your data. It will help you catch errors a lot easier. Sometimes we can make a mistake with our labeling, but having a number of people working on labeling, you can catch the ones that are not matching to review those again for correction.
While labeling, you want to ensure consistency with your data. That may mean deleting any white space or punctuation or fixing any inconsistent labeling format on the column or row of a spreadsheet.
Needing more data
There are some ways to expand your dataset once you have a certain quality of data. There are some AI techniques that allow you to create new artificial data that looks like and sounds like an original recording, video, or image. The key here is that it must be realistic and something we can see in the real world.
Your original data will help with creating the fake data. There will be a number of variables usually set by the engineer on what needs to change to make to create multiple versions.
Check for bias
If you are collecting data that reflect the human experience, you must ensure that you have not omitted any data. It’s hard to know what you don’t know. That’s why it is important to create an advisory group, either formally or informally, of people that you trust who can help you fill in what you might have forgotten, not aware of, or what’s missing from your dataset.
Prepare to collect more data
I mentioned that data collection is not a one-and-done. Your business is a data company should it be building Ai-driven products. It is important if you run models that are time-sensitive or based on trends and information changes by the hour. You will need a system for continuously collecting new data to train and update your model effectively.