There is a constant debate for and against the use of ready-to-use dataset develop high-end artificial intelligence solutions for businesses. But ready-made training datasets can be the ideal solution for organizations that don't have a dedicated in-house team of data scientists, engineers, and annotators.
Even if organizations have teams for large-scale ML deployments, they sometimes struggle to collect the high-quality data required for the model.
Additionally, speed of development and deployment is necessary to gain a competitive advantage in the market, forcing many companies to rely on commercially available data sets. Let's define out-of-the-wayconservation dataand understand their benefits and considerations before deciding to opt for them.
What are commercially available datasets?
A standard training dataset is a viable option for businesses looking to quickly develop and deploy AI solutions when they don't have the time or resources to create custom data.
Ready-made training data, as the name suggests, is a set of data that has already been collected, cleaned, categorized, and ready for use. Even if the value of personalized data cannot be compromised, the next best alternative would be ready-to-use dataset.
Why and when should you consider commercially available datasets?
Let's start by responding to the first part of the statement: 'Why.'
Perhaps the biggest advantage of using a standard training dataset is its speed. As a business, you no longer need to spend a lot of time, money and resources developing custom data from scratch. The initial stages of data collection and verification take up a large portion of project time. The longer you wait to deploy a solution to market, the less likely it is to succeed due to the competitive nature of the business.
Another advantage is the price level— predefined datasets are cost-effective and ready. Think about this for a second: a company creating an AI solution will collect enormous amounts of internal and external data. However, not all data collected is used to develop applications. Additionally, the company will not only pay for the data gathering but also for evaluation, cleaning and retouching. In contrast, with commercially available datasets, you only pay for the data used.
As there are data privacy guidelines, commercially available data is generally a safer and more secure dataset. However, instant data still carries risks, such as less control over the data source and a lack of intellectual property rights over the data.
Now let's move on to the next part of the statement: “When” use a pre-built database?