What is off-the-shelf datasets?

There has been a debate about whether to use off-the-shelf datasets to develop high-end AI solutions for enterprises. Especially for organizations lacking reliable data scientists, engineers and annotation teams, off-the-shelf datasets are the perfect solution.

Even though some companies own professional teams, they must solve many data quality issues. In addition, speed of development and deployment is necessary to gain a competitive advantage in the marketplace. Many companies increasingly rely on existing datasets to improve efficiency and quickly capture the market. The benefits and considerations of existing datasets are expanded on below.

Speed is the most significant advantage of off-the-shelf datasets. Companies no longer need to spend much time, money, and resources to collect and develop custom data from scratch. This will directly save much of the upfront time of the initial project. Much of the market's competitiveness depends on the time cycle of solution deployment; the longer it takes, the less chance of winning.

Another advantage is price. Companies that choose to customize their data, this means they pay for steps such as data cleanup, evaluation, and rework. Secondly, the deployment of an AI solution requires the collection of a large amount of data. However, companies will only use some of the data collected to develop applications. If you go with an existing data machine, you will only pay for the portion of the data used.

There is also the advantage of compliance. Existing datasets are relatively safer and more reliable datasets. For those instant data, there are significant risks, such as less control over the data source or lack of intellectual property rights.

Attention for using off-the-shelf AI training datasets for your ML projects

By using off-the-shelf datasets, you will likely have less control over individual procedures such as acquisition and annotation. Since these off-the-shelf datasets are generic, there is a high possibility of some data bias when solving some cases. Companies must supplement the existing information with ready-to-go datasets to ensure the data meets your business needs.

So to get the best out of data training and circumvent these drawbacks, finding a data partner with more experience is essential. Through their project experience with existing markets and related models, they can help you provide the data you need well, help companies avoid bias to the maximum extent, and help them seize market opportunities more efficiently.