Not all data labeling is created equal. Although data labeling is considered an uninteresting aspect of AI development, it is a crucial part of the process for developing AI. AI development can be divided into 4 key stages. There is the design phase, the data collection phase, the development phase, and the deployment phase.
In the first phase you target a problem and design a solution for it. The second phase is where you extract all the necessary information for the algorithm you want to develop. In the development phase the data is polished and labeled and the algorithm is trained. Finally, in the deployment phase, the solution is set out to perform in a real world scenario and continues to be evaluated for any improvements.
Recently, we wrote an article for Inside Big Data where we addressed data labeling and outlined the different strategies to implement in your process to help you develop successful solutions for your algorithms.
AI development has created a market subsector that targets investors interested in data labeling. Data labeling, considered the third mile in AI development, is the most important part for successful AI solutions.
To train and polish an algorithm, huge amounts of data is needed. 10,000 labeled data points are the minimum requirement to start the training process. Additionally, it must be collected in a structured manner to be able to test them out and train the model to identify and understand recurring patterns. The labels can be in the form of boxes around objects, tagging items visually or with text labels in images or in a text-based database that accompanies the original data.
The annotated data is important because, once the algorithm is able to identify patterns in structured data, it can begin to recognize patterns in new unstructured data. Any raw data must be accommodated into the required shape, polished, and labeled with a proper identification.
Data labeling is oftentimes a tedious and arduous task. A group of people are placed in charge of labeling images based on the specifics of the project, like: identifying a “car” or a “white car”. Since this is a process that takes time, data firms are trying to find a way around it. They are searching for automated systems that do the tag and identify data-sets. Automation speeds up the process, but it can still be faulty without ensuring that the AI solutions are making the right decisions. For example: an algorithm trained to identify children at a crosswalk of a busy intersection, but not recognizing some children because their height wasn’t considered when training the algorithm.
It is no surprise that investors are noticing the growth opportunities in the market because data labeling is the force behind successful AI. Companies everywhere are turning to data labeling firms to find faster and effective ways to AI transformation. Effective algorithms are a constant process that takes time. When selecting a data labeling firm, buyers must be aware of the following to help them consider how to best approach their data labeling process:
- Use custom-data. Owning your own quality private data-sets is a competitive advantage. If you are searching for a partner, you need to make sure the data they use is quality controlled. Additionally, know where your data comes from and if synthetic data was used to enrich the data-set.
- Effective data labeling requires expertise. Great data labeling requires good eyes and skill. Data labeleres get better and faster over time. They can determine and avoid false positives because of bad data.
- Data privacy should remain paramount. The team of data labelers need access to a lot of company information most of the time for effective data training. Have them under NDA with your firm or service provider.
- Data labelers and data scientists should be part of a single team. Data scientists will guarantee quality assurance and control of data labeling to provide the best data-sets . They will align the process with the specific needs of the AI project.
- Find a long-term partner, not a data labeling factory. AI is a continuous process of improvement, never a limited endeavor. You need to constantly train your algorithm to improve, which means that it is best to stick with a partner that develops that initial solution, for they will understand the algorithm best and how to best refine the inner workings of it.
- Partially automate when needed. Partial automation can guide data labelers to where the objects are, but it isn’t as effective and precise as human-led work. Automation is always best when paired with human intelligence.
Data labeling should not be treated as a commodity, it is an essential part of effective AI that deserves attention. While data labeling will never be a one-size-fits all, it does have a need for a level of expertise, customization, collaboration, and a strategic approach that will lead to smarter AI solutions.