The performance of an AI system undeniably depends on the training data rather than the programming. Data is necessary for machine learning models to work. Even the most performant algorithms can be rendered worthless without a foundation of high-quality training data. Indeed, when substandard or irrelevant data is provided to machine learning models in the early stages, then it will hamper the overall result and the whole thing might also cripple. Therefore, quality data is necessary for successful machine learning projects.
According to research firm Cognilytica, around 80% of AI project effort is spent in acquiring, organizing, and categorizing the data.
Training Data and Data Labeling
The initial data used to build a machine learning model, from which the model generates and refines its rules, is referred to as training data. The quality of this data delivers a big impact on the model’s overall development, and it sets a top bar for all future applications or models that use the same training data.
Now the question arises that how will you identify the quality of training data?
You must ensure that your data labeling is accurate in order to have good quality training. In machine learning and training data context, data labeling refers to the preparation and processing of unlabeled and undiscovered datasets in order to make them relevant to an AI model.
In simple words, data that has been tagged in order to prepare it for a machine learning model is referred to as labeled data. That typically means that you’re adding features to the data that make it more valuable for machine learning and predictive modeling.
Data labeling processes are increasingly being outsourced from data labeling companies nowadays. Choosing a data labeling partner remains a crucial choice that can affect the performance of your model and the time to market. It might be challenging to discern the difference between your selections unless you recognize what to look for. It’s essential to make an informed selection based on your use case, data characteristics, and data security needs.
You’ll be investing time and resources in anyone you choose, as well as committing your most sensitive information to them. To ensure successful collaboration, there are a few things or questions you should ask before committing to a with the data annotation companies.
Questions to Ask:
1. What kind of labeling tools, annotation techniques, and data features has your team used before?
2. Describe how your recommended tooling solution handles quality assurance. We have to set up and run a separate QA workflow outside of the tool?
3. Is your data labeling system or workforce platform agnostic? How will you deal with our team’s modifications or iterations that alter data characteristics for labeling?
4. Do you have a dedicated manager or specialised project manager available? What channels of communication will our team have with your data labeling team? And are they available or operate 24*7 or round the clock?
5. Describe how scalable your staff is. What is the maximum number of workers we can have? Is it possible to adjust the volume of data labeling based on our requirements? How frequently are we going to do that? Are there any cost advantages to scalability?
6. How can you figure out how productive your employees are? When a team of your data labelers reaches full throughput, how long does it take them?
7. Tell us about the level of customer service we may expect after we work with your team. How often are we going to meet? How much time should my team budget for project management?
8. What would the cost of your solution be if we did the work ourselves?
9. Do you have secure facilities that meet our security requirements? How do you choose and approve employees for those positions? What type of data security training do you offer your employees? What happens when new members of the team are hired?
10. How do you keep data safe when it’s subject to regulations like HIPAA or GDPR?
11. Do you have industry professionals or specialised experts or proficiencies for different projects?
Above are the top-11 questions you must say when choosing a data labeling partner for your company because top labeling companies like Cogito can provide you useful information about data characteristics that may help you enhance the productivity and quality of your model.