An Introduction to Data Labeling
Imagine you want to start an agribusiness and your goal is to maximize profits by growing abundant, good-quality crops. However, growing large amounts of crops is limited by the number of resources you have, such as labor and land. And the quality of crops depends on the quality of the inputs that nurture the plants, such as the type of seed, environment, and so forth.
This scenario is analogous to the problems faced with building good machine learning models. You can’t expect profits from a business if you don’t provide the right input or are not able to produce the expected quantity. In the same way, you can’t expect good machine learning models with low-quality or small amounts of training data.
Getting abundant, high-quality datasets is more difficult than it seems. Popular machine learning classification techniques such as supervised and deep learning require massive amounts of high-quality labeled data. Annotating data at this scale is expensive, time-consuming, and extremely boring.
Secondly, just providing models with a lot of data is not enough. Most models need accurately labeled datasets. They follow this simple GIGO protocol – Garbage in Garbage Out. In a “Survey on Data Collection for Machine Learning,” authors Yuji Roh, Geon Heo, and Steven Euijong Whang explain that “trained models are only as good as their training data, and it is important to obtain high-quality data labels. Simply labeling more data may not improve the model accuracy.”
Data labeling for machine learning is the tagging or annotation of data with representative labels. It is the hardest part of building a stable, robust machine learning pipeline. A small case of wrongly labeled data can tumble a whole company down.
In pharmaceutical companies, for example, if patient data is incorrectly labeled and used for developing a new treatment, it may lead to a product recall, government fines, and irrevocable reputational damage. But when machine learning data labeling is tackled correctly, it can not only avert such scenarios but also boost the development of data science and analytical projects that can deliver market insights, drive sales, and save company costs.
In this blog, we will introduce a few common techniques to address the following questions:
- How can you decrease the time and cost of labeling massive amounts of data?
- How can you create a high-quality labeled dataset?
We will also consider the pros and cons of these methods and explore alternatives to help lubricate the machine learning data labeling process.
Semi-Supervised Learning (SSL)
Semi-supervised learning is a class of machine learning that incorporates supervised and unsupervised learning to label large amounts of data with only a small labeled dataset. It uses supervised learning models trained on the small labeled dataset to predict labels for unlabeled data or assign them with what are called proxy labels.
If these proxy labels satisfy a criteria set by the model maker, they are added to the training dataset and the model is re-trained with this updated data. This process continues until no more data satisfies the criteria or the required model accuracy is achieved. Some of the most effective SSL techniques are Tri-Training and Active Learning.
- Time and cost efficiency: Because a smaller amount of manually labeled training data is needed, this approach saves time and cost.
- Better accuracy: Some of the techniques such as active learning may achieve better accuracy over time as it involves human feedback to improve labeling.
- Requires a bootstrap labeled set, which must be derived using a different labeling method.
- Model performance strictly depends on the initial training dataset. There is no guarantee that it will accurately label unseen data. The initial training dataset is only a small sample of the entire dataset and it may miss labels that represent data outside the selected sample but within the dataset.
- If any data is wrongly predicted with high confidence, it will be added to the training dataset of the model. This will inject future errors.
In this technique, a pre-trained machine learning model is used to label the data. The idea is to use a model that has been trained on a dataset similar to the one you want to label and fine-tune it to achieve the required accuracy. Let’s say you want your model to annotate electrical appliances in an image. For this, you may use a model that has been trained on a dataset of annotated objects.
- Time and cost-efficient:Very little human intervention is needed, thereby saving a ton of time and cost.
- Saves computational time: As the model is already trained, fewer computational resources are needed to build the final model.
- The model built may perform worse than the initial model. Sometimes the model maker might think that the data to be labeled is similar to the data on which the model was trained, but this might not be true for the model.
Manual Data Labeling for Machine Learning
This is when experts within the company label the data. It is also known as in-house labeling.
- High accuracy: Labelers are usually people within the team that know what is needed for their model. The labeling is of high quality because the company manages the resources directly and puts the required tests and management in place for governance and quality control. For example, most good labeling companies have sophisticated systems that check the quality of the labels. These systems reward the best labelers and penalize those with lower quality. As a result, quality labeling is reinforced.
- Data security: Data does not leave the database systems managed directly by the company. The security measures on these systems are enforced by the data owners within the company, thereby significantly lowering the risk of any data leakage
- Expensive and time consuming: Most of the time, model experts label their own data. They are highly paid resources who spend an incredible amount of time on easy annotation tasks, leading to extended project deadlines and costs. The other option is to hire new cheap labelers, but training and managing them may also add further costs to the project budget.
- Lacks flexibility in scaling resources: Although scaling the labeling workforce seems like a viable option, it is difficult to keep up with it when labeling requirements change frequently.
In this method, also known as Outsourced/Crowd-based labeling, labeling tasks are given to dedicated vendors or workforce outside the company. The difference between crowd-based and outsourced labeling is that crowd-based labeling assigns labeling tasks to a bunch of unorganized workers, whereas outsourcing involves an organized workforce.
- Flexibility scaling resources: Labeling tasks are scaled according to project requirements, and therefore the process is a lot more flexible.
- Cheaper labor and requires less time: With experienced, reputable vendors, high-quality data labeling for machine learning is a lot cheaper and saves time. Little to no management is needed from the client’s end to train and manage the labeling workforce.
- Outside workers may lack oversight: There is the risk of incorrectly labeled data. Outside workers lack oversight, and data might not be labeled as experts need.
- Potential data security risk: External labeling may pose a risk of exposing your organization’s sensitive data. Some vendors who leverage automated learning may use your data to build common models that could be used to label other clients’ data. In this case, clients might have to request that the vendor maintains data privacy.
- Difficulty finding experienced vendors: Data labeling efficiency depends largely on the vendor’s experience and its own infrastructure of labeling using manual and technological resources. Finding vendors with good experience and a record for meeting client requirements can be tough.
Conclusion: Use a Blended Approach
After examining multiple ways to label data for machine learning, we recommend a blended approach: using both automated and external data labeling. There may be some data security risks with external labeling, but in most cases, the data to be labeled is not sensitive. In such scenarios, external data labeling along with some kind of automated data labeling is the best option to achieve high-quality labeled data cheaply and quickly.
Luckily for us, some companies such as Amazon, Scale AI, and Labelbox have identified gaps in labeling and offered a plethora of combinations within their services that can help you achieve your desired labeled dataset and within your Service Level Agreement.
These service offerings have created a lubricated process that incorporates crowd-based data labeling with automated machine learning so that you can have a smooth pipeline-building experience. To make sure that labeling tasks are accurate and comply with the standards of the client, their strategy is to work with the client experts in a timely manner for quality check, thus gaining confidence in the data that has been labeled to compensate for lack of oversight.
Is your data causing you headaches? Whether the data is labeled, unlabeled, structured, or unstructured, phData’s Machine Learning practice is here to help.