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Every machine learning (ML) algorithm lies on the foundations of AI training. Whether you deal with a model that predicts prices in the housing market or the banking algorithms that assess the possibility of granting you a new credit, their performance will directly depend on the success of their initial training.
All ML models, including supervised learning and deep learning algorithms, go through a detailed training process. But even before it starts, you need to prepare an accurate dataset that will serve as a basis for the training. Among all stages of raw data processing, data labeling is one of the most critical ones. It directly impacts the model's mastery and makes possible the accomplishment of defined tasks.
A glance at 10 critical steps of data labeling will show you its role and contribution to the success of an ML project.
Step 1. Choosing the Strategy
Your task at hand is your starting point in choosing the right strategy for your project. Another point is your resources and the volume of your data. The most common types of the labeling process include manual labeling, semi-automated and automated labeling, crowdsourcing, and synthetic data labeling.
Manual labeling experiences higher interest among businesses, even though labeling datasets for machine learning projects can take time and extra sources. This approach is considered the most reliable one, ensuring accuracy and high quality.
Step 2. Collecting the Data
At this stage, you collect the needed data from various sources and ensure it corresponds to the set ML tasks. Since data can be in various formats and sizes, it is important to make it uniform and standardized. At this stage, you also consider the ethical side of data and check if the collected data doesn't violate any regulations.
Step 3. Creating Labeling Guidelines
Before starting any annotation process, you should clearly define your guidelines for the tasks the ML model will perform. They should be comprehensive and detailed, accompanying annotators in the process. The guidelines usually include instructions, criteria for making labeling decisions, and examples of the needed annotations.
Step 4. Assigning the Labeling Task
Depending on your project, you will decide whether you go for automated labeling or assign the task to manual annotators. The option for labeling includes using simple or sophisticated tools for automated labeling, cooperating with outsourcing data labeling companies for data annotation, or assigning a task to in-house annotators inside the company.
Step 5. Conducting a Pilot
In cases when you expect annotation from an outsourcing company, you can agree on a pilot variant. There are companies that choose this option at the beginning of every annotation project to assess the quality and agree on expectations. This is a small piece of your dataset annotated in accordance with your guidelines and needs.
Step 6. Starting the Labeling Process
This is the most important part. The whole process of annotation is usually split into different segments, or milestones. It allows accomplishing the labeling step by step and making control assurance after each milestone. It is also critical to define the timeline at this stage. It will serve as a starting point of dividing the process into smaller parts.
Step 7. Finding Agreement between Annotators
The complicated tasks for data annotation may contain voluminous datasets that differ in nature. This is particularly applicable in such industries as law or healthcare. Data from different sources may differ in nature, which requires annotators to have an agreement on labeling such data in the same way. Some widespread agreements among data annotators include consensus algorithms, Cronbach's alpha test, and other inter-annotator agreement metrics.
Step 8. Performing Quality Checks
Quality assurance procedure depends on the complexity of the annotation task. The bigger the task, the more quality checks you will need. Usually, the annotation splits into smaller milestones, after which the quality check takes place. It can include reviewing all milestones as well as choosing a random sample to ensure its quality meets the initial requirements.
Step 9. Refining Annotations
You need to leave some time and place for refinement. If after the quality check, you have identified some gaps or inconsistencies, you devote this time to refinement and polishing the final result. At this stage, you may also accomplish the final quality check of the whole labeling before submitting it for the ML training. This step helps to improve the overall quality of the labeled dataset.
Step 10. Implementing Data for the Training
Once the annotation process is complete, you prepare the dataset for further ML training. You get it ready in the format suitable for the ML model, and divide into numerous stages. The common ones include training, validation, and testing.
Importance of Accurate Data Annotation
The process of data labeling is so meticulous and long because data labeling is one of the main contributors to the accuracy of ML performance. Thanks to data labeling, machine learning algorithms learn the relationship between data and its tasks. Data labeling also allows evaluating and validating the performance of ML algorithms.
With representative labels, the ML algorithm will perform without biases, allowing fair performance across different scenarios. Besides, with labeled datasets, your development process accelerates, streamlining model training and iteration.
Summing Up
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The journey towards mastering machine learning significantly hinges on the effective labeling of data. This is a foundational process that cannot be understated. This 10-step guide helps to navigate the complexities of data labeling. Following these steps, you can prepare your datasets for ML training with accuracy, efficiency, and consistency.
From defining clear project objectives to selecting the right tools and ensuring rigorous quality control, each step is pivotal in shaping the quality of the resulting ML models. Embracing these guidelines not only streamlines the data preparation workflow but also enhances model performance. With data annotation, you underscore the importance of ethical considerations, and ultimately contribute to the success of your machine learning project.
Understanding and implementing these data labeling steps is crucial in mastering machine learning. This simple approach will guide you towards success and innovation, unlocking new possibilities across various domains.
