Data, data everywhere!

We are dwelling into an ocean of diverse data at all times. And this data is useful for many use cases that have made our day-to-day life way easier than what it used to be. But all this comes at a price, as the amount of effort necessary to make this data useful is very high and often goes to waste if the data is not handled properly.

Data annotation is a key practice in DataPrepOps that aims at tagging data such as images, text, videos to make sure that the data you’re feeding to your ML model is properly read and interpreted by the model. Though considered a boring and time-consuming process, data annotation is not easy. The bad handling and poor management of data annotation projects has led to losses of millions of dollars that ended up resulting in a full stop on many promising AI projects, including in the world’s most prestigious and data-mature organizations.

But worry not! It is definitely possible to build a great dataset to feed your ML model with. You just have to be fully aware of the correct steps to prepare your data and give data annotation the attention and care it deserves. Following proper annotation practices while managing your data annotation projects can not only reduce the time and money you will spend on the project, but can also help produce way better results.

Here are 6 best practices that data teams can follow to obtain great annotations for their projects:

Define how you need your annotations to be generated

Defining the desired data annotations involves specifying the necessary information to be included in the annotations, the format for the annotations, and the level of detail needed. It also requires outlining the guidelines for the annotators, including any specific terminology or domain-specific knowledge that should be taken into account. Additionally, obtaining the desired annotations means setting criteria for quality control and determining the methods for verifying the accuracy of the annotations. All of these factors help ensure that the generated annotations meet the project’s objectives and provide meaningful information to the model.

Strive to diversify the background of your annotators

Deciding who will be responsible for annotating your data is crucial for maximizing the performance of your machine learning model. Neglecting this aspect can result in inefficiencies and underutilization of resources. To ensure the success of your ML application, it’s important to have a comprehensive understanding of the demographic, geographic and usage patterns of its users, so that the annotators can provide outputs that are culturally suitable for the audience. Similarly, selecting a diverse and culturally representative workforce can minimize the existence of biases in the labels, and help increase the generalization power of the model.

Curate / select your data properly to avoid waste

Data curation is a crucial step in ensuring the efficiency of the data annotation process. It involves carefully selecting and filtering data to eliminate any irrelevant or redundant information. This not only helps avoid wasting time and resources, but also improves the performance of the subsequent model. In order to achieve optimal results, it is essential to consider the granularity of the data being annotated. This refers to the level of detail and specificity required for the annotation task. Additionally, defining the stages of the annotation process can help streamline the work and optimize the overall workflow. By taking a thoughtful and systematic approach to data curation, organizations can guarantee the quality and value of their training data.

Choose the right experts for the task

Decide on your labeling provider based on the expertise you’ll need for your data annotation project. Make sure you understand the requirements, as not every data annotation project will be easy. Many times, there are complex annotation projects where expertise is needed. Let’s be honest: there are many labeling companies out there to pick from. However, few can offer “niche” expertise in areas like medical imaging or space engineering. And in order to avoid errors, make sure you select the right workforce in apt numbers who will feel comfortable navigating and managing at all stages of the process.

Annotate continuously to test your labeling process’s effect on the model

It’s crucial to the success of an annotation project that the labels are as accurate as possible. That said, most people fail to consider that it is hard to define the right annotation process in a vacuum, meaning without analyzing how it is impacting the model’s learning process. The annotations might very well be “accurate”, in the sense that they are in line with your instructions, but they might still be inappropriate to the model. The way you plan the execution of the project will decide on what trajectory the project will go and also the authenticity of the results. A pro-tip here is to create processes where a continuous feedback loop is established. You should also never underestimate the impact of your ML team’s involvement, as their feedback on the project’s progress can help a lot. Besides, as over time, it is likely that your project will mature and its associated requirements might change, the choices you made initially might need to get revised from time to time.

Define clear quality targets to make quality control easier

Before you start any annotation process, make sure you have a fixed set of standards & metrics to define the quality of data annotation you need for your project. Do this beforehand in order to achieve desirable quality and results. Make sure you apply concurrent & post annotation quality control checks to ensure everything is as it should be. Of these two, the concurrent checks play the most critical role, as they allow addressing problems during the annotation process. These quality checks are here to confirm that you are on your way to a successful annotation project.

This can include tasks such as preparing the data, defining annotation guidelines, and quality control checks to ensure the accuracy and consistency of the annotated data.

AI is entering a phase of quick development, which requires the labeling of training data to be faster and more reliable than ever before. Besides, due to the current global economy, there is ever less room for wastage and error. You need the right labeling process right off the bat. Your ML team incorporating the best data annotation practices will make sure you don’t come across bad surprised down the line, which could cost you and your company significant amounts of money.

Organizations can try tools like Alectio to annotate their labeled data accurately and more efficiently, helping their teams to develop at a better pace and quality.

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References:
– 5 ways to save on data labeling
– Agile Data Labeling: What it is and why you need it