Advantages of Automation Labeling Process for Machine Learning
Data labeling takes care of the most important work for machine learning — it gets the data ready for it. Thus, this process must be carried out with special care in order for the final ML model to function properly and provide accurate and reliable analytics.
Even though data annotation has long served as a crucial connecting factor for AI-based systems and technologies, the use of automation in this field is still in its infancy. Automatic labeling data, however, holds promising potential for helping annotators deal with massive amounts of raw and unstructured data. By automating minor and repetitive labeling tasks, annotators can focus on edge cases that require human supervision.
And it’s only one of the several advantages that automation brings to the process of data annotation. If you want to see more of them, keep reading the article!
Automation vs. Human Workforce: Who Can Do Better?
Labeled data is the cornerstone of machine learning projects. Such data is structured and understandable for machines to generate recommendations and predictions, enable big data analytics, and so on.
Automation is no longer a buzzword in today’s data-driven environment. So, why do data annotation experts are still vague about this option?
By its very nature, data labeling is a tedious manual process. It takes hundreds of hours for human annotators to label each piece of data manually, whether it’s an image, a video, a text file, or even an audio file. These labels help machines recognize various objects depicted or mentioned in that data. Manual data labeling, however, takes a lot of time and effort, which doesn’t work well for businesses aiming to succeed in the current fast-paced digital world.
Automated data annotation is performed by machines (and, yes, for machines). Auto labeling can be done using heuristic techniques, machine learning models, or a combination of the two. A heuristic approach involves processing a single piece of data in accordance with a predetermined set of criteria. Automated methods for labeling data are also divided into the groups, such as programmatic labeling and model-assisted (AI-assisted) labeling. In general, automated alternatives have the advantage of being cost-efficient, and they can also ensure a fairly efficient process of data labeling.
Still, automation is a tricky business and isn’t always a one-size-fits-all solution for machine learning projects. Each AI initiative is unique: some projects can fully rely on automation, while others might end up badly with no human intervention. With that said, let’s dive into the benefits that automation brings to the process of data annotation and see why this option is not for everyone.
Top 8 Advantages of Automated Data Labeling for Machine Learning
Machine learning initiatives require 80% of the time allotted for preparation, of which 25% is spent on data annotation. As such, the need to accelerate the ML project is inextricably linked to the process of labeling data. This is why there’s so much interest in automation among businesses relying on data and working with AI.
They value automation labeling process for the following reasons:
Auto labeling allows pre-applying labels without entirely replacing human input. You can, therefore, pre-annotate a portion or the entire dataset. Automation is also a good option for checking, revising, and completing the annotations. However, there might be exceptions and edge cases that automation can’t cover, and it’s far from flawless. So, human supervision is required.
2. Reduced workload
The amount of human labor needed to perform data annotation for an ML project can be cut back with automation. A confidence level can be assigned by an auto-labeling model depending on the use case, the complexity of the task, etc. Labels are essential elements that enrich the dataset. Thus, annotations with lower confidence ratings are forwarded to a data expert for evaluation or revision.
3. Speed & cost
When compared to the conventional manual process, AI-assisted labeling is quicker and less costly. Businesses can save considerable operating costs and time by using automated data labeling, which requires minimal or no human involvement and eliminates the need to hire tech experts or build your own in-house team to handle data annotation.
Automated data labeling generates highly accurate annotations using active learning, which is a semi-supervised method. In order to label more data using active learning, the annotator must first choose an initial sample from raw, unlabeled data. Additionally, you may use automation to keep enhancing all the human-led data labeling procedures.
Model-assisted auto-labelers can be integrated into the user interface of labeling systems. Some good examples are V7 Labs, Roboflow, and LabelBox. These let you use a sample of manually labeled data, like images, to train your own models. So, when the user loads new photos for data annotation, the UI will start labeling them automatically.
Expanding your scope of work and the labeling effort of your data team is easier with model-assisted labeling. Using bounding boxes, for instance, it’s much easier to automatically annotate big objects or work with repetitive tasks. Besides, human annotators have more time they can dedicate to more sophisticated and time-consuming objects in the same image that need polygon annotation.
7. AI-human teamwork
Programmatic labeling is a great way to integrate the expertise and tech skills of subject matter specialists and human annotators into a machine learning model. Hence, instead of relying solely on probability in this case, heuristic rules are applied to automate the process. When done correctly, auto labeling offers a unique fusion of AI scale and human agency.
8. Complex tasks
When model-assisted strategies fail to accurately annotate complicated datasets, the automation labeling process offers a viable solution. As the project advances, other heuristic rules can be built and modified. For highly developed enterprise-level procedures in machine learning, programmatic labeling is a perfect option.
Yet, is automation the only option to scale your machine learning project? Or enhance your data practices?
There Is a Secret Option Number #3
For projects in machine learning, there is no one data annotation technique that works for all cases. Although automated labeling methods are more widely available and simpler to use, they are still not a perfect solution to the data labeling bottleneck. Building solid manual foundational datasets is essential in all circumstances.
Working with training data is typically thought to be a fully manual process. However, as machines are introduced into this laborious procedure, predictions put this notion to the test. Because training data and predictions share the same format, a model’s output may be immediately utilized to annotate raw data in real time.
Then, the data annotation specialists may evaluate the data, clean it up, and re-feed it to the machine learning model as part of the training data pipeline. They thus get superior outcomes and precise forecasts. This is the secret option we talked about — semi-automated data labeling. It’s frequently used by data annotation companies. For example, labelyourdata.com provides a number of services to annotate client data using a semi-automated method.
Final Words on Auto Labeling for Machine Learning
Automation of data labeling is indeed a revolutionary way to handle mass volumes of unstructured data and retrieve meaningful information from it for machine learning models. It does not, however, completely address all data labeling issues.
AI-assisted annotation can help businesses grow by enabling their data teams to concentrate on higher-level activities that require thorough human supervision. It may be a terrific approach to efficiently arrange your time and tasks for maximum efficiency and prolific results.
So, we suggest you keep an eye on auto labeling as you plan your next machine learning project. This area will continue to advance, as it already bears a promising potential for the future of AI.
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