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Decoding AI success: The complete data labeling guide

  • John Lee 
data labeling

Data labeling is crucial to machine learning model training in AI development. AI algorithms learn to recognize patterns, predict, and perform tasks from accurately labeled data. In this comprehensive guide, we’ll explore data labeling techniques, best practices, and AI project success factors.

We’ve heard a lot about AI in the past decade. From robot assistants to automated industrial processes, this technology has simplified many jobs and lives. Data is a key tool for AI algorithm creation and training. An AI-based algorithm can process massive amounts of data and provide valuable insights.

To make data actionable, it must be labeled so the computer can understand it. Tag your data points to train the Machine Learning algorithm. Machine Learning can automate data processing, but first you must set rules.

What is data labeling?

Data labeling—or data annotation—tags or labels raw data like photos, videos, text, and audio. The data’s entity type, attributes, and characteristics are described by these tags. A Machine Learning model can learn to recognize that type of object in unlabeled data. Data labeling must be efficient and high-quality to train AI and Machine Learning algorithms to understand and learn from your data.

Labeling by class, subject, theme, or other category must be precise. Comprehensive ethical data labeling companies such as Innovatiana improve AI performance and accuracy.

Labeling data—why is it important?

Data labeling is essential to machine learning data pre-processing. Labeling organizes data for meaning. It then trains a machine learning model to find “meaning” in new, relevantly similar data. In this process, machine learning practitioners seek quality and quantity. Because machine learning models make decisions based on all labeled data, accurately labeled data in larger quantities creates more useful deep learning models.

In image labeling or annotation, a human labeler applies bounding boxes to relevant objects to label an image asset. Taxis are yellow, trucks are yellow, and pedestrians are blue. A model that can accurately predict new data (in this case, street view images of objects) will be more successful if it can distinguish cars from pedestrians.

Decoding AI success: The complete data labeling guide

What are the various data labeling types?

Many AI fields work with different data and require different data labeling. Most fields are computer vision for image and video, NLP for text, and audio processing for speech recognition.

Images and videos are used to label data for computer vision

A computer vision model interprets images and videos to identify, classify, and extract object information. Like the example above, this model labels images during data labeling. The labeled data would train the computer vision model to categorize images, recognise object positions, and identify important objects. This model helps retailers manage inventory by identifying shelf products and stock levels.

Data labeling for NLP

AI models can understand spoken and written natural language using NLP. Labelers must identify key passages or label text to train the model in this data labeling method. Even with slightly different wording, the model would learn to understand and interpret the text. The model is often used in customer support chatbots. This model allows a chatbot to understand the question, “When is my package being delivered?” even when phrased differently by customers, such as “When will my package be delivered?” or “What is the delivery date of my package?” and respond accordingly.

Speech recognition through the use of audio processing

Audio processing organizes speech, animal, and construction sounds for Machine Learning. Transcription is often required before audio processing. Tag and classify audio to provide more information.

Speech recognition and NLP often go together. NLP is used to understand text after the audio file is transcribed.

Labeled vs. unlabeled data

Labeled data is a term that is used to describe a data point that has a tag attached to it, which could be a name, a type, or a number. The term “unlabeled data” refers to information that has not been given a label on any occasion.

In order to gain an understanding of the distinction between labeled data and unlabeled data, we will first become familiar with the three different types of machine learning that are available to us. Different kinds of data are required for each of the different types of machine learning.

Data labeling capabilities of an AI data engine

Locating and training human labelers (annotators) starts data labeling projects. Annotators must be trained on each annotation project’s specifications and guidelines because use cases, teams, and organizations have different needs.

After training, image and video annotators will label hundreds or thousands of images and videos using home-grown or open-source labeling tools. An efficient labeling data engine will be available to advanced AI teams.

An AI data engine has all the tools needed to label any data modality. Iterative data labeling is encouraged by this software. An AI data engine allows AI teams to label data in smaller batches instead of using one large dataset to train their model. AI teams provide more scrutiny and feedback at the start of the project, making it more agile. To streamline and improve data labeling, this approach prioritizes labeler-AI collaboration.

A recent Stanford University study found that this agile, data-centric approach reduces training data by 10% to 50%, depending on the task. This reduces data labeling time and cost.

AI data engines enable this iterative data labeling approach and include features to optimize your projects.

Powerful data labeling tools

The right AI data engine for your team should support enough labels and annotations per asset without slowing loading times. This lets you use the data engine for simple and complex use cases, which your team may need in the future.

Ontology-based customization

The AI data engine can be configured to your exact data structure (ontology) requirements to ensure data labeling consistency and scalability as your use cases grow. Labelbox makes it easy to copy your ontology across projects for cascading changes or starting from scratch.

Wide-ranging device performance focus

A best-in-class AI data engine with an intuitive user interface reduces labelers’ cognitive load and speeds data labeling. Professional annotators who work in editors all day need high performance on low-spec PCs and laptops.

Connect data via Python SDK or API for easy labeling

Labeled data should be fed into TensorFlow and PyTorch from an AI data engine. Labelbox is developer-friendly and API-first, so you can scale up and connect your ML models to speed up data labeling and orchestrate active learning.

Decoding AI success: The complete data labeling guide

Data labeling benchmarks and consensus

Quality is measured by labeled data consistency and accuracy. Benchmarks (gold standard), consensus, and review are industry data quality standards.

An AI data scientist must determine the best quality assurance procedures for your machine learning project.

Quality assurance runs automatically during training data development and improvement. Labelbox consensus and benchmark lets you automate consistency and accuracy tests. These tests let you choose the percentage of data to test and how many labelers to annotate it.

Monitoring performance and collaboration

For scalability and security, you need a system to invite and supervise data labelers with expertise in platforms such as CVAT. An AI data engine should invite and review users individually.

Labelbox makes it easy to start a project and invite new members, and there are many ways to track their performance, including image labeling time. You can use automatic labeler consensus or gold standard benchmarks to control quality.

Final thoughts on data labeling with an AI data engine

The old method of training your model with one large dataset no longer works. Machine learning has evolved to be more agile, curating datasets to speed up data labeling and train the model, then evaluating its performance and modifying the next dataset.

An AI data engine promotes this iterative process and gives AI teams tools to accelerate data labeling, allowing them to build better AI products faster. Thus, successful AI product deployment requires an AI data engine.