How to Develop a Solid Data Annotation Strategy for an ML Project?

1. Building Blocks for Efficient Data Labeling Strategy
   1. Data
   2. Humans
   3. Process
   4. Technology
2. The Top 5 Data Annotation Strategies in Machine Learning from Label Your Data
   1. Measuring the Scope of the Dataset Volume as Part of a Data Annotation Strategy
3. Build Better Data Annotation Strategies with Label Your Data
4. FAQ

Machine learning, deep learning, data analysis, and NLP help 48% of businesses to get the most out of their large datasets. However, without a sound data annotation strategy in place, these advanced technologies may fall short in delivering optimal results.

For machine learning projects, a well-thought-of annotation strategy guarantees effective model performance. We decided to explore the essentials of developing such a strategy, covering key considerations, methodologies, and our best practices.

From selecting appropriate annotation types to mitigating biases, this guide will equip you with the insights necessary for building a data labeling strategy and creating a dataset tailored to your specific ML project needs.

Building Blocks for Efficient Data Labeling Strategy

Why is having a strategy essential for effective data labeling? About 80% of the time spent getting an ML algorithm ready is dedicated to collecting, cleaning, and annotating data. Therefore, a well-defined annotation strategy can streamline the entire machine learning pipeline.

Before we share our top annotating strategies at Label Your Data, let’s look at the dynamics between the core pillars of data labeling, that is data, human input, technology, and optimized process.

Data

As you start planning out your data labeling strategy, you should have a clear understanding of the following:

• What’s the goal of your ML project?

• How much data do you need?

• What sources will you use to gather data?

• Will you use supervised or unsupervised learning?

The problem you want to solve with your ML model will define the type of data, the sources, and the type of ML approach you will use. Also, the more data you have, the more precise your model will be. Hence, when collecting data, make sure to consider where this data is coming from, are there any biases in the data, and whether any changes need to be made according to your project’s goals.

Another important factor is storing your collected data the right way and in the right format. This usually means storing the data in a data warehouse or lake, or even cloud storage, for easier management. Either way, the storage system you choose must be able to meet the needs of your model as the data increases.

Poor data undermines the annotation process, as well as the model performance. For this reason, you should consider data processing services to clean your data by identifying and correcting (or deleting) errors, noise, and missing values. This also implies making your data consistent.

Humans

Despite the level of automation we’ve reached so far, data annotation cannot do without human intelligence. Of course, you can automate and speed up the process, but always make sure to have human experts on your team. They bring the context, expertise, experience, and reasoning to streamline the automated workflow.

This is especially crucial for tasks like detecting the sentiment or emotion in data, for example in content moderation services. Think of this: would a machine be able to understand sarcasm or propaganda the way we do?

Building the annotation team is part of annotation strategies. You should beware of the team’s structure and the right correspondence of roles and responsibilities. For instance, you may have data engineers, data scientists, data annotators, data labeling managers, and MLOps engineers. Each of them must understand their roles and goals to achieve. For edge cases in data annotation, you need subject-matter experts for complex domains, like healthcare, finance, scientific research, or for multilingual tasks in NLP.

Process

The data annotation process must be scalable, well-organized, and efficient. It’s an iterative process, involving constant monitoring, feedback, optimization, and testing. Each data labeling project in ML is unique, however, they usually fall under one of the categories:

• Data labeling for initial ML model training

  A typical process involving annotators working on a set of unstructured data. They follow project-specific instructions and deliver high-quality training data that is further fed into the ML model.

• Data labeling for ML model fine-tuning

  Sometimes, you need to annotate an additional set of data for your ML model that is already in production. This is done to improve the model’s prediction accuracy, either due to errors, bias, or new data available.

  This process consists of:

  1. Running the pre-trained model over the initial dataset

  2. Recording the predictions

  3. Watching the performance of the pre-trained model

  4. Revealing bias in the algorithm or any other issue

  5. Relabeling the new dataset

  6. Preparing the dataset for model training

  7. Testing the model once again

  Keep iterating this process until you reach the desired accuracy of your model.

• Human-in-the-loop (HITL) and active learning

  As more sophisticated models are being developed, they require more and more annotated data. Manual approach may not always work for such cases, and so the best way to speed it up is to combine human intelligence with automated approach. This technique is known as “human in the loop,” or simply HITL.

  In a nutshell, the concept of HITL looks like this: annotators label a dataset sample for model training and then this dataset is used to teach machines to detect and label data points in a transfer-learning model. It automatically continues the annotation process. Low-confidence predictions are sent back to humans for review (say the machine is unsure whether it’s a bird or a plane in the photo). The model learns from annotators’ scores of its predictions, which is called active learning.

Technology

If you consider bringing automation into your data annotation strategy, you should choose the tooling that will meet your core ML project needs and support model-specific annotation. Most importantly, your labeling tool must allow data imports from various data sources you’re going to use.

A labeling tool should support all the different types of data, from images and videos to text and audio data. And in different formats. Of course, you can use several tools (one for computer vision data and the other one for NLP data). Yet, having one tool handling all data types drastically simplifies this process.

Another aspect to think about is having an automatic annotation option in your tool. This can help when your team is dealing with large datasets. Last but not least, your labeling software should have an intuitive interface for managing multiple teams and projects.

The Top 5 Data Annotation Strategies in Machine Learning from Label Your Data

A strategic approach helps in defining clear guidelines, standards, and objectives for annotation teams. They can better minimize errors, enhance the quality of labeled datasets, and ultimately contribute to the development of robust ML models.

Yet, you can achieve the same and save valuable time if you decide to outsource data annotation to experts. Get your free pilot from Label Your Data to see how it works in practice!

Here are our top data annotation strategies for your ML project:

1. Ask questions before taking actions

   Before you start the project, grasp the specific issues it’s trying to solve. Work with your team to answer these questions:

   • What does your project aim to achieve?

   • How much and what type of data is needed?

   • How to do data annotation correctly?

   • How accurate does the ML model have to be?

   • How much time do you need to finish the project?

   • What results do you expect?

   • Is the budget sufficient for the results you want?

   • What to choose between in-house vs. outsource data annotation?

   After answering these questions, you can set up a team and a data annotation process.

2. Plan, document, and secure your workflows

   Consider your datasets as integral parts of your organization’s intellectual property (IP) and the ML project itself. This strategy highlights the importance of thoroughly documenting the entire process.

   To enhance the scalability of data operations, document annotation workflows to establish standard operating procedures (SOPs). This not only protects datasets from theft and cyber threats but also ensures a transparent and compliant data pipeline according to data labeling and data privacy guidelines.

   Before project commencement, make sure to:

   • Establish clear processes,

   • Obtain necessary labeling tools,

   • Set a comprehensive budget covering tool expenses, human resources, and QA,

   • Gain expert support,

   • Secure resources, including operating procedures.

3. Treat data annotation as an iterative process

   To establish an effective strategy for data labeling operations, start with a small-scale approach. By doing so, you can learn from any minor setbacks that may arise, make necessary improvements, and then gradually expand the process.

   It’s also important to avoid the risk of attempting to annotate too much data in a single go. This increases the likelihood of mistakes by annotators. Starting small allows you to invest less time initially compared to starting with a larger dataset.

   Regularly monitor annotation progress and be prepared to adapt the strategy based on feedback, challenges, and evolving project needs. Once you’ve achieved a smooth operation, including the integration of appropriate labeling tools, you can move forward with scaling up the entire operation.

4. Communication and consistency are your cornerstones

   Data annotators are more effective when they understand the purpose behind their labeling tasks. Communicate the project goals and the specific requirements to your team. Explain the necessity of annotations and connect it to the overall business value. This is particularly crucial when working with internal teams whose primary focus is outside of data annotation.

   Summarize the guidelines by providing examples of a “gold standard” to assist in understanding complex tasks. Introduce an “unknown/uncertain” class to efficiently address issues in guidelines and ontology. Highlight edge cases and errors to minimize initial mistakes.

   Clearly communicate the evaluation criteria to annotators, preventing potential issues during reviews. Implement version control for guidelines to adapt to the ML project lifecycle. Among various annotation strategies, this one is essential as it ensures continuous improvement.

5. Fine-tune your QA procedures

   Develop a systematic quality assurance (QA) process to improve labeling quality. Similarly to data annotation, the process of checking its quality also follows an iterative cycle.

   Knowing how to measure data quality after it’s been annotated is pivotal. You can’t fully avoid errors, inaccuracies, and issues like poorly-labeled images or video frames in the model. Thus, constant quality monitoring is key.

   Sometimes, to reduce human disadvantages, you may employ automated tools. Choosing the one that seamlessly integrates into your quality control workflow is crucial for faster resolution of annotation bugs and errors. This becomes especially beneficial when deploying automated data pipelines, active learning pipelines, or micro-models, allowing for more efficient and cost-effective feedback loops.

Measuring the Scope of the Dataset Volume as Part of a Data Annotation Strategy

Knowing how to measure the volume of the dataset to label is important for each of the annotation strategies. For annotators, this allows assessing the project complexity and setting realistic deadlines, while project managers can better allocate resources and distribute tasks efficiently.

Additionally, knowledge of the size of the dataset helps evaluate the overall progress of the ML project, making it easier to identify potential bottlenecks and allocate additional resources if needed.

We suggest taking these steps:

1. Count the number of instances

   Determine the total number of data points or instances in your dataset. This could be the number of rows in a table, documents in a corpus, images in a collection, etc.

2. Evaluate data complexity

   Assess the complexity of the data. Consider the variety and types of data and the diversity of labels or categories needed.

3. Examine feature space

   If your dataset has multiple features, assess the dimensionality of the feature space. The number and types of features can impact the annotation effort.

4. Consider annotation granularity

   Understand the level of detail required for annotation. Finer granularity may require more effort (i.e., annotating each word in a document versus annotating the document as a whole).

5. Understand the difficulty of the labeling task

   Annotation tasks vary in complexity. Labeling images, for instance, can include object detection, segmentation, or classification, each with differing levels of difficulty. Assess the complexity of annotating each instance, as some may be straightforward (like those in data entry services) while others demand more nuanced judgment.

6. Analyze time requirements

   Estimate the time required to label each data point. This can depend on the task and the expertise needed for accurate annotation.

7. Account for iterative annotation

   If data annotation is an iterative process, consider that some annotated data may be used to improve ML models and guide subsequent labeling efforts.

8. Use sampling techniques

   If the dataset is large, you might consider sampling a subset to estimate the annotation effort required. Ensure that the sampled subset is representative of the overall dataset.

9. Consult domain experts

   Seek input from domain experts to understand the context and intricacies of the data. They can provide valuable insights into the annotation process.

   With that said, these key steps provide a foundational framework for measuring the scope of dataset volume and enhancing the effectiveness of your data labeling strategy.

Build Better Data Annotation Strategies with Label Your Data

For machine learning and AI projects, achieving a well-organized, consistent data annotation workflow depends greatly on the chosen strategy and how your manpower and data are managed.

You can continue learning about data annotating strategies and how to implement them into your ML project. Or, you can make a smart move and let experts do their job. We at Label Your Data can take care of all your data annotation needs.

FAQ

by Yuliia Kniazieva
on February 8, 2024. Share on LinkedIn Post on Twitter