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[Infographic] Workflow to label your ML data in-house

[Infographic] Workflow to label your ML data in-house

Data labeling is a crucial part of supervised machine learning modeling.

Your in-house data or data acquired from external sources must be cleaned, labeled, and annotated to effectively train, test, and validate your machine learning models.

But who labels your data?

Do you have enough skilled employees to label your data in-house?

Are they trained on the processes and tools to label your data?

Do you have a list of data labeling best practices for your labelers to follow?

We will answer these questions in this post.

Phases of data labeling

Data labeling contains four phases:

1 - Data collection: you may acquire data from external sources, look to use your in-house data, or a combination of both. The first phase of data labeling starts with collecting and collating data in one place.

2 - Data tagging: When you collect data, most of it will be unlabeled. This is where your labelers spend time sifting through each column of data and tag each data element.

3 - Checking data labeling quality: As your labelers begin tagging and labeling data, you need a process to quality-check the labeling for accuracy. Besides labelers, you need QA inspectors (managers or admins) to review labeled data against a predefined checklist to meet quality data labeling requirements.

4 - Training your ML models: As the data get labeled and quality-checked, you may hand over the data to your ML engineers to train the ML models. The ML model's output will determine the accuracy of labeled data.

Best practices of data labeling

Your in-house data labeling efforts may involve many people—labelers, managers, admins, QA specialists, etc.

To make everyone's job easy, you need a well-defined set of guidelines and best practices to label your data quickly, accurately, and cost-effectively.

Errors or delays in labeling your data adds to your ML budget.

Here is a checklist of seven simple points you need to address to make your data labeling effective and friction-free.

1 - Collect diverse, specific data: Diverse data minimizes bias, and collecting specific data makes your ML models more accurate. What is specific data? Let's say you want to build an AI solution to create a robot-waiter. Collecting data from restaurants is specific data. Collecting data from airport food courts and mall eateries isn't.

2 - Set up a data labeling guideline: Create a guideline that defines the labeling process, labeling names and tags, and how to use the tools.

3 - Create a visual, easily understandable data labeling workflow: A workflow visually defines the labeling process. This is easy to remember and refer to when needed.

4 - Establish communication: Establish a clear line of communication between labelers, admins, QA, and ML engineers.

5 - Establish a QA process: Integrate a QA method into your project pipeline to assess the quality of the labels and guarantee successful project results.

Three ways to conduct quality checks
  • #1 Timely audits: Your QA folk need to perform quality checks at regular intervals.
  • #2 Targeted discussions: Allow your QA and labelers to discuss disagreements in labeling patterns, conventions, and processes.
  • #3 Random checks: This should happen besides regular quality checks to test the quality of data labeling.

6 - Provide regular feedback to labelers: Communicate annotation errors with your workforce for a more streamlined QA process.

7 - Run a data labeling pilot project: Put your workforce, annotation guidelines, and project processes to test by running a pilot project.

Three ways to label your data

While this post addresses one way of labeling your data - in-house, there are two more ways to get your data labeled.

#2 Outsourcing: Hiring a data labeling partner. Data labeling partners usually have quality, skilled data annotators on their payroll and deliver quality data labeling services at affordable costs.

#3 Crowdsourcing: If you lack internal resources, you may consider crowdsourcing your data annotation projects to a trusted third-party platform that helps you hire data annotators temporarily.

How to choose the best data labeling tools

Your data annotators are only as good as your data labeling tools.

Either you buy an existing data labeling tool or build one yourself.

Here's a checklist of five points you should consider while choosing tools to label your data in-house.

#1 Inclusive tools: Choose tools that fit your data labeling use case. Maybe you need the polygon tool to label cars or a rotating bounding box to label containers. Consider your future data labeling requirements and choose tools that might fit your future use cases as well.

#2 Integrated management system: The tool you choose should have an integrated management system built-in. This allows your managers to track project progress and communicate with all stakeholders.

#3 Quality assurance process: The data labeling tool you choose should contain features that allow your QA and admins to perform quality checks without needing to exit the tool and use another.

#4 Privacy and security: As data labeling tools are cloud-operated, you must choose a built-in enterprise-grade data security tool that you can trust your data with.

#5 Technical support and documentation: The tool you choose should be backed with up-to-date documentation and technical support at any juncture (paid or free).

Choosing a data labeling partner

Sourcing, training, and structuring data to train, test, and validate your machine learning models is an expensive and challenging task to carry out in-house.

Outsourcing your data labeling and annotation requirements can help you reduce your AI/ML budget.

Outsourcing not only can be cost-effective, but it can also allow you to allocate more money to engineering and data science resources to build robust ML models in quick-time.

That's why you need a reliable data training partner who can quickly understand your project needs timeline and prepare your data swiftly and hand it over for ML model training and testing.

We are Ex-Yahoo!s with over 15 years of experience preparing data for AI/ML modeling. Get your data trained on time and budget now.

Visit www.traindata.us to learn more.

[Infographic] Workflow to label your ML data in-house - Traindata