How to Measure Quality While Training the Machine Learning Models?
The quality of data in training the machine learning models is one the most important factor while developing such models. The quality means here is the accuracy and consistency of labeled data. While calculating the training data, benchmarks consensus, and review are the industry standards followed by annotators. We need to figure out here what combination of these quality assurance procedures is suitable for your project.
Here in this article, you will learn about the definitions of quality, consistency, and accuracy and why quality matters in training the machine learning models. Here you will also get to know about the industry-standard methods to quantify quality and what are the most cutting-edge tools used to automate quality assurance processes in this field.
Consistency or Accuracy, which one is important
Quality here is related to consistency and accuracy, this is not just how correct a data or label is but also how frequently it is correct. So, here we will discuss how industry-standard methods are used for measuring consistency and accuracy. But we proceed, check below what are the advantages of a quality check in machine learning training.
Benefits of Quality Check in Training the Machine Learning:
1. Monitor the consistency and accuracy of training data.
2. Quickly troubleshoot quality related errors.
3. Improve labeler instructions, on-boarding, and training.
4. Better understanding of their project on what and how to label.
Consistency is the parameter labeler annotators agree with one another and helps to ensure that labels are correct or incorrect inconsistent manner. The level of consistency is measured through the right algorithm and without the automation of advance AI tools this process is manual, time taking and unsecured.
While accuracy means how close a label is to the “Ground Truth” which is also known as a subset of the training data is labeled by the experts or data scientists who have the knowledge to test annotator accuracy. The benchmark is determined to measure the accuracy and allows data scientist scientists to monitor the quality of data to examine and troubleshoot the quality-related issues by providing insight into the accuracy of the labeler’s work.
Review is another method to check the accuracy, and this is done by trusted experts after the completion of labeling. The review process is performed by spot-checking the labels while few projects review all labels. The review helps to use to identify accuracy levels and inconsistencies in the labeling process, whereas a benchmark is often used to know the performance of the labeler.
Benchmarks are considered one of the economical ways of assurance option, as it involves the latest amount of overlapping work. And it can only cover the subset of the training data. While on the other hand, Consensus and Review are more expensive, but cheaper than each other depending on the consensus settings and the level of review.
Idyllically, quality assurance is an automated process, that works continuously, during your training data development and improvement processes. With such data labeling consensus and benchmark features, you can automate consistency and accuracy tests with amazing results. This test allows you to customize the section of your data to test and the number of labelers that will annotate the test data with the right process.
This process of data quality and to follow this process of testing the quality you have to create a new Benchmark by starring an existing label. Below, as a sample project for benchmarks workflow is mentioned in which rectangular bounding box technique is used in image annotation services, though polygon and point options for more fine-tuned shapes are also available here for more fined tuned annotations. And once you have started a project and labeled the ground truth data, using the benchmark star you can tag the labels.
In this method, labelers are randomly benchmarked, and you can observe the project quality with the overall quality chart. And if troubleshoot drops in the quality you can explore performance by labeler or benchmark. Systemic poor labeler performance is often an indication of poor instructions while poor performance on certain pieces of data is often indicative of edge cases.
For example, if you can compare the benchmark with accuracy by clicking on the benchmark to see the crowded jellyfish image and compare the same. Though, they are remarkably similar as the labels are not agreed on how much of the tentacle to be included in each bounding box with the options to edit, delete and re-queue the label.
Consensus measures the rate of agreement between multiple annotators that is usually humans or machines. To calculate the consensus score, divide the sum of agreeing on labels by the total number of labels per asset and get the accurate score results.
The workflow of Consensus enables consensus and customize the consensus parameters and facilitates, as random labels are distributed across labelers at random intervals. This process also keeps track of overall consistency and investigates any dips in quality by looking into individual labeler and label consensus score.
While measuring the quality with the Consensus process you can customize the percentage data and number of labelers to test to monitor the consistency with the Consensus histogram. To compare the labels of a particular image you can break down the consensus score by an asset. This is a two-way part of research and development enabling AI teams to be innovating in such projects to think about the problem from various perspectives also provide the opportunity to find out better alternative solutions.
To ensure quality, getting control of data with a tight feedback loop between machines and humans enables building functional machine learning applications for various needs. Envisaging the data is vital and not only helps in controlling the quality check but also for developing a deep understanding of a solution for machine learning data related issues.
Reviewing is a manual process and becomes part of a human loop. And to perform this action you must either choose which labels to review or modify or re-queue labels. The best example of reviewing is filtering that helps annotators prioritize which labels to review. In this process, filters include labeler, consensus score, the label contains, and more.
Reviewers are considered the best mode of quality measurement that labelers do with their internal knowledge and experience. Along with reviewing with thumbs up or downs icons, a reviewer has options to modify or correct the labels on the spot. Moreover, it also allows reviewers to delete and re-queue the label or view the benchmark wherever applicable with the option to copy the link to send to other reviewers for a better decision.
While building machine learning applications, creating training data is one of the costliest components. Consistently monitoring training data quality improves the chance of having a well-performing model even develop for the first time. While getting labels right the first time, is cheaper than the cost of identifying and redoing work to solve such problems.
However, using the right tools and techniques can ensure your labeling maintains the level of quality you need to develop the best model and get the expected results you are seeking. Cogito is the most suitable company providing high-quality training data for machine learning and computer vision with image annotation and data labeling service for various industries like Healthcare, Ecommerce, Automobile, Agriculture, and various other sectors working on AI-based models through machine learning.