A right prediction is the output that expected by every developer while training a model through machine learning. And to get such desirable outputs, set of raw data needs to be layered properly with wise human decisions to train such models that can work itself take similar decisions while working on new pieces of data.

Humans are playing a key role in training such models through various forms. Image annotation is one of them, an intelligible contribution that translate the human knowledge into such form so that these models can understand and the method in which predictions by models can in turn understood by the humans.

While working with machine learning models predictions are the key role player in the entire project. And there are mainly types of model predictions adopted in machine learning. In first use case, predictions are used to partially or semi-automate the labeling process. The advantages of using the semi-automating is the cost of human labor is comparatively low and it also reduces the training time taking process into the production line.

While on the other hand, another use case model predictions is keep monitoring the model assessments and at the same time improve the accuracy of organizing the machine learning systems. In this case, each model prediction is accompanied by confidence score where a low confidence scores go through a review process done by humans.

Here the decision of humans can override the model’s prediction and resulting data feedbacks into training that very same model in production. So, here we will discuss about what are the two cases of model predictions are used while building the machine learning based models.

Types of Use Cases of Model Predictions in Machine Learning

Organizing training data sets in right manner is one of the most complicated processes into machine learning that requires a strict and disciplined curating by experts. But predictions make this notion wrong by adding a machine into labor intensive development process.

As per the normal course of action, training data machine learning takes the same form as predictions an output of model can be used to make an initial annotation of raw data in real time. And this data can be used to feed through the training data development where it can be further improved with the help of labeling team.

After reviewing and labeled by the team, the improved annotations would be used back into a model as a data set to increase the accuracy of prediction. And this kind of strict feedback loop is known as semi-automatic labeling in machine learning world.

Also Read: How to Create Training Data for Machine Learning?

SEMI-AUTOMATIC LABELING – Predictions with Manually Pre-label Data

The best part of semi-automatic labeling is that predictions are used to pre-label data and as per the research and experiments, semi-automatic labeling can outperform manual labeling across bounding boxes and polygon shapes with better results.

Though, correct prediction is also much faster than manually labeling of data but when a prediction is wrong, it is often but not always faster to correct a label rather than label it from scratch. And the types of labeling configuration can affect the predictions.

Though, if you think because a label is not faster to fix does not means that it wouldn’t be faster overall to use predictions to pre-label data. But here question arise how many times model is correct and how to calculate the benefit of time saved during the labeling.

How efficiency of predictions evaluated?

Thus, the answer for the last line in previous Para is right here. To get the answer of this question we need to evaluate the efficiency of using predictions on you own project you first need to decide how often your model is correct. And the next step is measuring the changes in labeling speed for an accurate label and incorrect label.

For an example, if you are working on a model that is correct 80% of the time. Assure here each image takes around 6 seconds to get labeled. And if the model is correct you will save about 5 seconds per label while assuming that it take 1 second to accept the correct pre-label image. However, if model is not correct you will do it at faster speed but comparatively at slower rate than when the model is completely correct.

So, right here let’s imagine 1 second is lost in every label correction that leads to an average net positive gain of 3.8 seconds per label, resulting in a cost reduction of about 60%. And it matters when you achieve state-of-the-art performance for deep learning models that often requires millions of labeled images with best quality.

QA PRODUCTION MODELS – AI-enabled Fully Automated Predictions

The fully functional machine learning application is believed to be one that makes decisions independent without human interference. In this case the labeling process is entirely automated and all predictions would be decisions and this workflow chart would compress into a single step. However, such models operating in the real are not 100% accurate every time. While AI-based applications only reduce the need of human input but not eliminate completely.

However, humans play an important role in machine learning, even after the deployment of models. Actually, data collection, training, and deployment – the three basic pillars of machine learning are not isolated stages that happen in a sequential order.

All they operate at the same time and interactively to form a sophisticated and complex workflow. And like any other mission’s critical system once the model has been installed it still needs to be maintained and updated on regular basis. And this is another scenario where predictions can fit into a big picture of building best deep learning models.

Every prediction that a model makes in a real world application is accompanied by a confidence score. And models are great at showing us how confident they are in their prediction. Hence, you can set a confidence score benchmark to define how to treat a model’s prediction in production. A prediction with a confidence score above that benchmark will be considered as final decision without human intervention whereas a prediction below the confidence benchmark will go through a quality check assurance process.

Also Read: How to Measure Quality While Training the Machine Learning Models?

Predictions facilitate a best way of conducting the quality assurance at scale. And predictions, training data and quality assurance are all rendered visually in such cases. Since the subsequent quality assurance data is also like training data that can be used back into the model to improve accuracy level in batch or on real time basis.

For an instance, suppose an insurance company uses the model in production to assess the damages automatically. Now imagine a customer sent a photo of his damaged car to insurance company that is further submitted to the model which automatically predicts the exact location and severity of damage.

Here the predictions are considered with confidence score and if the confidence score is low the model’s prediction would not considered a conclusive claim. And such doubtful claims need to routed through human check who will conduct the physical examination of damage for actual assessment of production. And such manually data collection is then fed back into the model as training data and adding such new improved training data will continue to improve the accuracy of production models and also confidence in its predictions.

Summing-up

In the whole discussion we got to know how predictions can be used to increase the speed of labeling and assure the prediction accuracy in production line. As compare to traditional labeling process where the labeling was purely done manually by humans, the predictions introduce machine-driven automation into the training data loop. And, as apposed to models in production being a purely automated process assuring quality predictions with low confidence scores are always improve and update increasingly performant models. And Cogito is the providing such labeled training data sets use into machine learning for accurate predictions.

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 directly 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 about how industry standard methods used for measuring consistency and accuracy. But we proceed, check below what are the advantages of 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.

Actually, consistency is the parameter labeler annotators agree with one another and helps to ensure that labels are correct or incorrect in consistent manner. The level of consistency is measured through a right algorithm and without the automation of advance AI tools this process is manual, time taking and also unsecured.

While, accuracy means how close a label is to the “Ground Truth” which also known as subset of the training data is labeled by the experts or data scientists have knowledge to test annotator accuracy. The benchmark is determined to measure the accuracy and allows data scientist to monitor the quality of data to examine and troubleshoot the quality related issues by providing the insight into accuracy of the labelers work.

Review is another method to check the accuracy and this done by trusted experts after completion of labeling. The review process is performed by spot checking the labels while few projects review all labels. Review helps to use to identify accuracy level and inconsistencies in the labeling process, whereas benchmark is often used know the performance of labeler.

Benchmarks, is considered one of the economical way 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.

QUALITY WORKFLOWS

Idyllically, quality assurance is an automated process, that works continuously, during your training data development and improvement processes. Such data labeling consensus and benchmark features, you can automate consistency and accuracy tests with amazing results. As 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 right process.

Benchmarks

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 is 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 drop in quality you can explore performance by labeler or benchmark. Systemic poor labeler performance is often indication of poor instructions while poor performance on certain pieces of data is often indicative of edge cases.

For an example, if you can compare the benchmark with accuracy by clicking on benchmark to see the crowded jellyfish image and compare the same. Though, they are very similar as the labels is 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

Consensus actually measures the rate of agreement between multiple annotators that is usually humans or machines. To calculate the consensus score divide the sum of agreeing 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 also 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 Consensus process you can customize the percentage data and number of labelers to test to monitor the consistency with Consensus histogram. To compare the labels of a particular image you can breakdown the consensus score by 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 the better alternative solutions.

REVIEW

To ensure the quality, getting the control of data with tight feedback loop between machines and humans enables building a 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.

Review Workflow

Reviewing is a manual process and become part of a human in loop. And to perform this action you have to either choose which labels to review or modify or re-enqueue labels. And the best example of reviewing is filtering that helps annotators to prioritize which labels to review. In this process filters include labeler, consensus score, 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, reviewer has options to modify or correct the labels on the spot. Moreover, it also allows reviewers to delete and re-enqueue the label or view the benchmark wherever applicable with the options to copy the link to send to other reviewers for better decision.

Summing-up

While building the machine learning applications, creating a training data is one the most costly components. And 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, it 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 the 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.