A right prediction is an output expected by every developer while training a model through machine learning. And to get such desirable outputs, a set of raw data needs to be layered properly with wise human decisions to train such models that can work themselves to 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 translates human knowledge into such form so that these models can understand and the method in which predictions by models can in turn understood by humans.
While working with machine learning models’ predictions are the key role player in the entire project. And there are main types of model predictions adopted in machine learning. In the first use case, predictions are used to partially or semi-automate the labeling process. The advantages of using semi-automating are the cost of human labor is comparatively low and it also reduces the training time taking the process into the production line.
While on the other hand, another use case model prediction is to 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 a confidence score where 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 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 the right manner is one of the most complicated processes in machine learning that requires strict and disciplined curating by experts. But predictions make this notion wrong by adding a machine into the labor-intensive development process.
As per the normal course of action, training data machine learning takes the same form as predictions an output of the 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 the 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 the 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 data 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 mean that it wouldn’t be faster overall to use predictions to pre-label data. But here the question arises of how many times the 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 the previous Para is right here. To get the answer to this question we need to evaluate the efficiency of using predictions on your 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 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 takes 1 second to accept the correct pre-label image. However, if the model is not correct you will do it at a faster speed but comparatively at a 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 require millions of labeled images with the best quality.
QA PRODUCTION MODELS – AI-enabled Fully Automated Predictions
The fully functional machine learning application is believed to make decisions independently 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 for human input but not eliminate it.
However, humans play an important role in machine learning, even after the deployment of models. Data collection, training, and deployment – the three basic pillars of machine learning are not isolated stages that happen in 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 the big picture of building the 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 a final decision without human intervention whereas a prediction below the confidence benchmark will go through a quality check assurance process.
Predictions facilitate the best way of conducting 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 a 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 the insurance company that is further submitted to the model which automatically predicts the exact location and severity of the damage.
Here the predictions are considered with a confidence score and if the confidence score is low the model’s prediction would not be considered a conclusive claim. And such doubtful claims need to be routed through human check who will conduct the physical examination of damage for actual assessment of production. And such manual 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.
In the whole discussion, we got to know how predictions can be used to increase the speed of data labeling and assure prediction accuracy in the production line. As compare to the 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 opposed 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 providing such labeled training data sets used in machine learning for accurate predictions.