Model validation service of TagOn: A scaling-up solution for AI data labeling

What is model validation?

What is data labeling?

First, before getting to know data or model validation, we need to know data labeling. In machine learning, data labeling is about the process of adding labels or tags to raw data. These labels/tags create a representation of which class of objects the data belongs to. Besides, it helps a model of machine learning learn to recognize a certain class of objects when faced with data without a label or tag.

What is model validation?

Model validation is the process of evaluating the trained model with a testing data set after it has been trained. The testing data may or may not come from the same data set as the training data. To have a better understanding of things, we may observe that there are two sorts of Model Validation techniques:

• In-sample validation: Data from the same dataset that was used to develop the model is utilized for testing.
• Out-of-sample validation: Data from a new dataset that wasn’t utilized to develop the model is being tested.

Data validation is known as the act of validating that the model fulfills its intended objective, i.e., how successful our model is.

How does model validation work?

Any machine learning model’s ultimate goal is to learn from examples in such a way that the model can apply what it’s learned to new situations it hasn’t encountered before. As a result, finding the correct machine learning technique to develop our model is critical when approaching a problem with a dataset in hand. Every model has its own set of advantages and disadvantages. Some algorithms, for example, are better with little datasets, while others are better with enormous amounts of data. As a result, two distinct models utilizing comparable data can predict different outcomes with varying degrees of accuracy, necessitating model validation. The following is a timeline for data validation:

• Make a decision on a machine learning algorithm.
• Choose the hyperparameters of the model.
• Match the training data to the model.
• Predict labels for new data using the model.

The model’s accuracy score is then determined, and if it is poor, the value of the hyperparameters used in the model is changed, and the model is retested until it has a respectable accuracy score. There are several methods for verifying a model, the two most well-known of which are cross-validation and Bootstrapping, although no single validation approach is suitable for all cases. As a result, it’s critical to know what kind of data we’re dealing with.

How is model validation crucial?

Now that we’ve seen model validation in action, we can all see how critical it is to the entire model development process. It is critical to validate the machine learning model’s outputs in order to assure their accuracy. When a machine learning model is created, a large amount of training data is used, and the primary goal of model validation allows machine learning engineers to increase the data quality and quantity. It turns out that relying on a model’s forecast without first evaluating and validating it is a bad idea. In sensitive sectors such as healthcare and self-driving automobiles, any error in object detection can result in severe fatalities due to the machine’s incorrect judgements in real-life forecasts. Validating the machine learning model during the training and development stages also aids in the model’s ability to produce accurate predictions. The following are some additional benefits of data validation:

• Flexibility and scalability
• Reduce your expenditures.
• Improve the model’s quality.
• Much more mistakes are being explored.
• Prevents the model from becoming underfitting and overfitting.

Data scientists must ensure that machine learning models under training are accurate and stable, as the model must be able to pick up on the majority of the trends and patterns in the data without introducing too much noise. Now that we understand that establishing a machine learning model is not enough to rely on its predictions, we must also check and validate its accuracy in order to assure the precision of the model’s outputs and make it usable in real-world applications.

Model validation service from TagOn – A scaling-up solution for AI data labeling

TagOn – Model Validation services

Both data validation, in particular, and data labeling, in general, is extremely crucial to any AI project’s whole success. Therefore, it is necessary for you to choose the service provider at the highest level of quality and prestige. Among the largest data validation service providers in Vietnam, TagOn is absolutely outstanding because of extensive experience in implementing many data validation projects for AI SMEs and vendors in Vietnam. With TagOn, you will have reasonable costs when scaling up, stable dataset quality at scale, and an amazing time-booster when scaling up. TagOn’s data validation services include:

Identify model failures

Discover your model output failures properly without data volume limitations, coordinate annotators’ reviews using TagOn error metrics.

Correct the outputs of machine learning of any volumes

By pixel-perfectly correcting erroneous model predictions, you may reduce the likelihood of your large-scale machine learning outputs failing.

For a healthy expansion, gain insights into machine learning performance through visible analysis

Track model performance and ensure healthy organizational growth, collect tailored model insights and display them with visible analysis. For more advice, please contact us at the following information:

Contact information:

Website: https://tagon.ai/en Linkedin: https://www.linkedin.com/company/tagon-data-labeling Facebook: https://www.facebook.com/TagOnAi/ Phone number: +84 2466 603 178 Email: contact@tagon.ai / linh.le@tagon.ai