Choosing the Right Data Annotation Process to Train Machine Learning Algorithms

Data annotation process involves from collection of data to labeling, quality check and validation that makes the raw data usable for machine learning training. For supervised machine learning projects, without labeled data, it is not possible to train the AI model.

1. Collecting Data

One of the key components for any machine learning project is to collect data in an efficient manner. If data is not collected in the right way, it will create a lot of issues for the people working on the project. The data must be accurate, clean and the use of the data must be, structured. Data can be used in many applications, however in AI projects, the data itself and the algorithms applied on it are the most important. For data preparation, the process is based on statistical learning method and the data is manually labeled using a labeled data set. Data is marked manually and put into the central collection, which then is a large collection of labeled data, which the AI algorithms can use.

2. Labeling Data

The key component of data annotation process is labeling the data. Labeling helps researchers determine the attribute values. For labeling, the data has to be classified into one of four categories: pre-labeled data contains only the attributes that have been explicitly labeled pre-labeled data contains only the attributes that have been explicitly labeled data also contains attributes that have been pre-labeled but are not labeled. In this case the task is identifying the attributes that have not been labeled but are either pre-labeled or labeled. (e.g., raw data contains pre-labeled values, not labeled data) A data annotation tool should provide tagging features for pre-labeled and labeled data.

3. Quality Checking Data

Now if we have labeled data then we can train the model. But, it is not enough to simply label the data to have AI model, there are several other checks in the system which also have to be performed. 1. Validation: A part of annotation process, the validation must be done. When the data collected and processed by the system is validated, then it is considered to be good enough to be used in production. 2. Quality check: The quality check involves searching for discrepancies in data, checking if the data is correct or not. 3. Formulation: This involves renaming the data that has been properly data annotated for training purpose to something else. 4. Acquisition: This comprises of human selection of data from data sets and then placing the data set for training purposes.

4. Validation Data

Data validation is required by machine learning techniques for training the system to learn from the datasets. For example, if you have been working with the R programming language, but you want to train the Deep Learning based system to a deeper level, you have to validate the result in the form of its ability to predict a synthetic outcome as a good as that of a previously labeled set of predictions. Validation Data plays a very crucial role in the process. 5. Data Leakage In data-enabled systems, data leaks are usually the main challenge that face. This can be mainly attributed to the fact that the idea of data-enabled systems is fundamentally different from traditional data structures and thus the traditional systems are unable to collect enough data to correctly classify objects.

5. Conclusion

This article describes key strategies for getting the most from machine learning. Key methods include building a data science team, using a structured data science approach and carefully deciding how to label and construct raw data. Machine learning relies on lots of data and lots of data analysis. Organizations that want to benefit from AI need to understand the type of data that they have and the steps required to effectively train a machine learning model.