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How an annotation workflow can help tracking a model accuracy performance in data labeling?

September 5, 2022

What is an annotation workflow?

Annotation workflow is the automated multistep approach to data annotation. This is done by the breaking down of  annotation projects into smaller and easier tasks, and in addition customizing job designs.

 What is data labeling?

Data labeling is a process in machine learning where raw data is detected and tagged with informative and meaningful labels within a context. This is to enable the training model to learn from it. Examples of labelled data include videos, audio clips and images.

Types of data labeling

Programmatic labeling

With this form of labeling, labeling functions are created. labeling rationales are captured, applied to voluminous, unlabeled data and trained to auto-label large training sets. This approach needs no human efeffort.In addition, when there is a change in requirements. In addition, all training models can be traced to their specific and traceable functions. Any undesired model behavior is easily traced to its original labeling functions, which can either be removed or modified in a short period. 

Synthetic labeling

Synthetic labeling involves real data imitation data generation, through the use of a generative model which is trained and validated on an original dataset.

Outsourcing

With this method, third parties are contracted to do the work. The tasks may include software development, and network services. Many IT companies have resorted to this method of data labeling to save time and cost.

Crowdsourcing

Crowdsourcing usually involves online platforms which  break  down projects into smaller tasks. They are then  assigned to multiple freelancers globally. Some tasks require specific skills, such as language translation and text transcription. Resources and tools including notes, tutorials and code samples among others are given to members of the platform to aid in the work. 

How an annotation workflow tracks accuracy performance

Cleaning data

Here data is analysed and the irrelevant or incorrect information is wiped out. It also applies to rectifying incorrect information and reducing duplication. In addition, poorly collected data sets lead to data representation lessening their decision-making powers, hence the need to clean.

Error analysis

It is the process where model predictions contradict ground-truth labels. This can be attributed to poor model prediction or labeling mistake (where the ground truth is wrong)

Small datasets

Small amounts of data are introduced to the training model. It serves as a reference for interpreting new dataset. It requires a small amount of data therefore overloading it gives wrong results. It can also gather a supervisory signal from an available training model. Available data is then used to predict hidden data. That way, the entire process is independently built and supervised.

 Huge datasets

Formation that used to be available only offline (in hard copies) can now be converted to digital formats in a very cheap way. It includes digital libraries where are volumes of educational resources are carefully digitized for easy access anywhere. Included among such materials are maps. Others include image and video compressions.

Advantages of annotation workflow

- It helps to critically understand and detect data inputted by training models. 

- It also helps computer systems to process visual information and interpret within their unique contexts. This is owing to the fact that they are unable to do so by themselves.

- Annotation workflow makes projects scalable.This further allows training models to easily process the essentially needed attributes with ease.

- Keeping track of key ideas and questions.

- Helping formulate thoughts and questions for deeper understanding.

- Fostering analyzing and interpreting texts.

- Encouraging the reader to make inferences and draw conclusions about the text.

- Annotation workflow helps to rectify data with missing labels or which have been poorly tagged. 

Disadvantages of annotation workflow

The quality and accuracy of the data is very important. Models are trained to recognise the dataset patterns and variables. An oversight in data feeding will alter the final results negatively. 

A lot of data is needed to keep up with the annotation workflow. Depending on the goal of the machine learning process, training items may vary from thousands to even millions. 

It is reported from the McKinsey Global Institute, that 75% of data annotation projects need to refresh the training models every month. In addition,24% need daily refreshment to be daily refreshed.

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