Understanding the state and need of each of our ML DatasetsFollow instructions and complete each task respective to the current iteration.Carefully screen the data set to select the best images from the given data set.Using photo and video editing software for processing / applying transformations to themTagging(key-value pair that you define for an object) vehicle w.r.t the current iteration rules.Being able to estimate and deliver on time.

Ensuring the quality and accuracy of annotated data is crucial for trainingrobust machine learning model and responsible for performing qualitychecks on labelled data to maintain high standards. Analysing and understanding the data requirements for machine learningprojects. This could involve working closely with data scientists and otherteam members to comprehend the specific needs of the ML models beingdeveloped. Documenting the annotation guidelines and processes to ensure consistencyin data labelling. Clear and comprehensive documentation is essential formaintaining data quality and facilitating collaboration within the team. Working collaboratively with other team members, including data scientists,engineers, and project managers. Effective communication and collaborationare crucial for the success of machine learning projects. Using various annotation tools and software to perform data labelling tasksefficiently. Familiarity with tools designed for image annotation, textlabelling, or other data types. Being adaptable to changes in project requirements and staying updated onthe latest techniques and tools in the field of machine learning and dataannotation.