- Data Collection and Annotation
- Getting data from various sources
- Annotating/ Labelling it correctly
- Data Analysis
- Data Exploration and Visualization for identifying missing values and all
- Feature Engineering:
- Dealing with the identified missing values and preprocesing
- Feature Selection
- Determining the right features of the data needed for the learning algorithm to perform well
- Model Development
- developing several machine learning and deep learning architectures
- Evaluation of the model accuracy using the right evaluation metrics.
- Deployment : Using the model in production environment such as
- Deploying on a Mobile Applications: model compression to smaller footprint before usage
- Deploying on Web Applications
- Deploying on embedded devices