Model prototyping is the phase in machine learning model development lifecycle where data scientists iterate towards building best performing models through data loading, cleansing, preparation, feature engineering, model training, tuning and scoring so that it can be used in production environment to meet a business need. On the data side, this experimental and iterative phase is where data scientists gather all the domain knowledge from SMEs, explore the univariate data distributions and relationships between features and possible target labels, and establish relationships among multiple features. On the model side, data scientists explore different modelling options based upon the identified business use case, as well as requirements for interpretability and metrics for evaluating the performance of the models.
The various decisions made during model prototyping contribute to the end performance of AI applications. Further optimizing and automating the model prototyping experience (as needed) for rapid iteration enables data scientists to become efficient in terms of time taken, infrastructural resources used and number of experiments required to iterate on, thereby accelerating the entire AI application development lifecycle.
C3 provides a no–code, low–code and full–code experience to enable different types of end users to rapidly prototype and develop high performant machine learning models. C3 AI Suite provides ML Studio and Ex Machina for no–code users, Jupyter notebook for low–code users, and AutoML supported experiments for data scientists. Further C3 provides full–code users the ability to develop custom Types from scratch (through VS Code) that can be used both for model prototyping and application development.
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