Successful Machine Learning projects is not only about ML code but also mandates for a standardized process where models are automatically logged and trained with proper versioning. Typically ML projects kickstart with business team setting the goal. Once the goal is set, data engineers develop the data pipeline by collecting & processing data from different sources like Data Warehouse, Data Lake, Traditional SQL databases, etc.,

For Data Scientists, developing a Machine Learning model is not a finite step, but rather an extensive process experimenting models with different datasets, hyperparameters, ml libaries making it hard to track and log the experiments manually. Many ML projects are stalled due to the lack of standardized toolsets to automatically train the models. It is not only enough to automate model training but the datasets should also be logged automatically as it becomes the decisive factor in comparing and choosing the right model version for deployment.

Model versioning along with automatic logging of datasets becomes crucial to compare and select the optimal model for deployment. AIQ Workbench automates model training where models and datasets used in experiments are logged and versioned.

Below is the detailed overview of how to automatically train and log datasets using our AIQ Workbench

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