AI ML Pipeline Dagstart vs Apache Airflow
image generated by meta.ai Let's comapare Dagster and Apache Airflow, for an AI/ML pipeline , we'll design a simple pipeline that includes data loading, data preprocessing, model training, and model evaluation. Here's a breakdown of the common steps in an AI/ML pipeline and how they can be implemented in both Dagster and Airflow. AI/ML Pipeline Steps: Data Loading: Load raw data from a source (e.g., CSV, database). Data Preprocessing: Clean, transform, and prepare the data for model training (e.g., handle missing values, feature scaling). Model Training: Train a machine learning model on the preprocessed data. Model Evaluation: Evaluate the trained model's performance. Let's assume we're working with a simple scikit-learn example, like training a Logistic Regression model on the Iris dataset...