Today marks an important milestone in the journey of building a robust, automated, and scalable Machine Learning (ML) pipeline. The goal for Day 7 is to build and containerize an end-to-end ML pipeline, encompassing the core phases: data preprocessing, model training, and model evaluation. This post will walk through the entire process, including the steps involved, best practices, and lessons learned along the way.
The pipeline consists of the following key steps:
- Data Preprocessing: The first step involves data cleaning, feature extraction, and transformation, ensuring the data is in a suitable format for training a model.
- Model Training: In this step, a machine learning model is trained using the preprocessed data. This includes selecting an algorithm, fitting the model to the training data, and saving the trained model.
- Model Evaluation: After training, the model’s performance is evaluated using a validation set or test data. Metrics like accuracy, precision, recall, and F1-score are commonly used for classification tasks.
- Containerization: Once the pipeline works smoothly, it’s containerized using Docker to ensure portability and…