A machine learning pipeline is a series of steps that are applied to data in order to train and deploy a machine learning model. It ensures that the model is accurate and consistent over time.

Here is a high-level overview of the typical steps involved in a machine learning pipeline:

  • Data Collection: Gather the data you will use to train your model.
  • Data Cleaning: Clean the data to remove noise and inconsistencies.
  • Feature Engineering: Create new features from the existing data.
  • Model Selection: Choose a model that is appropriate for your data and problem.
  • Training: Train the model using the collected data.
  • Evaluation: Evaluate the model's performance.
  • Deployment: Deploy the model to a production environment.
  • Monitoring: Monitor the model's performance over time and retrain it as necessary.

For more information on machine learning pipelines, you can visit our Machine Learning Basics course.

Common Challenges in Machine Learning Pipelines

  • Data Quality: Poor data quality can lead to inaccurate models.
  • Model Selection: Choosing the right model can be challenging.
  • Overfitting: The model may perform well on training data but poorly on new data.
  • Underfitting: The model may not be complex enough to capture the underlying patterns in the data.

Data Science Pipeline

To learn more about the different components of a data science pipeline, check out our Data Science Workflow course.