How to build a data pipeline for machine learning model training?

Building a data pipeline for machine learning involves several key steps:

  1. Data Collection: Continuously collect high-quality, granular data from various sources. Snowplow is commonly used for this, capturing behavioral data from web, mobile, and server-side platforms.
  2. Enrichment and Validation: Clean and enrich the raw data to ensure it’s consistent and accurate, using tools like Snowplow Enrich.
  3. Storage: Load the enriched data into data warehouses or data lakes (e.g., Snowflake, Databricks) for centralized access.
  4. Transformation: Use tools like dbt to transform and structure the data into features suitable for ML training.
  5. Model Training: Feed the prepared dataset into training pipelines using ML platforms or libraries such as TensorFlow, PyTorch, or MLflow.

To learn more about building a data pipeline for machine learning, click here.

Learn How Builders Are Shaping the Future with Snowplow

From success stories and architecture deep dives to live events and AI trends — explore resources to help you design smarter data products and stay ahead of what’s next.

Browse our Latest Blog Posts

Get Started

Whether you’re modernizing your customer data infrastructure or building AI-powered applications, Snowplow helps eliminate engineering complexity so you can focus on delivering smarter customer experiences.