Building a data pipeline for machine learning involves several key steps:
- 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.
- Enrichment and Validation: Clean and enrich the raw data to ensure it’s consistent and accurate, using tools like Snowplow Enrich.
- Storage: Load the enriched data into data warehouses or data lakes (e.g., Snowflake, Databricks) for centralized access.
- Transformation: Use tools like dbt to transform and structure the data into features suitable for ML training.
- 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.
.avif)
.avif)
.avif)
