Data in Production, Part 1: Building for Production (Not Just for Your Laptop)

📰 Medium · Machine Learning

Learn how to build data systems for production, not just your laptop, and understand the key differences between development and production environments

intermediate Published 19 Apr 2026

Action Steps

Design your data pipeline with scalability in mind using tools like Apache Beam or AWS Glue
Test your data model on a small scale before deploying to production
Configure your data storage solution for high availability and reliability using services like Amazon S3 or Google Cloud Storage
Implement monitoring and logging for your data pipeline using tools like Prometheus or Grafana
Plan for data versioning and reproducibility using techniques like data snapshotting or version control

Who Needs to Know This

Data scientists, data engineers, and machine learning engineers will benefit from this article as it highlights the importance of considering production requirements when building data systems

Key Insight

💡 Building for production requires a different mindset than building for your laptop, with a focus on scalability, reliability, and maintainability