5 Best Practices AI Engineers Should Learn From Data Engineering

AI engineering is built on the same fundamental principles as data engineering, with a focus on scalability, reliability, and performance. AI systems rely on high-quality data to build intelligent models, making data quality crucial for effective models. To be successful, AI engineers must adopt data engineering best practices, such as ensuring pipelines are idempotent and repeatable, using scheduling to automate pipeline runs, making pipelines observable, using flexible tools and languages for data ingestion and processing, and testing pipelines across environments before production. The modern orchestration platform Dagster enables AI teams to follow these best practices, providing features like asset-based APIs, declarative automation, observability, and integrations with other environments. By applying these principles, AI engineers can build high-quality, scalable, and reliable AI systems that succeed in real-world scenarios.

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