From the course: MLOps and Data Pipeline Orchestration for AI Systems
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Data orchestration pipeline best practices and dos and don'ts
From the course: MLOps and Data Pipeline Orchestration for AI Systems
Data orchestration pipeline best practices and dos and don'ts
- [Instructor] In this movie, we'll discuss some of the challenges that you'll encounter with data orchestration pipelines and some dos and don'ts for working with pipelines. Handling failures in distributed environments is complex and error prone. LLM pipelines often span multiple services, GPUs and nodes, making it hard to detect, diagnose, and recover from failures. You need a robust fault tolerance and logging. Otherwise, issues can silently corrupt outputs or halt systems. As LLM applications scale, they accumulate numerous scripts, prompts, data paths, and services leading to tangle dependencies. This sprawl increases maintenance overhead and makes debugging or refactoring riskier over time. Maintaining the right balance is also challenging. Teams want to experiment with new prompts, fine tuning methods or tools, but if you give too much flexibility, that can lead to inconsistent practices and fragile systems. Your orchestration pipeline needs to strike a balance, enabling…
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