From the course: Data-Centric AI: Best Practices, Responsible AI, and More
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Optimizing the MLOps process: Development
From the course: Data-Centric AI: Best Practices, Responsible AI, and More
Optimizing the MLOps process: Development
- [Instructor] Robust data sourcing is crucial for quality machine learning pipelines. So let's start with some guiding principles for quality data sourcing. We need to start with clearly defining the data requirements upfront, including the schema, the format, the expected data volume, and data velocity. When you are working on a data or ML problem statement, it is essential to document in a data catalog to align with the stakeholder expectations and maintain transparency. Using standard well-documented APIs and protocols like REST, GRPC and Kafka to interface with sources helps ensure compatibility across systems. When building a data pipeline, you must integrate monitoring, alerting, retries, and error handling into the ingestion pipeline. This enables catching issues early and maintaining data integrity. During the process, automation is key for scalability and reliability, and it also minimizes any kind of manual…
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