From the course: Data-Centric AI: Best Practices, Responsible AI, and More
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End-to-end workflow of data-centric AI
From the course: Data-Centric AI: Best Practices, Responsible AI, and More
End-to-end workflow of data-centric AI
- We've just explored why taking a data-centric approach is so critical for building reliable, fair, and impactful AI systems. Focusing on the data first enables more robust models and protects against pitfalls like bias and concept drift. Now that we understand these motivations for prioritizing data, I want to equip you with concrete skills to put data-centric AI into practice. A key enabler for operationalizing data-centric principles is MLOps, a set of practices for efficiently taking models into production and managing them through their lifecycle. So in the next section, we'll provide foundational backgrounds on MLOps, what it entails, why it becomes essential for real-world AI, and how it sets the stage for responsible data-driven systems. Understanding MLOps will give you the processes and toolkits needed to properly implement data-centric AI. With both the motivations and mechanisms covered, you'll be set up for…
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