Data Engineering Best Practices for AI

On Demand Replay

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Data engineering is the backbone of AI systems. After all, the success of AI models heavily depends on the volume, structure, and quality of the data that they rely upon to produce results. With proper tools and practices in place, data engineering can address a number of common challenges that organizations face in deploying and scaling effective AI usage.

Learn how to:

Quickly integrate data from multiple sources across different environments
Build scalable and efficient data pipelines that can handle large, complex workloads
Ensure that high-quality, relevant data is fed into AI systems
Enhance the performance of AI models with optimized and meaningful input data
Maintain robust data governance, compliance, and security measures
Support real-time AI applications

Meet the Speaker

Join the session for live Q&A with the speaker

Tim Spann
Principal Developer Advocate
Tim Spann is a Principal Developer Advocate at Zilliz.