Las mejores herramientas para gestionar la calidad de los datos en proyectos de IA

I think collaboration between data engineers and data scientists is crucial for good quality data.

Sometimes I feel like too many tools just add complexity rather than simplify things.

How do you handle data quality when dealing with real-time streaming data?

Any recommendations for tools that work well in cloud environments like AWS or GCP?

One tool I recently heard about is TFDV (TensorFlow Data Validation). Anyone tried that?

I find that sometimes the biggest issues come from poor data collection rather than the cleaning phase.

Honestly, I prefer open-source stuff like Deequ. Works well with big data and Spark, which is my daily grind.

I've been using Great Expectations for a while now, it's pretty solid for monitoring data quality and setting up tests.

Has anyone tried commercial options like Talend or Informatica for AI data quality?

You can also check ai-u.com for new or trending tools related to AI data quality, they've got some cool lists!

Data quality tools are great but sometimes simple manual checks with pandas or SQL queries catch a lot as well.

Would be great if more tools had better visualization for data quality metrics.

I think the key is to automate as much quality checks as possible, otherwise it becomes a nightmare.

I usually combine data profiling tools with quality checks to get a better sense of data issues.

Thanks for all the ideas guys, really helping me get a better handle on this stuff!

Would love to hear if anyone has experience integrating these tools with ML ops pipelines.

Don't forget data lineage tracking, it helps a lot in understanding where bad data is coming from.

Data quality rules sometimes fail when data schema changes unexpectedly, how do you handle that?

Is there any tool that can automatically suggest fixes for detected data quality issues?