Hi everyone,
I'm currently working on a project that involves building machine learning models on large datasets in Databricks. While Databricks offers great tools for handling big data, I'm facing some challenges when trying to integrate this data with ML models.
Some of the issues I've encountered include:
- Data Preprocessing: Handling large datasets for feature engineering and cleaning can be time-consuming and resource intensive.
- Model Training: Scaling machine learning algorithms to work efficiently with massive datasets often requires significant tuning and optimization.
- Performance: Balancing performance and accuracy while training models on distributed systems can be tricky.
- Data Quality: Ensuring the data is clean, complete, and consistent is crucial, but difficult at such a scale.
- Resource Management: Allocating enough compute resources for both data processing and model training without overspending is challenging.
Has anyone else faced similar issues when integrating Big Data with machine learning in Databricks? What strategies or tools have you found helpful in overcoming these challenges?
Looking forward to your insights!
Thanks!
