We are releasing UDAO - a next-generation unified data analytics optimizer.
Some known limitations:
1. Pandas DataFrame may have limitations when working with very large datasets.
2. Optimization algorithms require independent functions for each objective or constraint, impacting optimization speed, which may not match the speed achieved in our referenced papers (a fix is planned soon)
3. Categorical variables are always enumerated in MOGD.
4. Preprocessed data is not cached for reuse in hyper-parameter tuning
References:
- [Spark-based Cloud Data Analytics using Multi-Objective Optimization](https://ieeexplore.ieee.org/document/9458826/)
- [UDAO: a next-generation unified data analytics optimizer](https://dl.acm.org/doi/10.14778/3352063.3352103)