### Briefly describe what ML framework does this handler integrate to MindsDB, and how? 
This handler integrates with MindsDB using FLAML, an open-source machine learning framework developed by Microsoft for research purposes. FLAML is a powerful Python library that automates the process of selecting models, hyperparameters, and other choices in machine learning applications. By utilizing FLAML, this handler can take advantage of its efficient and effective automation capabilities. Although there is more work to be done in implementing FLAML, it is a useful tool for automating machine learning tasks. Microsoft is currently developing new features for automated machine learning, which are in beta stage and are planned to be integrated into MindsDB in the near future.

### Why is this integration useful? What does the ideal predictive use case for this integration look like? When would you definitely not use this integration? 
This integration is useful for automating machine learning tasks and improving predictive accuracy. An ideal use case is when there is a large dataset with many variables to consider. However, it may not be suitable for situations where there is limited data or when the model needs to be highly customized.
_NOTE: As of now, FLAML has a requirement that all input columns must be specified. If data is missing for any of the columns, an error will be triggered._
   

### Are models created with this integration fast and scalable, in general?
Yes, models created with the MindsDB-FLAML integration are generally fast and scalable. FLAML is designed to efficiently automate the machine learning process, including the selection of models and hyperparameters, resulting in faster training times and better performance. 

### What are the recommended system specifications for models created with this framework?
N/A

### To what degree can users control the underlying framework by passing parameters via the USING syntax?
The following FLAML parameters can be controlled by the user via USING syntax:
* metric: allows users to specify the metric used to measure the performance of the model.
* time_budget: sets the maximum amount of time allowed for training the model.
* n_jobs: controls the number of threads used for training, with higher numbers resulting in faster training.
* max_iter: sets the maximum number of times the model will be trained.
* n_splits: sets the number of splits to be used in cross-validation.
* eval_method: determines the type of resampling strategy to be used, with options for 'auto', 'cv', and 'holdout'.
* split_ratio: sets the percentage of data to be used as the validation data in holdout resampling strategy.


### Does this integration offer model explainability or insights via the DESCRIBE syntax?
Not Supported Now.

### Does this integration support fine-tuning pre-existing models (i.e. is the update() method implemented)? Are there any caveats?
No

### Are there any other noteworthy aspects to this handler?
The FLAML ML handler offers support for multiple machine learning models, including but not limited to XGBoost, LightGBM, and CatBoost. Note that while some libraries are installed by default alongside FLAML, the user has to verify that their model of choosing has been correctly installed before using this engine. For more details and installation options, check out FLAML documentation and their setup.py script.

The handler provides features such as automatic hyperparameter tuning, model selection, and feature selection.

### Any directions for future work in subsequent versions of the handler?
There are several directions for future work in subsequent versions of the handler, such as:

Adding support for more ML tasks: The current version of the handler only supports FLAML for automated machine learning. Future versions could potentially add support for **Time Series**, **NLP**, **AutoGen** and [more](https://microsoft.github.io/FLAML/docs/Examples/).




### Please provide a minimal SQL example that uses this ML engine (pointers to integration tests in the PR also valid)
[Example](./Manual_QA.md)


