Episode 11: XGBoost special

In this episode, I talk about XGBoost 1.0, a major milestone for this very popular algorithm. Then, I discuss the three options you have for running XGBoost on Amazon SageMaker: built-in algo, built-in framework, and bring your own container. Code included, of course!

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Additional resources mentioned in the podcast:
* XGBoost built-in algo: https://gitlab.com/juliensimon/ent321
* XGBoost built-in framework: https://gitlab.com/juliensimon/dlnotebooks/-/blob/master/sagemaker/09-XGBoost-script-mode.ipynb
* BYO with Scikit-learn: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb
* Deploying XGBoost with mlflow: https://youtu.be/jpZSp9O8_ew
* New model format: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
* Converting pickled models: https://github.com/dmlc/xgboost/blob/master/doc/python/convert_090to100.py

This podcast is also available in video at https://youtu.be/w0F4z0dMdzI.

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