Update README.md

added blog link to Taxi notebook

README.md

@@ -62,7 +62,7 @@ The `/data` folder is also symlinked into `/rapids/notebooks/extended/data` so y
 | examples   | [rf_demo](intermediate_notebooks/examples/rf_demo.ipynb)     | Demonstration of using both cuml and sklearn to train a RandomForestClassifier on the Higgs dataset.   |
 | E2E-> mortgage      | [mortgage_e2e](intermediate_notebooks/E2E/mortgage/mortgage_e2e.ipynb)            | This is an end to end notebook consisting of `ETL`, `data conversion` and `machine learning for training` operations performed on the mortgage dataset.      |
 | E2E-> mortgage      | [mortgage_e2e_deep_learning](intermediate_notebooks/E2E/mortgage/mortgage_e2e_deep_learning.ipynb)     | This notebook combines the RAPIDS GPU data processing with a PyTorch deep learning neural network to predict mortgage loan delinquency.                                                                                                                          |
-| E2E-> taxi      | [NYCTaxi](intermediate_notebooks/E2E/taxi/NYCTaxi-E2E.ipynb)     | Demonstrates multi-node ETL for cleanup of raw data into cleaned train and test dataframes. Shows how to run multi-node XGBoost training with dask-xgboost |
+| E2E-> taxi      | [NYCTaxi](intermediate_notebooks/E2E/taxi/NYCTaxi-E2E.ipynb)     | Demonstrates multi-node ETL for cleanup of raw data into cleaned train and test dataframes. Shows how to run multi-node XGBoost training with dask-xgboost.  [Blog](https://medium.com/rapids-ai/scale-out-rapids-on-google-cloud-dataproc-8a873233258f) |
 | E2E-> synthetic_3D      | [rapids_ml_workflow_demo](intermediate_notebooks/E2E/synthetic_3D/rapids_ml_workflow_demo.ipynb) | A 3D visual showcase of a machine learning workflow with RAPIDS (load data, transform/normalize, train XGBoost model, evaluate accuracy, use model for inference). Along the way we compare the performance gains of RAPIDS [GPU] vs sklearn/pandas methods [CPU].   |
 | E2E-> census      | [census_education2income_demo](intermediate_notebooks/E2E/census/census_education2income_demo.ipynb)     | In this notebook we use 50 years of census data to see how education affects income.  |
 | E2E-> gdelt    | [Ridge_regression_with_feature_encoding](intermediate_notebooks/E2E/gdelt/Ridge_regression_with_feature_encoding.ipynb)    | An end to end example using ridge regression on the gdelt dataset. Includes ETL with `cuDF`, feature scaling/encoding, and model training and evaluation with `cuML` |