├── .flake8 ├── .gitignore ├── LICENSE ├── README.md ├── beatles-automl └── GCP │ ├── EDA │ ├── EDA.ipynb │ └── img │ │ ├── panda_example.png │ │ ├── panda_example2.png │ │ ├── panda_profile.png │ │ └── sweetviz.png │ ├── README.md │ ├── Tutorial │ ├── Tutorial.html │ └── images │ │ ├── image1.png │ │ ├── image10.gif │ │ ├── image11.png │ │ ├── image12.png │ │ ├── image13.png │ │ ├── image14.png │ │ ├── image15.png │ │ ├── image16.png │ │ ├── image17.png │ │ ├── image18.png │ │ ├── image2.png │ │ ├── image3.png │ │ ├── image4.png │ │ ├── image5.png │ │ ├── image6.png │ │ ├── image7.png │ │ ├── image8.png │ │ └── image9.png │ └── answers │ ├── Data from BiqQuery.ipynb │ ├── Enrich_top_300.ipynb │ ├── artist_df.feather │ ├── bands_tags.csv │ ├── create_training_data.ipynb │ ├── file_out_2495.csv │ ├── lastfm.conf │ ├── listen_top_300.ipynb │ ├── play_counts_df.feather │ ├── predict.ipynb │ └── test_record.txt ├── black-friday ├── EDA │ ├── Data │ │ └── train.csv │ └── EDA.ipynb ├── README.md ├── answers │ ├── AI Platform.ipynb │ ├── LICENSE │ ├── generate_sample.py │ ├── predictor.py │ ├── requirements.txt │ ├── setup.py │ ├── test.csv │ ├── train.csv │ └── trainer │ │ ├── __init__.py │ │ ├── create_data_func.py │ │ ├── hp_tuning.py │ │ └── rf_trainer.py ├── exercises │ ├── AI Platform.ipynb │ └── README.md └── notebooks │ └── README.md ├── cellular image ├── EDA │ └── Cellular_Image_EDA.ipynb ├── README.md ├── answer │ ├── Cellular_Image.ipynb │ └── trainer │ │ ├── __init__.py │ │ └── trainer.py └── exercises │ ├── Cellular_Image_Exercise.ipynb │ └── README.md ├── census-income ├── GCP │ ├── EDA │ │ └── Census_Income_EDA.ipynb │ ├── answers │ │ ├── AutoML_batch_online_prediction_explain.ipynb │ │ ├── Census_Income.ipynb │ │ ├── hptuning │ │ │ ├── Dockerfile │ │ │ ├── hptuning.ipynb │ │ │ └── trainer │ │ │ │ ├── __init__.py │ │ │ │ └── task.py │ │ └── local_training.ipynb │ ├── exercises │ │ ├── AutoML_batch_online_prediction_explain.ipynb │ │ ├── hptuning │ │ │ ├── Dockerfile │ │ │ ├── hptuning.ipynb │ │ │ └── trainer │ │ │ │ ├── __init__.py │ │ │ │ └── task.py │ │ └── local_training.ipynb │ └── visualize │ │ └── What_If_Tool.ipynb └── README.md ├── chicago-taxi ├── EDA │ └── EDA.ipynb ├── README.md ├── answers │ ├── AI Platform.ipynb │ ├── Chicago Taxi ML Specialization.pdf │ ├── LICENSE │ ├── README.md │ ├── calc_mape.py │ ├── create_sample.py │ ├── hptuning_config.yaml │ ├── predictor.py │ ├── requirements.txt │ ├── setup.py │ └── trainer │ │ ├── __init__.py │ │ ├── create_data_func.py │ │ ├── create_scaler_func.py │ │ ├── model.py │ │ └── task.py ├── exercise │ ├── Instructions_Chicago_Taxi_EDA.ipynb │ └── bray │ │ └── BR-AI Platform.ipynb └── notebooks │ ├── BR_taxi_EDA.ipynb │ └── README.md ├── hp_tuning ├── answers │ ├── hp_tuning.ipynb │ └── trainer │ │ ├── __init__.py │ │ ├── hptuning_config_tensorflow.yaml │ │ ├── hptuning_config_xgboost.yaml │ │ ├── model.py │ │ ├── task_tensorflow.py │ │ └── task_xgboost.py └── exercises │ ├── hp_tuning.ipynb │ └── trainer │ ├── __init__.py │ ├── hptuning_config_tensorflow.yaml │ ├── hptuning_config_xgboost.yaml │ ├── model.py │ ├── task_tensorflow.py │ └── task_xgboost.py ├── iot-condition-monitoring ├── answers │ └── bearing_condition_monitoring.ipynb ├── data │ └── N09_M07_F10_K001_1.mat └── readme.md ├── lending-club ├── GCP │ ├── EDA │ │ └── Lending_Club_EDA.ipynb │ ├── answers │ │ ├── Lending_Club.ipynb │ │ ├── setup.py │ │ └── trainer │ │ │ ├── __init__.py │ │ │ ├── hptuning_config.yaml │ │ │ └── task.py │ └── exercises │ │ ├── Lending_Club_Exercise.ipynb │ │ └── README.md └── README.md ├── nasa-iot-demo ├── README.md ├── base_image │ └── Dockerfile ├── dd_image │ ├── Dockerfile │ └── dd.py ├── explore-data-describe.ipynb ├── explore-load-data.ipynb ├── explore-train-test-split.ipynb ├── explore-train.ipynb ├── kfp_component │ ├── __init__.py │ └── func_components.py ├── load_raw_data.yaml ├── nasa_iot_training.json ├── pipeline-notebook-vertex.ipynb ├── requirements.txt ├── split_data.yaml ├── train_image │ ├── Dockerfile │ └── train.py └── vertex_custom_job.yaml └── pitch-predictor ├── README.md ├── answers ├── Run Pipelines.ipynb ├── components │ ├── collectStats │ │ ├── Dockerfile │ │ ├── ParDoFns │ │ │ ├── __init__.py │ │ │ ├── collectGames.py │ │ │ ├── collectPitchers.py │ │ │ ├── collectStats.py │ │ │ └── gameday.py │ │ ├── cloudbuild.yaml │ │ ├── collect_stats_dataflow.py │ │ ├── requirements.txt │ │ └── setup.py │ ├── enhanceFeatures │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ └── enhance_features.py │ ├── evaluateModel │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ ├── evaluate_model.py │ │ └── requirements.txt │ ├── featureEng │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ ├── feature_eng.py │ │ └── requirements.txt │ ├── findThreshold │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ ├── find_threshold.py │ │ └── requirements.txt │ ├── hostRF │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ └── host_rf.py │ ├── hostSeldon │ │ ├── Dockerfile │ │ ├── MyModel.py │ │ ├── cloudbuild.yaml │ │ ├── requirements.txt │ │ └── rf-mlb.yaml │ ├── hostXGBoost │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ ├── host_xgboost.py │ │ └── requirements.txt │ ├── splitTrainTestVal │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ ├── requirements.txt │ │ └── split_train_test_val.py │ ├── trainRF │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ └── train_rf.py │ ├── trainXGBoost │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ ├── requirements.txt │ │ └── train_xgboost.py │ └── tuneHP │ │ ├── Dockerfile │ │ ├── cloudbuild.yaml │ │ ├── requirements.txt │ │ └── tune_hp.py ├── pipeline-enhance.png ├── pipelines │ ├── baseball-pipeline-branch.py │ ├── baseball-pipeline-branch.py.zip │ ├── baseball-pipeline-enhance.py │ ├── baseball-pipeline-enhance.py.zip │ ├── baseball-pipeline-seldon.py.zip │ ├── baseball-pipeline-single.py │ └── baseball-pipeline-single.py.zip ├── pred_utils │ ├── __init__.py │ ├── collectPitch.py │ ├── collectSample.py │ 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