├── .gitignore ├── README.md ├── datasets ├── 256_sampledata │ ├── 196.spaghetti │ │ ├── 196_0001.jpg │ │ ├── 196_0002.jpg │ │ ├── 196_0003.jpg │ │ ├── 196_0004.jpg │ │ ├── 196_0005.jpg │ │ ├── 196_0006.jpg │ │ ├── 196_0007.jpg │ │ ├── 196_0008.jpg │ │ ├── 196_0009.jpg │ │ ├── 196_0010.jpg │ │ ├── 196_0011.jpg │ │ ├── 196_0012.jpg │ │ ├── 196_0013.jpg │ │ ├── 196_0014.jpg │ │ ├── 196_0015.jpg │ │ ├── 196_0016.jpg │ │ ├── 196_0017.jpg │ │ ├── 196_0018.jpg │ │ ├── 196_0019.jpg │ │ ├── 196_0020.jpg │ │ ├── 196_0021.jpg │ │ ├── 196_0022.jpg │ │ ├── 196_0023.jpg │ │ ├── 196_0024.jpg │ │ ├── 196_0025.jpg │ │ ├── 196_0026.jpg │ │ ├── 196_0027.jpg │ │ ├── 196_0028.jpg │ │ ├── 196_0029.jpg │ │ ├── 196_0030.jpg │ │ ├── 196_0031.jpg │ │ ├── 196_0032.jpg │ │ ├── 196_0033.jpg │ │ ├── 196_0034.jpg │ │ ├── 196_0035.jpg │ │ ├── 196_0036.jpg │ │ ├── 196_0037.jpg │ │ ├── 196_0038.jpg │ │ ├── 196_0039.jpg │ │ ├── 196_0040.jpg │ │ ├── 196_0041.jpg │ │ ├── 196_0042.jpg │ │ ├── 196_0043.jpg │ │ ├── 196_0044.jpg │ │ ├── 196_0045.jpg │ │ ├── 196_0046.jpg │ │ ├── 196_0047.jpg │ │ ├── 196_0048.jpg │ │ ├── 196_0049.jpg │ │ ├── 196_0050.jpg │ │ ├── 196_0051.jpg │ │ ├── 196_0052.jpg │ │ ├── 196_0053.jpg │ │ ├── 196_0054.jpg │ │ ├── 196_0055.jpg │ │ ├── 196_0056.jpg │ │ ├── 196_0057.jpg │ │ ├── 196_0058.jpg │ │ ├── 196_0059.jpg │ │ ├── 196_0060.jpg │ │ ├── 196_0061.jpg │ │ ├── 196_0062.jpg │ │ ├── 196_0063.jpg │ │ ├── 196_0064.jpg │ │ ├── 196_0065.jpg │ │ ├── 196_0066.jpg │ │ ├── 196_0067.jpg │ │ ├── 196_0068.jpg │ │ ├── 196_0069.jpg │ │ ├── 196_0070.jpg │ │ ├── 196_0071.jpg │ │ ├── 196_0072.jpg │ │ ├── 196_0073.jpg │ │ ├── 196_0074.jpg │ │ ├── 196_0075.jpg │ │ ├── 196_0076.jpg │ │ ├── 196_0077.jpg │ │ ├── 196_0078.jpg │ │ ├── 196_0079.jpg │ │ ├── 196_0080.jpg │ │ ├── 196_0081.jpg │ │ ├── 196_0082.jpg │ │ ├── 196_0083.jpg │ │ ├── 196_0084.jpg │ │ ├── 196_0085.jpg │ │ ├── 196_0086.jpg │ │ ├── 196_0087.jpg │ │ ├── 196_0088.jpg │ │ ├── 196_0089.jpg │ │ ├── 196_0090.jpg │ │ ├── 196_0091.jpg │ │ ├── 196_0092.jpg │ │ ├── 196_0093.jpg │ │ ├── 196_0094.jpg │ │ ├── 196_0095.jpg │ │ ├── 196_0096.jpg │ │ ├── 196_0097.jpg │ │ ├── 196_0098.jpg │ │ ├── 196_0099.jpg │ │ ├── 196_0100.jpg │ │ ├── 196_0101.jpg │ │ ├── 196_0102.jpg │ │ ├── 196_0103.jpg │ │ └── 196_0104.jpg │ ├── 212.teapot │ │ ├── 212_0001.jpg │ │ ├── 212_0002.jpg │ │ ├── 212_0003.jpg │ │ ├── 212_0004.jpg │ │ ├── 212_0005.jpg │ │ ├── 212_0006.jpg │ │ ├── 212_0007.jpg │ │ ├── 212_0008.jpg │ │ ├── 212_0009.jpg │ │ ├── 212_0010.jpg │ │ ├── 212_0011.jpg │ │ ├── 212_0012.jpg │ │ ├── 212_0013.jpg │ │ ├── 212_0014.jpg │ │ ├── 212_0015.jpg │ │ ├── 212_0016.jpg │ │ ├── 212_0017.jpg │ │ ├── 212_0018.jpg │ │ ├── 212_0019.jpg │ │ ├── 212_0020.jpg │ │ ├── 212_0021.jpg │ │ ├── 212_0022.jpg │ │ ├── 212_0023.jpg │ │ ├── 212_0024.jpg │ │ ├── 212_0025.jpg │ │ ├── 212_0026.jpg │ │ ├── 212_0027.jpg │ │ ├── 212_0028.jpg │ │ ├── 212_0029.jpg │ │ ├── 212_0030.jpg │ │ ├── 212_0031.jpg │ │ ├── 212_0032.jpg │ │ ├── 212_0033.jpg │ │ ├── 212_0034.jpg │ │ ├── 212_0035.jpg │ │ ├── 212_0036.jpg │ │ ├── 212_0037.jpg │ │ ├── 212_0038.jpg │ │ ├── 212_0039.jpg │ │ ├── 212_0040.jpg │ │ ├── 212_0041.jpg │ │ ├── 212_0042.jpg │ │ ├── 212_0043.jpg │ │ ├── 212_0044.jpg │ │ ├── 212_0045.jpg │ │ ├── 212_0046.jpg │ │ ├── 212_0047.jpg │ │ ├── 212_0048.jpg │ │ ├── 212_0049.jpg │ │ ├── 212_0050.jpg │ │ ├── 212_0051.jpg │ │ ├── 212_0052.jpg │ │ ├── 212_0053.jpg │ │ ├── 212_0054.jpg │ │ ├── 212_0055.jpg │ │ ├── 212_0056.jpg │ │ ├── 212_0057.jpg │ │ ├── 212_0058.jpg │ │ ├── 212_0059.jpg │ │ ├── 212_0060.jpg │ │ ├── 212_0061.jpg │ │ ├── 212_0062.jpg │ │ ├── 212_0063.jpg │ │ ├── 212_0064.jpg │ │ ├── 212_0065.jpg │ │ ├── 212_0066.jpg │ │ ├── 212_0067.jpg │ │ ├── 212_0068.jpg │ │ ├── 212_0069.jpg │ │ ├── 212_0070.jpg │ │ ├── 212_0071.jpg │ │ ├── 212_0072.jpg │ │ ├── 212_0073.jpg │ │ ├── 212_0074.jpg │ │ ├── 212_0075.jpg │ │ ├── 212_0076.jpg │ │ ├── 212_0077.jpg │ │ ├── 212_0078.jpg │ │ ├── 212_0079.jpg │ │ ├── 212_0080.jpg │ │ ├── 212_0081.jpg │ │ ├── 212_0082.jpg │ │ ├── 212_0083.jpg │ │ ├── 212_0084.jpg │ │ ├── 212_0085.jpg │ │ ├── 212_0086.jpg │ │ ├── 212_0087.jpg │ │ ├── 212_0088.jpg │ │ ├── 212_0089.jpg │ │ ├── 212_0090.jpg │ │ ├── 212_0091.jpg │ │ ├── 212_0092.jpg │ │ ├── 212_0093.jpg │ │ ├── 212_0094.jpg │ │ ├── 212_0095.jpg │ │ ├── 212_0096.jpg │ │ ├── 212_0097.jpg │ │ ├── 212_0098.jpg │ │ ├── 212_0099.jpg │ │ ├── 212_0100.jpg │ │ ├── 212_0101.jpg │ │ ├── 212_0102.jpg │ │ ├── 212_0103.jpg │ │ ├── 212_0104.jpg │ │ ├── 212_0105.jpg │ │ ├── 212_0106.jpg │ │ ├── 212_0107.jpg │ │ ├── 212_0108.jpg │ │ ├── 212_0109.jpg │ │ ├── 212_0110.jpg │ │ ├── 212_0111.jpg │ │ ├── 212_0112.jpg │ │ ├── 212_0113.jpg │ │ ├── 212_0114.jpg │ │ ├── 212_0115.jpg │ │ ├── 212_0116.jpg │ │ ├── 212_0117.jpg │ │ ├── 212_0118.jpg │ │ ├── 212_0119.jpg │ │ ├── 212_0120.jpg │ │ ├── 212_0121.jpg │ │ ├── 212_0122.jpg │ │ ├── 212_0123.jpg │ │ ├── 212_0124.jpg │ │ ├── 212_0125.jpg │ │ ├── 212_0126.jpg │ │ ├── 212_0127.jpg │ │ ├── 212_0128.jpg │ │ ├── 212_0129.jpg │ │ ├── 212_0130.jpg │ │ ├── 212_0131.jpg │ │ ├── 212_0132.jpg │ │ ├── 212_0133.jpg │ │ ├── 212_0134.jpg │ │ ├── 212_0135.jpg │ │ └── 212_0136.jpg │ ├── 234.tweezer │ │ ├── 234_0001.jpg │ │ ├── 234_0002.jpg │ │ ├── 234_0003.jpg │ │ ├── 234_0004.jpg │ │ ├── 234_0005.jpg │ │ ├── 234_0006.jpg │ │ ├── 234_0007.jpg │ │ ├── 234_0008.jpg │ │ ├── 234_0009.jpg │ │ ├── 234_0010.jpg │ │ ├── 234_0011.jpg │ │ ├── 234_0012.jpg │ │ ├── 234_0013.jpg │ │ ├── 234_0014.jpg │ │ ├── 234_0015.jpg │ │ ├── 234_0016.jpg │ │ ├── 234_0017.jpg │ │ ├── 234_0018.jpg │ │ ├── 234_0019.jpg │ │ ├── 234_0020.jpg │ │ ├── 234_0021.jpg │ │ ├── 234_0022.jpg │ │ ├── 234_0023.jpg │ │ ├── 234_0024.jpg │ │ ├── 234_0025.jpg │ │ ├── 234_0026.jpg │ │ ├── 234_0027.jpg │ │ ├── 234_0028.jpg │ │ ├── 234_0029.jpg │ │ ├── 234_0030.jpg │ │ ├── 234_0031.jpg │ │ ├── 234_0032.jpg │ │ ├── 234_0033.jpg │ │ ├── 234_0034.jpg │ │ ├── 234_0035.jpg │ │ ├── 234_0036.jpg │ │ ├── 234_0037.jpg │ │ ├── 234_0038.jpg │ │ ├── 234_0039.jpg │ │ ├── 234_0040.jpg │ │ ├── 234_0041.jpg │ │ ├── 234_0042.jpg │ │ ├── 234_0043.jpg │ │ ├── 234_0044.jpg │ │ ├── 234_0045.jpg │ │ ├── 234_0046.jpg │ │ ├── 234_0047.jpg │ │ ├── 234_0048.jpg │ │ ├── 234_0049.jpg │ │ ├── 234_0050.jpg │ │ ├── 234_0051.jpg │ │ ├── 234_0052.jpg │ │ ├── 234_0053.jpg │ │ ├── 234_0054.jpg │ │ ├── 234_0055.jpg │ │ ├── 234_0056.jpg │ │ ├── 234_0057.jpg │ │ ├── 234_0058.jpg │ │ ├── 234_0059.jpg │ │ ├── 234_0060.jpg │ │ ├── 234_0061.jpg │ │ ├── 234_0062.jpg │ │ ├── 234_0063.jpg │ │ ├── 234_0064.jpg │ │ ├── 234_0065.jpg │ │ ├── 234_0066.jpg │ │ ├── 234_0067.jpg │ │ ├── 234_0068.jpg │ │ ├── 234_0069.jpg │ │ ├── 234_0070.jpg │ │ ├── 234_0071.jpg │ │ ├── 234_0072.jpg │ │ ├── 234_0073.jpg │ │ ├── 234_0074.jpg │ │ ├── 234_0075.jpg │ │ ├── 234_0076.jpg │ │ ├── 234_0077.jpg │ │ ├── 234_0078.jpg │ │ ├── 234_0079.jpg │ │ ├── 234_0080.jpg │ │ ├── 234_0081.jpg │ │ ├── 234_0082.jpg │ │ ├── 234_0083.jpg │ │ ├── 234_0084.jpg │ │ ├── 234_0085.jpg │ │ ├── 234_0086.jpg │ │ ├── 234_0087.jpg │ │ ├── 234_0088.jpg │ │ ├── 234_0089.jpg │ │ ├── 234_0090.jpg │ │ ├── 234_0091.jpg │ │ ├── 234_0092.jpg │ │ ├── 234_0093.jpg │ │ ├── 234_0094.jpg │ │ ├── 234_0095.jpg │ │ ├── 234_0096.jpg │ │ ├── 234_0097.jpg │ │ ├── 234_0098.jpg │ │ ├── 234_0099.jpg │ │ ├── 234_0100.jpg │ │ ├── 234_0101.jpg │ │ ├── 234_0102.jpg │ │ ├── 234_0103.jpg │ │ ├── 234_0104.jpg │ │ ├── 234_0105.jpg │ │ ├── 234_0106.jpg │ │ ├── 234_0107.jpg │ │ ├── 234_0108.jpg │ │ ├── 234_0109.jpg │ │ ├── 234_0110.jpg │ │ ├── 234_0111.jpg │ │ ├── 234_0112.jpg │ │ ├── 234_0113.jpg │ │ ├── 234_0114.jpg │ │ ├── 234_0115.jpg │ │ ├── 234_0116.jpg │ │ ├── 234_0117.jpg │ │ ├── 234_0118.jpg │ │ ├── 234_0119.jpg │ │ ├── 234_0120.jpg │ │ ├── 234_0121.jpg │ │ └── 234_0122.jpg │ └── 249.yo-yo │ │ ├── 249_0001.jpg │ │ ├── 249_0002.jpg │ │ ├── 249_0003.jpg │ │ ├── 249_0004.jpg │ │ ├── 249_0005.jpg │ │ ├── 249_0006.jpg │ │ ├── 249_0007.jpg │ │ ├── 249_0008.jpg │ │ ├── 249_0009.jpg │ │ ├── 249_0010.jpg │ │ ├── 249_0011.jpg │ │ ├── 249_0012.jpg │ │ ├── 249_0013.jpg │ │ ├── 249_0014.jpg │ │ ├── 249_0015.jpg │ │ ├── 249_0016.jpg │ │ ├── 249_0017.jpg │ │ ├── 249_0018.jpg │ │ ├── 249_0019.jpg │ │ ├── 249_0020.jpg │ │ ├── 249_0021.jpg │ │ ├── 249_0022.jpg │ │ ├── 249_0023.jpg │ │ ├── 249_0024.jpg │ │ ├── 249_0025.jpg │ │ ├── 249_0026.jpg │ │ ├── 249_0027.jpg │ │ ├── 249_0028.jpg │ │ ├── 249_0029.jpg │ │ ├── 249_0030.jpg │ │ ├── 249_0031.jpg │ │ ├── 249_0032.jpg │ │ ├── 249_0033.jpg │ │ ├── 249_0034.jpg │ │ ├── 249_0035.jpg │ │ ├── 249_0036.jpg │ │ ├── 249_0037.jpg │ │ ├── 249_0038.jpg │ │ ├── 249_0039.jpg │ │ ├── 249_0040.jpg │ │ ├── 249_0041.jpg │ │ ├── 249_0042.jpg │ │ ├── 249_0043.jpg │ │ ├── 249_0044.jpg │ │ ├── 249_0045.jpg │ │ ├── 249_0046.jpg │ │ ├── 249_0047.jpg │ │ ├── 249_0048.jpg │ │ ├── 249_0049.jpg │ │ ├── 249_0050.jpg │ │ ├── 249_0051.jpg │ │ ├── 249_0052.jpg │ │ ├── 249_0053.jpg │ │ ├── 249_0054.jpg │ │ ├── 249_0055.jpg │ │ ├── 249_0056.jpg │ │ ├── 249_0057.jpg │ │ ├── 249_0058.jpg │ │ ├── 249_0059.jpg │ │ ├── 249_0060.jpg │ │ ├── 249_0061.jpg │ │ ├── 249_0062.jpg │ │ ├── 249_0063.jpg │ │ ├── 249_0064.jpg │ │ ├── 249_0065.jpg │ │ ├── 249_0066.jpg │ │ ├── 249_0067.jpg │ │ ├── 249_0068.jpg │ │ ├── 249_0069.jpg │ │ ├── 249_0070.jpg │ │ ├── 249_0071.jpg │ │ ├── 249_0072.jpg │ │ ├── 249_0073.jpg │ │ ├── 249_0074.jpg │ │ ├── 249_0075.jpg │ │ ├── 249_0076.jpg │ │ ├── 249_0077.jpg │ │ ├── 249_0078.jpg │ │ ├── 249_0079.jpg │ │ ├── 249_0080.jpg │ │ ├── 249_0081.jpg │ │ ├── 249_0082.jpg │ │ ├── 249_0083.jpg │ │ ├── 249_0084.jpg │ │ ├── 249_0085.jpg │ │ ├── 249_0086.jpg │ │ ├── 249_0087.jpg │ │ ├── 249_0088.jpg │ │ ├── 249_0089.jpg │ │ ├── 249_0090.jpg │ │ ├── 249_0091.jpg │ │ ├── 249_0092.jpg │ │ ├── 249_0093.jpg │ │ ├── 249_0094.jpg │ │ ├── 249_0095.jpg │ │ ├── 249_0096.jpg │ │ ├── 249_0097.jpg │ │ ├── 249_0098.jpg │ │ ├── 249_0099.jpg │ │ └── 249_0100.jpg ├── bot_data.csv ├── env_setting.ipynb └── winequality-red.csv ├── figures ├── mae.jpeg ├── rsquare.jpeg └── salary-example.png └── notebooks ├── mlruns └── 0 │ ├── 54c4f057d43e474f92cd0bfeef9fabd5 │ ├── artifacts │ │ └── model │ │ │ ├── MLmodel │ │ │ ├── conda.yaml │ │ │ ├── model.pkl │ │ │ └── requirements.txt │ ├── meta.yaml │ ├── metrics │ │ ├── mae │ │ ├── r2 │ │ └── rmse │ ├── params │ │ ├── alpha │ │ └── l1_ratio │ └── tags │ │ ├── mlflow.log-model.history │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ ├── 5b8533b5b016430f9facd42c9a13c9b7 │ ├── artifacts │ │ └── model │ │ │ ├── MLmodel │ │ │ ├── conda.yaml │ │ │ ├── model.pkl │ │ │ └── requirements.txt │ ├── meta.yaml │ ├── metrics │ │ ├── mae │ │ ├── r2 │ │ └── rmse │ ├── params │ │ ├── alpha │ │ └── l1_ratio │ └── tags │ │ ├── mlflow.log-model.history │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ ├── 6de15c2dc6cd43c681a511325c0704a7 │ ├── artifacts │ │ └── model │ │ │ ├── MLmodel │ │ │ ├── conda.yaml │ │ │ ├── model.pkl │ │ │ └── requirements.txt │ ├── meta.yaml │ ├── metrics │ │ ├── mae │ │ ├── r2 │ │ └── rmse │ ├── params │ │ ├── alpha │ │ └── l1_ratio │ └── tags │ │ ├── mlflow.log-model.history │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ ├── 7fa10efd7b1648868f43e663898f452c │ ├── artifacts │ │ └── model │ │ │ ├── MLmodel │ │ │ ├── conda.yaml │ │ │ ├── model.pkl │ │ │ └── requirements.txt │ ├── meta.yaml │ ├── metrics │ │ ├── mae │ │ ├── r2 │ │ └── rmse │ ├── params │ │ ├── alpha │ │ └── l1_ratio │ └── tags │ │ ├── mlflow.log-model.history │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ ├── 82b9276391af498f9a51c886bf563afe │ ├── artifacts │ │ └── model │ │ │ ├── MLmodel │ │ │ ├── conda.yaml │ │ │ ├── model.pkl │ │ │ └── requirements.txt │ ├── meta.yaml │ ├── metrics │ │ ├── mae │ │ ├── r2 │ │ └── rmse │ ├── params │ │ ├── alpha │ │ └── l1_ratio │ └── tags │ │ ├── mlflow.log-model.history │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ ├── bc5bd669d1884f8c8d2607a942b9262b │ ├── artifacts │ │ └── model │ │ │ ├── MLmodel │ │ │ ├── conda.yaml │ │ │ ├── model.pkl │ │ │ └── requirements.txt │ ├── meta.yaml │ ├── metrics │ │ ├── mae │ │ ├── r2 │ │ └── rmse │ ├── params │ │ ├── alpha │ │ └── l1_ratio │ └── tags │ │ ├── mlflow.log-model.history │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ ├── ce363f1b11b64d0eadcff90333d4dca6 │ ├── artifacts │ │ └── model │ │ │ ├── MLmodel │ │ │ ├── conda.yaml │ │ │ ├── model.pkl │ │ │ └── requirements.txt │ ├── meta.yaml │ ├── metrics │ │ ├── mae │ │ ├── r2 │ │ └── rmse │ ├── params │ │ ├── alpha │ │ └── l1_ratio │ └── tags │ │ ├── mlflow.log-model.history │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ ├── d57c7bb4d49742f589e5d9f3129aa70e │ ├── artifacts │ │ └── model │ │ │ ├── MLmodel │ │ │ ├── conda.yaml │ │ │ ├── model.pkl │ │ │ └── requirements.txt │ ├── meta.yaml │ ├── metrics │ │ ├── mae │ │ ├── r2 │ │ └── rmse │ ├── params │ │ ├── alpha │ │ └── l1_ratio │ └── tags │ │ ├── mlflow.log-model.history │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ ├── db6a02be076f43c5bd8947bb67554ab5 │ ├── meta.yaml │ └── tags │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ ├── f552fbc3fde940bf9bdc84f7c416cf19 │ ├── artifacts │ │ └── model │ │ │ ├── MLmodel │ │ │ ├── conda.yaml │ │ │ ├── model.pkl │ │ │ └── requirements.txt │ ├── meta.yaml │ ├── metrics │ │ ├── mae │ │ ├── r2 │ │ └── rmse │ ├── params │ │ ├── alpha │ │ └── l1_ratio │ └── tags │ │ ├── mlflow.log-model.history │ │ ├── mlflow.source.name │ │ ├── mlflow.source.type │ │ └── mlflow.user │ └── meta.yaml ├── week-1.0-mlflow.ipynb ├── week-1.0-pyspark.ipynb ├── week-2.0-pyspark-data-marshaling.ipynb ├── week-2.0-pyspark-images-feature-engineering.ipynb ├── week-2.0-pyspark-text-feature-engineering.ipynb ├── week-3.0-batch_deployment.ipynb ├── week-3.0-data-prep-for-training.ipynb ├── week-3.0-evaluate-and-automate-pipelines.ipynb └── week-3.0-train-with-mllib.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | .jupyter 80 | .local/share/* 81 | 82 | # IPython 83 | profile_default/ 84 | ipython_config.py 85 | 86 | # pyenv 87 | .python-version 88 | 89 | # pipenv 90 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 91 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 92 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 93 | # install all needed dependencies. 94 | #Pipfile.lock 95 | 96 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 97 | __pypackages__/ 98 | 99 | # Celery stuff 100 | celerybeat-schedule 101 | celerybeat.pid 102 | 103 | # SageMath parsed files 104 | *.sage.py 105 | 106 | # Environments 107 | .env 108 | .venv 109 | env/ 110 | venv/ 111 | ENV/ 112 | env.bak/ 113 | venv.bak/ 114 | 115 | # Spyder project settings 116 | .spyderproject 117 | .spyproject 118 | 119 | # Rope project settings 120 | .ropeproject 121 | 122 | # mkdocs documentation 123 | /site 124 | 125 | # mypy 126 | .mypy_cache/ 127 | .dmypy.json 128 | dmypy.json 129 | 130 | # Pyre type checker 131 | .pyre/ 132 | 133 | 134 | 135 | notebooks/mlruns/* 136 | .bash_history 137 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Scaling machine learning in 3 weeks course 2 | Scaling Machine Learning in Three Week course in a collaboration with O'Reilly following the guidance in this [book](https://amzn.to/3WgHQvd). 3 | 4 | This project contains the example code and solutions to the exercises in O'Reilly course. 5 | 6 | ## Course description 7 | Join expert Adi Polak to go in-depth into Spark, the most widely used technology in the data and machine learning ecosystem. You’ll gain hands-on experience building a scalable ML workflow with PySpark and MLflow, and you’ll learn how to deploy machine learning and reproduce your experiments. 8 | 9 | 10 | # Quick Start 11 | _Prerequisites:_ 12 | * make sure you have docker installed, if not skip to the notebooks viewer. 13 | 14 | 15 | From you local terminal, clone this repository, and enter the `scaling-machine-lerning-course` directory with 16 | 17 | ```code 18 | gh repo clone adipolak/scaling-machine-learning-course 19 | cd scaling-machine-learning-course 20 | ``` 21 | From the `scaling-machine-lerning-course` directory, run the following docker run command: 22 | 23 | ```code 24 | docker run -it --memory="28g" --memory-swap="30g" -p 8888:8888 --mount type=bind,source=$(pwd),target=/home/jovyan adipolak/ml-with-apache-spark 25 | ``` 26 | 27 | From here, you will see the docker run output, make sure to copy the url with the token, it would look something like this: 28 | 29 | ```code 30 | http://127.0.0.1:8888/?token=379fbbea91af62751b2616331d688f7a45db215b62dcfb04 31 | ``` 32 | 33 | ## Just want to quickly look at some notebooks, without executing any code? 34 | Brows it on the [Juypyter.org notebook viewer](https://nbviewer.org/github/adipolak/scaling-machine-learning-course/tree/main/notebooks/) 35 | 36 | 37 | 38 | # FAQ 39 | 40 | **Is AMD/new M1/M2 supported?** 41 | Not currently, in the future I will add support for those as well. 42 | 43 | 44 | **I get Py4JJavaError: An error occurred while calling o{some number}.parquet. (Reading Parquet file), what to do?** 45 | From Jupyter enter the terminal and validate your Java version, given that we use Spark 3.1.1 it should be `openjdk version "11.0.11"`. 46 | You local Java runtime should be of the same version - local Java runtime - is the Java that installed on the machine where you are running your docker from. 47 | 48 | 49 | **Which Apache Spark version should I use?** 50 | The apache spark version is 3.1.1 51 | 52 | # Contributors 53 | I want to thank everyone who contributed to this project by providing helpful feedback, filing issues, or submitting Pull Requests. 54 | If you would like to contribute, please feel free to submit a pull request, or reach out on [@AdiPolak](https://twitter.com/AdiPolak). 55 | -------------------------------------------------------------------------------- /datasets/256_sampledata/196.spaghetti/196_0001.jpg: -------------------------------------------------------------------------------- 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| "outputs": [], 17 | "source": [ 18 | "import zipfile\n", 19 | "with zipfile.ZipFile(\"/some/zip/to/extract.zip\",\"r\") as zip_ref:\n", 20 | " zip_ref.extractall(\"/folder/to/extract/to\")" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 8, 26 | "id": "52236328", 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "import shutil\n", 31 | "shutil.rmtree('/path/to/delete')" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "id": "20c55f57", 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [] 41 | } 42 | ], 43 | "metadata": { 44 | "kernelspec": { 45 | "display_name": "Python 3", 46 | "language": "python", 47 | "name": "python3" 48 | }, 49 | "language_info": { 50 | "codemirror_mode": { 51 | "name": "ipython", 52 | "version": 3 53 | }, 54 | "file_extension": ".py", 55 | "mimetype": "text/x-python", 56 | "name": "python", 57 | "nbconvert_exporter": "python", 58 | "pygments_lexer": "ipython3", 59 | "version": "3.9.4" 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-------------------------------------------------------------------------------- /notebooks/mlruns/0/f552fbc3fde940bf9bdc84f7c416cf19/tags/mlflow.user: -------------------------------------------------------------------------------- 1 | jovyan -------------------------------------------------------------------------------- /notebooks/mlruns/0/meta.yaml: -------------------------------------------------------------------------------- 1 | artifact_location: file:///home/jovyan/notebooks/mlruns/0 2 | experiment_id: '0' 3 | lifecycle_stage: active 4 | name: Default 5 | -------------------------------------------------------------------------------- /notebooks/week-2.0-pyspark-text-feature-engineering.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "38f2d36c", 6 | "metadata": {}, 7 | "source": [ 8 | "# ⭐ Scaling Machine Learning in Three Week course - Week 2:\n", 9 | "## PySpark - text - feature engineering\n", 10 | "\n", 11 | "In this excercise, you will use:\n", 12 | " * Bot data set\n", 13 | " * Work with Null values\n", 14 | "\n", 15 | "\n", 16 | "\n", 17 | "\n", 18 | "This excercise is part of the [Scaling Machine Learning with Spark book](https://learning.oreilly.com/library/view/scaling-machine-learning/9781098106812/)\n", 19 | "available on the O'Reilly platform or on [Amazon](https://amzn.to/3WgHQvd).\n" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 4, 25 | "id": "a45b62a7", 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "# Create SparkSession from builder\n", 30 | "import pyspark\n", 31 | "from pyspark.sql import SparkSession\n", 32 | "spark = SparkSession.builder.master(\"local[1]\") \\\n", 33 | " .appName('Scalling_ml_with_spark-week_2') \\\n", 34 | " .getOrCreate()" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "id": "2a03dfcd", 40 | "metadata": {}, 41 | "source": [ 42 | "### ✅ **Task 1 :** Tokanizer -> N Gram\n", 43 | "\n", 44 | "Extract an NGram using Tokanizer and connect data marshaling with Feature engineering.\n", 45 | "For that, use `NGram` functionality.\n", 46 | "\n", 47 | "
Hint\n", 48 | "

\n", 49 | " \n", 50 | "Checkout this:\n", 51 | " \n", 52 | "```python\n", 53 | "tokenizer = Tokenizer(inputCol=\"sentence\", outputCol=\"words\")\n", 54 | "countTokens = udf(lambda words: len(words), IntegerType())\n", 55 | "wordDataFrame = tokenizer.transform(sentenceDataFrame)\n", 56 | "\n", 57 | "ngram = NGram(n=2, inputCol=\"words\", outputCol=\"ngrams\")\n", 58 | "\n", 59 | "ngramDataFrame = ngram.transform(wordDataFrame)\n", 60 | "ngramDataFrame.select(\"ngrams\").show(truncate=False)\n", 61 | " \n", 62 | "```\n", 63 | "

\n", 64 | "
" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 5, 70 | "id": "adc10f2a", 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [ 74 | "from pyspark.ml.feature import NGram\n", 75 | "from pyspark.ml.feature import Tokenizer\n", 76 | "from pyspark.sql.functions import col, udf\n", 77 | "from pyspark.sql.types import IntegerType\n", 78 | "\n", 79 | "sentenceDataFrame = spark.createDataFrame([\n", 80 | " (0, \"Hi I heard about Spark \"),\n", 81 | " (1, \"I wish, wish Java, Java could\"),\n", 82 | " (2, \"Logistic regression, regression models\")\n", 83 | "], [\"id\", \"sentence\"])\n", 84 | "\n", 85 | "# your solution goes here\n", 86 | "# ...\n", 87 | "\n" 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "id": "9e0eef96", 93 | "metadata": {}, 94 | "source": [ 95 | "### ✅ **Task 2 :** PCA\n", 96 | "\n", 97 | "Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation\n", 98 | "\n", 99 | "Take the DF and reduce the demensions of the vectores using `PCA`.\n", 100 | "\n", 101 | "
\n", 102 | "\n", 103 | "
Click here to see the Solution\n", 104 | "

\n", 105 | " \n", 106 | "here:\n", 107 | " \n", 108 | "```python\n", 109 | "pca = PCA(k=3, inputCol=\"features\", outputCol=\"pcaFeatures\")\n", 110 | "model = pca.fit(df)\n", 111 | "\n", 112 | "result = model.transform(df).select(\"pcaFeatures\")\n", 113 | "result.show(truncate=False)\n", 114 | "```\n", 115 | "

\n", 116 | "
\n" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 6, 122 | "id": "aa857bef", 123 | "metadata": {}, 124 | "outputs": [], 125 | "source": [ 126 | "from pyspark.ml.feature import PCA\n", 127 | "from pyspark.ml.linalg import Vectors\n", 128 | "\n", 129 | "data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),),\n", 130 | " (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),\n", 131 | " (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]\n", 132 | "df = spark.createDataFrame(data, [\"features\"])\n", 133 | "\n", 134 | "# your solution goes here\n", 135 | "# ...\n" 136 | ] 137 | }, 138 | { 139 | "cell_type": "code", 140 | "execution_count": null, 141 | "id": "0566e5f0", 142 | "metadata": {}, 143 | "outputs": [], 144 | "source": [] 145 | } 146 | ], 147 | "metadata": { 148 | "kernelspec": { 149 | "display_name": "Python 3", 150 | "language": "python", 151 | "name": "python3" 152 | }, 153 | "language_info": { 154 | "codemirror_mode": { 155 | "name": "ipython", 156 | "version": 3 157 | }, 158 | "file_extension": ".py", 159 | "mimetype": "text/x-python", 160 | "name": "python", 161 | "nbconvert_exporter": "python", 162 | "pygments_lexer": "ipython3", 163 | "version": "3.9.4" 164 | } 165 | }, 166 | "nbformat": 4, 167 | "nbformat_minor": 5 168 | } 169 | -------------------------------------------------------------------------------- /notebooks/week-3.0-batch_deployment.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "0767a21b", 6 | "metadata": {}, 7 | "source": [ 8 | "# ⭐ Scaling Machine Learning in Three Week course \n", 9 | "# - Week 3:\n", 10 | "## Deployment\n", 11 | "\n", 12 | "**Prerequisite**\n", 13 | "Run notebook `week-3.0-data-prep-for-training` and `week-3.0-evaluate-and-automate-pipelines.ipynb` before.\n", 14 | "\n", 15 | "\n", 16 | "In this excercise, you will use:\n", 17 | " * deployments in batch setting\n", 18 | "\n", 19 | "\n", 20 | "\n", 21 | "\n", 22 | "This excercise is part of the [Scaling Machine Learning with Spark book](https://learning.oreilly.com/library/view/scaling-machine-learning/9781098106812/)\n", 23 | "available on the O'Reilly platform or on [Amazon](https://amzn.to/3WgHQvd).\n" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 17, 29 | "id": "c686250b", 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "import mlflow\n", 34 | "import mlflow.spark\n", 35 | "from pyspark.sql.types import ArrayType, StringType\n", 36 | "from pyspark.sql.functions import col, struct\n", 37 | "from pyspark.ml.regression import LinearRegression, LinearRegressionModel\n", 38 | "from pyspark.sql import SparkSession \n" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 18, 44 | "id": "e02a2c88", 45 | "metadata": {}, 46 | "outputs": [], 47 | "source": [ 48 | "spark = SparkSession.builder \\\n", 49 | " .master('local[*]') \\\n", 50 | " .appName(\"deployment\") \\\n", 51 | " .getOrCreate()" 52 | ] 53 | }, 54 | { 55 | "cell_type": "markdown", 56 | "id": "f53c3595", 57 | "metadata": {}, 58 | "source": [ 59 | " ### ✅ **Task 1 :** ### Move model from Model folder to Best Model\n", 60 | " \n", 61 | " Now that we have a model that gives us a good results, it's time to move it to the next phase." 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 19, 67 | "id": "32939824", 68 | "metadata": {}, 69 | "outputs": [], 70 | "source": [ 71 | "model_path = \"../models/linearRegression_model\"" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 20, 77 | "id": "84b485b5", 78 | "metadata": {}, 79 | "outputs": [], 80 | "source": [ 81 | "restored_mllib_model = LinearRegressionModel.load(model_path)\n" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 21, 87 | "id": "44068a22", 88 | "metadata": {}, 89 | "outputs": [], 90 | "source": [ 91 | "restored_mllib_model.save(\"../models/best_model\")" 92 | ] 93 | }, 94 | { 95 | "cell_type": "markdown", 96 | "id": "724fd2d4", 97 | "metadata": {}, 98 | "source": [ 99 | "### ✅ **Task 2 :** use the model for prediction in production\n", 100 | "\n", 101 | "imagine there is a deployment to production of the best_model\n", 102 | "that meanes, that there is a new app that is going to load the model within it and leverage it with Spark. \n", 103 | "so now, there is a production dataframe.\n", 104 | "\n", 105 | "Write the functionality to load the model, and use it to predict production dataframe in a batch setting." 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 22, 111 | "id": "5c53c794", 112 | "metadata": {}, 113 | "outputs": [], 114 | "source": [ 115 | "# your code goes\n", 116 | "# ..." 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "id": "92035982", 122 | "metadata": {}, 123 | "source": [ 124 | "How is it different from what you have done so far? \n", 125 | "\n", 126 | "shar your response in the chat!" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": null, 132 | "id": "e4572fde", 133 | "metadata": {}, 134 | "outputs": [], 135 | "source": [] 136 | } 137 | ], 138 | "metadata": { 139 | "kernelspec": { 140 | "display_name": "Python 3", 141 | "language": "python", 142 | "name": "python3" 143 | }, 144 | "language_info": { 145 | "codemirror_mode": { 146 | "name": "ipython", 147 | "version": 3 148 | }, 149 | "file_extension": ".py", 150 | "mimetype": "text/x-python", 151 | "name": "python", 152 | "nbconvert_exporter": "python", 153 | "pygments_lexer": "ipython3", 154 | "version": "3.9.4" 155 | } 156 | }, 157 | "nbformat": 4, 158 | "nbformat_minor": 5 159 | } 160 | --------------------------------------------------------------------------------