├── .gitignore ├── C1 - Introduction to Machine Learning in Production ├── Week 1 │ ├── Q1 - The Machine Learning Project Lifecycle.txt │ └── Q2 - Deployment.txt ├── Week 2 │ ├── C1W2_Ungraded_Lab_Birds_Cats_Dogs.ipynb │ ├── Q1 - Selecting and Training a Model.txt │ └── Q2 - Modeling challenges.txt └── Week 3 │ ├── C1W3_Data_Labeling_Ungraded_Lab.ipynb │ ├── C1W3_Data_Labeling_Ungraded_Lab.pdf │ └── Q1 - Data Definition and Baseline.txt ├── C2 - Machine Learning Data Lifecycle in Production ├── Week 1 │ ├── C2W1_Assignment.ipynb │ ├── C2W1_Assignment.pdf │ ├── C2_W1_Lab_1_TFDV_Exercise.ipynb │ ├── C2_W1_Lab_1_TFDV_Exercise.pdf │ ├── Q1 Intro to MLEP.txt │ ├── Q2 - Data Collection.txt │ ├── Q3 - Data Labeling.txt │ ├── Q4 - Issues in Training.txt │ ├── dataset_diabetes │ │ └── diabetic_data.csv │ ├── img │ │ └── tfdv.png │ └── util.py ├── Week 2 │ ├── C2W2_Assignment.ipynb │ ├── C2W2_Assignment_Updated.ipynb │ ├── C2_W2_Lab_1_Simple_Feature_Engineering.ipynb │ ├── C2_W2_Lab_1_Simple_Feature_Engineering.pdf │ ├── C2_W2_Lab_2_Feature_Engineering_Pipeline.ipynb │ ├── C2_W2_Lab_2_Feature_Engineering_Pipeline.pdf │ ├── C2_W2_Lab_3_Feature_Selection.ipynb │ ├── C2_W2_Lab_3_Feature_Selection.pdf │ ├── Q1 - Feature Engineering and Preprocessing.txt │ ├── Q2 - Feature Transformation.txt │ ├── Q3 - Feature Selection.txt │ ├── img │ │ └── feature_eng_pipeline.png │ └── metro_traffic_pipeline │ │ └── data │ │ └── metro_traffic_volume.csv ├── Week 3 │ ├── C2W3_Assignment.ipynb │ ├── C2W3_Assignment_updated.ipynb │ ├── C2_W3_Lab_1_MLMetadata.ipynb │ ├── C2_W3_Lab_1_MLMetadata.pdf │ ├── C2_W3_Lab_2_IterativeSchema.ipynb │ ├── C2_W3_Lab_2_IterativeSchema.pdf │ ├── Q1 - Data Journey.txt │ ├── Q2 - Schema Environments.txt │ ├── Q3 - Enterprise Data Storage.txt │ └── util.py └── Week 4 │ ├── C2_W4_Lab_1_WeatherData.ipynb │ ├── C2_W4_Lab_2_Signals.ipynb │ ├── C2_W4_Lab_2_Signals.pdf │ ├── C2_W4_Lab_3_Images.ipynb │ ├── C2_W4_Lab_3_Images.pdf │ ├── Q1 - Advanced Labelling.txt │ ├── Q2 - Data Augmentation.txt │ └── Q3 - Different Data Types.txt ├── C3 - Machine Learning Modeling Pipelines in Production ├── Week 1 │ ├── C3_W1.pdf │ ├── C3_W1_Lab_1_Keras_Tuner.ipynb │ ├── C3_W1_Lab_2_TFX_Tuner_and_Trainer.ipynb │ ├── Q1 - Hyperparameter Tuning and Neural Architecture Search.txt │ ├── Q2 - AutoML.txt │ └── data │ │ └── data.csv ├── Week 2 │ ├── C3_W2.pdf │ ├── C3_W2_Lab_1_Manual_Dimensionality.ipynb │ ├── C3_W2_Lab_2_Algorithmic_Dimensionality.ipynb │ ├── C3_W2_Lab_3_Quantization_and_Pruning.ipynb │ ├── Q1 - Dimensionality Reduction.txt │ ├── Q2 - Quantization and Purning.txt │ └── data │ │ ├── taxi-test.csv │ │ ├── taxi-train.csv │ │ └── taxi-valid.csv ├── Week 3 │ ├── C3_W3.pdf │ ├── C3_W3_Lab_1_Distributed_Training.ipynb │ ├── C3_W3_Lab_2_Knowledge_Distillation.ipynb │ ├── Q1 - High-Performance Modeling.txt │ └── Q2 - Knowledge Distillation.txt ├── Week 4 │ ├── C3_W4.pdf │ ├── C3_W4_Lab_1_TFMA.ipynb │ ├── C3_W4_Lab_2_TFX_Evaluator.ipynb │ ├── C3_W4_Lab_3_Fairness_Indicators.ipynb │ ├── Q1 - Model Analysis.txt │ ├── Q2- Model Analysis and Debugging.txt │ └── Q3 - Continuous Evaluation and Monitoring.txt └── Week 5 │ ├── C3_W5.pdf │ ├── C3_W5_Lab_1_Shap_Values.ipynb │ ├── C3_W5_Lab_2_Permutation_Importance.ipynb │ ├── Q1 - Explainable AI.txt │ ├── Q2 - Interpretability.txt │ └── Q3 - Understanding Model Predictions.txt ├── C4 - Welcome to Deploying Machine Learning Models in Production ├── Week 1 │ ├── C4_W1.pdf │ ├── C4_W1_Lab_1_Docker_Intro.md │ ├── C4_W1_Lab_2_TFS_Docker.md │ ├── C4_W1_Lab_2_TF_Serving.ipynb │ ├── Q1 - Introduction to Model Serving.txt │ ├── Q2 - Introduction to Model Serving Infrastructure.txt │ └── Q3 - TensorFlow Serving.txt ├── Week 2 │ ├── C4_W2.pdf │ ├── C4_W2_Lab_1_FastAPI_Docker │ │ ├── README.md │ │ ├── no-batch │ │ │ ├── Dockerfile │ │ │ ├── README.md │ │ │ ├── app │ │ │ │ ├── main.py │ │ │ │ └── wine.pkl │ │ │ ├── requirements.txt │ │ │ └── wine-examples │ │ │ │ ├── 0.json │ │ │ │ ├── 1.json │ │ │ │ └── 2.json │ │ └── with-batch │ │ │ ├── Dockerfile │ │ │ ├── README.md │ │ │ ├── app │ │ │ ├── main.py │ │ │ └── wine.pkl │ │ │ ├── requirements.txt │ │ │ └── wine-examples │ │ │ ├── batch_0.json │ │ │ ├── batch_1.json │ │ │ └── batch_2.json │ ├── C4_W2_Lab_2_Intro_to_Kubernetes │ │ ├── README.md │ │ ├── img │ │ │ ├── autoscale_start.png │ │ │ ├── autoscaled.png │ │ │ ├── initial_load.png │ │ │ └── kubernetes.png │ │ ├── metrics-server-yaml │ │ │ └── metrics-server.yaml │ │ ├── request.ps1 │ │ ├── request.sh │ │ ├── saved_model_half_plus_two_cpu │ │ │ └── 00000123 │ │ │ │ ├── assets │ │ │ │ └── foo.txt │ │ │ │ ├── saved_model.pb │ │ │ │ └── variables │ │ │ │ ├── variables.data-00000-of-00001 │ │ │ │ └── variables.index │ │ └── yaml │ │ │ ├── autoscale.yaml │ │ │ ├── configmap.yaml │ │ │ ├── deployment.yaml │ │ │ └── service.yaml │ ├── C4_W2_Lab_3_Latency_Test_Compose │ │ ├── README.md │ │ ├── docker-compose.yml │ │ └── locustfile.py │ ├── C4_W2_Lab_4_Apache_Beam_and_Tensorflow.ipynb │ ├── C4_W2_Lab_4_ETL_Beam │ │ ├── C4_W2_Lab_4_Apache_Beam_and_Tensorflow.ipynb │ │ ├── data │ │ │ └── molecules.tar.gz │ │ └── images │ │ │ ├── etl.png │ │ │ ├── overview.png │ │ │ └── predict.png │ ├── Q1 - Model serving architecture.txt │ ├── Q2 - Scaling Infrastructure.txt │ ├── Q3 - Online Inference.txt │ ├── Q4 - Data Preprocessing.txt │ ├── Q5 - Batch inference scenarios.txt │ └── Q6 - Batch Processing with ETL.txt ├── Week 3 │ ├── C4_W3.pdf │ ├── C4_W3_Lab_1_Intro_to_KFP │ │ ├── C4_W3_Lab_1_Kubeflow_Pipelines.ipynb │ │ └── img │ │ │ ├── complete.png │ │ │ ├── complete_pipeline.png │ │ │ ├── dag_kfp.png │ │ │ ├── highlevel.jpg │ │ │ ├── kfp_ui.png │ │ │ ├── logs.png │ │ │ ├── progress.png │ │ │ ├── simple_dag.jpg │ │ │ ├── upload.png │ │ │ ├── upload_pipeline.png │ │ │ └── url.png │ ├── C4_W3_Lab_1_Kubeflow_Pipelines.ipynb │ ├── C4_W3_Lab_2_TFX_Custom_Components.ipynb │ ├── C4_W3_Lab_3_TFS_Model_Versioning │ │ ├── .gitignore │ │ ├── README.md │ │ ├── dog_example.json │ │ ├── models │ │ │ └── models.config │ │ └── setup.sh │ ├── C4_W3_Lab_4_Github_Actions │ │ ├── README.md │ │ ├── app │ │ │ ├── data │ │ │ │ └── test_data.pkl │ │ │ ├── main.py │ │ │ ├── models │ │ │ │ ├── wine-95-fixed.pkl │ │ │ │ ├── wine-95.pkl │ │ │ │ └── wine.pkl │ │ │ └── test_clf.py │ │ └── requirements.txt │ ├── ENB2012_data.xlsx │ ├── Q1 - 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