~Get Your Files Here !

  deploying-machine-learning-models-in-production

   01_week-1-model-serving-introduction

    01_a-conversation-with-andrew-ng-robert-crowe-and-laurence-moroney

     01_course-overview.en.txt

    02_introduction-to-model-serving

     01_introduction-to-model-serving.en.txt

     02_ungraded-labs-best-practices_instructions.html

     03_ungraded-lab-introduction-to-docker_instructions.html

    03_introduction-to-model-serving-infrastructure

     01_introduction-to-model-serving-infrastructure.en.txt

     02_deployment-options.en.srt

     02_deployment-options.mp4

     03_improving-prediction-latency-and-reducing-resource-costs.en.txt

     04_creating-and-deploying-models-to-ai-prediction-platform.en.srt

     04_creating-and-deploying-models-to-ai-prediction-platform.mp4

     05_optional-build-train-and-deploy-an-xgboost-model-on-cloud-ai-platform_instructions.html

    04_installing-tensorflow-serving

     01_installing-tensorflow-serving.en.txt

     02_ungraded-lab-tensorflow-serving-with-docker_C4_W1_Lab_2_TFS_Docker.md

     03_ungraded-lab-serve-a-model-with-tensorflow-serving_C4_W1_Lab_3_TFS.ipynb

   02_week-2-model-serving-patterns-and-infrastructure

    01_model-serving-architecture

     01_model-serving-architecture.en.txt

     02_model-servers-tensorflow-serving.en.srt

     02_model-servers-tensorflow-serving.mp4

     03_model-servers-other-providers.en.txt

     04_documentation-on-model-servers_instructions.html

     05_ungraded-lab-deploy-a-ml-model-with-fastapi-and-docker_instructions.html

    02_scaling-infrastructure

     01_scaling-infrastructure.en.txt

     02_learn-about-scaling-with-boy-bands_instructions.html

    03_online-inference

     01_online-inference.en.txt

     02_ungraded-lab-latency-testing-with-docker-compose-and-locust_README.md

    04_data-preprocessing

     01_data-preprocessing.en.txt

     02_data-preprocessing_instructions.html

    05_batch-inference-scenarios

     01_batch-inference-scenarios.en.txt

    06_batch-processing-with-etl

     01_batch-processing-with-etl.en.txt

     02_ungraded-lab-optional-machine-learning-with-apache-beam-and-tensorflow_C4_W2_Lab_4_Apache_Beam_and_T

   03_week-3-model-management-and-delivery

    01_ml-experiments-management-and-workflow-automation

     01_experiment-tracking.en.txt

     02_tools-for-experiment-tracking.en.srt

     02_tools-for-experiment-tracking.mp4

     04_introduction-to-mlops.en.srt

     04_introduction-to-mlops.mp4

    02_mlops-methodology

     01_mlops-level-0.en.txt

     02_mlops-levels-1-2.en.srt

     02_mlops-levels-1-2.mp4

     04_ungraded-lab-intro-to-kubeflow-pipelines_C4_W3_Lab_1_Kubeflow_Pipelines.ipynb

     05_developing-components-for-an-orchestrated-workflow.en.srt

     05_developing-components-for-an-orchestrated-workflow.mp4

    03_model-management-and-deployment-infrastructure

     01_managing-model-versions.en.txt

     02_ungraded-lab-model-versioning-with-tf-serving_README.md

     03_ml-model-management_instructions.html

     04_continuous-delivery.en.txt

     05_ungraded-lab-ci-cd-pipelines-with-github-actions_README.md

     06_continuous-delivery_instructions.html

     07_progressive-delivery.en.txt

     08_progressive-delivery_instructions.html

   04_week-4-model-monitoring-and-logging

    01_model-monitoring-and-logging

     01_why-monitoring-matters.en.txt

     02_observability-in-ml.en.srt

     02_observability-in-ml.mp4

     03_monitoring-targets-in-ml.en.txt

     04_logging-for-ml-monitoring.en.srt

     04_logging-for-ml-monitoring.mp4

     05_tracing-for-ml-systems.en.txt

     06_monitoring-machine-learning-models-in-production_instructions.html

    02_model-decay

     01_what-is-model-decay.en.txt

     02_model-decay-detection.en.srt

     02_model-decay-detection.mp4

     03_ways-to-mitigate-model-decay.en.txt

     04_addressing-model-decay_instructions.html

    03_gdpr-and-privacy

     01_responsible-ai.en.txt

     02_responsible-ai_instructions.html

     03_legal-requirements-for-secure-and-private-ai.en.txt

     04_gdpr-and-ccpa_instructions.html

     05_anonymization-and-pseudonymisation.en.txt

     06_right-to-be-forgotten.en.srt

     06_right-to-be-forgotten.mp4

    04_specialization-recap-and-farewell

     01_specialization-recap-and-farewell.en.txt

    05_course-resources

     01_course-4-optional-references_36356.pdf

     01_course-4-optional-references_BlueGreenDeployment.html

     01_course-4-optional-references_install.html

    06_acknowledgments

     02_optional-opportunity-to-mentor-other-learners_instructions.html

  introduction-to-machine-learning-in-production

   01_week-1-overview-of-the-ml-lifecycle-and-deployment

    01_a-conversation-with-andrew-ng-robert-crowe-and-laurence-moroney

     01_specialization-overview.en.txt

    02_the-machine-learning-project-lifecycle

     01_welcome.en.txt

     02_steps-of-an-ml-project.en.srt

     02_steps-of-an-ml-project.mp4

     03_case-study-speech-recognition.en.txt

     04_course-outline.en.srt

     04_course-outline.mp4

    03_deployment

     01_key-challenges.en.txt

     02_the-machine-learning-project-lifecycle_exam.html

     03_deployment-patterns.en.txt

     04_monitoring.en.srt

     04_monitoring.mp4

     05_pipeline-monitoring.en.txt

     06_week-1-optional-references_2010.02013

     06_week-1-optional-references_86df7dcfd896fcaf2674f757a2463eba-Paper.pdf

    04_graded-assessment

    05_ungraded-lab

   02_week-2-select-and-train-a-model

    01_selecting-and-training-a-model

     01_modeling-overview.en.txt

     02_key-challenges.en.srt

     02_key-challenges.mp4

     03_why-low-average-error-isn-t-good-enough.en.txt

     04_establish-a-baseline.en.srt

     04_establish-a-baseline.mp4

     05_tips-for-getting-started.en.txt

     06_selecting-and-training-a-model_exam.html

    02_error-analysis-and-performance-auditing

     01_error-analysis-example.en.txt

     02_prioritizing-what-to-work-on.en.srt

     02_prioritizing-what-to-work-on.mp4

     03_skewed-datasets.en.txt

     04_performance-auditing.en.srt

     04_performance-auditing.mp4

    03_data-iteration

     01_data-centric-ai-development.en.txt

     02_a-useful-picture-of-data-augmentation.en.srt

     02_a-useful-picture-of-data-augmentation.mp4

     03_data-augmentation.en.txt

     04_can-adding-data-hurt.en.srt

     04_can-adding-data-hurt.mp4

     05_adding-features.en.txt

     06_experiment-tracking.en.srt

     06_experiment-tracking.mp4

     07_from-big-data-to-good-data.en.txt

     08_week-2-optional-references_1912.02292

     08_week-2-optional-references_instructions.html

    04_graded-assessment

   03_week-3-data-definition-and-baseline

    01_define-data-and-establish-baseline

     01_why-is-data-definition-hard.en.txt

     02_more-label-ambiguity-examples.en.srt

     02_more-label-ambiguity-examples.mp4

     03_major-types-of-data-problems.en.txt

     04_small-data-and-label-consistency.en.srt

     04_small-data-and-label-consistency.mp4

     05_improving-label-consistency.en.txt

     06_human-level-performance-hlp.en.srt

     06_human-level-performance-hlp.mp4

     07_raising-hlp.en.txt

    02_label-and-organize-data

     01_obtaining-data.en.txt

     02_data-pipeline.en.srt

     02_data-pipeline.mp4

     03_meta-data-data-provenance-and-lineage.en.txt

     04_balanced-train-dev-test-splits.en.srt

     04_balanced-train-dev-test-splits.mp4

    03_scoping-optional

     01_what-is-scoping.en.txt

     02_scoping-process.en.srt

     02_scoping-process.mp4

     03_diligence-on-feasibility-and-value.en.txt

     04_diligence-on-value.en.srt

     04_diligence-on-value.mp4

     05_milestones-and-resourcing.en.txt

     06_week-3-optional-references_1706.06969.pdf

     06_week-3-optional-references_instructions.html

    04_course-resources

     01_references_1912.02292

     01_references_2010.02013

     01_references_86df7dcfd896fcaf2674f757a2463eba-Paper.pdf

     01_references_instructions.html

    05_acknowledgements

  machine-learning-data-lifecycle-in-production

   01_week-1-collecting-labeling-and-validating-data

    01_a-conversation-with-andrew-ng-robert-crowe-and-laurence-moroney

     01_specialization-overview.en.txt

     02_course-overview.en.srt

     02_course-overview.mp4

    02_introduction-to-machine-learning-engineering-in-production

     01_overview.en.txt

     02_ml-pipelines.en.srt

     02_ml-pipelines.mp4

    03_collecting-data

     01_importance-of-data.en.txt

     02_example-application-suggesting-runs.en.srt

     02_example-application-suggesting-runs.mp4

     03_responsible-data-security-privacy-fairness.en.txt

    04_labeling-data

     01_case-study-degraded-model-performance.en.txt

     02_data-and-concept-change-in-production-ml.en.srt

     02_data-and-concept-change-in-production-ml.mp4

     03_process-feedback-and-human-labeling.en.txt

    05_validating-data

     01_detecting-data-issues.en.txt

     02_tensorflow-data-validation.en.srt

     02_tensorflow-data-validation.mp4

     03_week-1-optional-references_2011.09926

     03_week-1-optional-references_instructions.html

     04_optional-downloading-your-notebook-and-refreshing-your-workspace_instructions.html

    06_assignment

   02_week-2-feature-engineering-transformation-and-selection

    01_feature-engineering

     01_introduction-to-preprocessing.en.txt

     02_preprocessing-operations.en.srt

     02_preprocessing-operations.mp4

     03_feature-engineering-techniques.en.txt

     04_feature-crosses.en.srt

     04_feature-crosses.mp4

    02_feature-transformation-at-scale

     01_preprocessing-data-at-scale.en.txt

     02_tensorflow-transform.en.srt

     02_tensorflow-transform.mp4

     03_hello-world-with-tf-transform.en.txt

    03_feature-selection

     01_feature-spaces.en.txt

     02_feature-selection.en.srt

     02_feature-selection.mp4

     03_filter-methods.en.txt

     04_wrapper-methods.en.srt

     04_wrapper-methods.mp4

     05_embedded-methods.en.txt

     06_week-2-optional-references_instructions.html

     06_week-2-optional-references_shampoo.csv

   03_week-3-data-journey-and-data-storage

    01_data-journey-and-data-storage

     01_data-journey.en.txt

     02_introduction-to-ml-metadata.en.srt

     02_introduction-to-ml-metadata.mp4

     03_ml-metadata-in-action.en.txt

    02_evolving-data

     01_schema-development.en.txt

     02_schema-environments.en.srt

     02_schema-environments.mp4

    03_enterprise-data-storage

     01_feature-stores.en.txt

     02_data-warehouse.en.srt

     02_data-warehouse.mp4

     03_data-lakes.en.txt

     04_week-3-optional-references_instructions.html

   04_week-4-optional-advanced-labeling-augmentation-and-data-preprocessing

    01_advanced-labeling-optional

     01_semi-supervised-learning.en.txt

     02_active-learning.en.srt

     02_active-learning.mp4

     03_weak-supervision.en.txt

    02_data-augmentation-optional

     01_data-augmentation.en.txt

    03_preprocessing-different-data-types-optional

     01_time-series.en.txt

     02_sensors-and-signals.en.srt

     02_sensors-and-signals.mp4

     03_week-4-optional-references_cifar.html

     03_week-4-optional-references_improving-deep-learning-performance.

    04_course-resources

     01_course-2-optional-references_2010.02013

     01_course-2-optional-references_86df7dcfd896fcaf2674f757a2463eba-Paper.pdf

     01_course-2-optional-references_dataset.php

     01_course-2-optional-references_instructions.html

     01_course-2-optional-references_preprocessing-for-machine-learning-with.html

    05_acknowledgements

  machine-learning-modeling-pipelines-in-production

   01_week-1-neural-architecture-search

    01_a-conversation-with-andrew-ng-robert-crowe-and-laurence-moroney

     01_course-overview.en.txt

    02_hyperparameter-tuning-searching-for-the-best-architecture

     01_hyperparameter-tuning.en.txt

     02_keras-autotuner-demo.en.srt

     02_keras-autotuner-demo.mp4

     03_ungraded-lab-intro-to-keras-tuner_instructions.html

    03_automl

     01_intro-to-automl.en.txt

     02_understanding-search-spaces.en.srt

     02_understanding-search-spaces.mp4

     03_search-strategies.en.txt

     04_neural-architecture-search_1603.01670

     04_neural-architecture-search_1712.00559.pdf

     04_neural-architecture-search_instructions.html

     05_measuring-automl-efficacy.en.txt

     06_automl-on-the-cloud.en.srt

     06_automl-on-the-cloud.mp4

    04_assignment-classify-images-of-clouds-in-the-cloud-with-automl-vision

     01_assignment-setup.en.txt

     02_week-1-wrap-up.en.srt

     02_week-1-wrap-up.mp4

   02_week-2-model-resource-management-techniques

    01_dimensionality-reduction

     01_dimensionality-effect-on-performance.en.txt

     02_curse-of-dimensionality.en.srt

     02_curse-of-dimensionality.mp4

     03_curse-of-dimensionality-an-example.en.txt

     04_manual-dimensionality-reduction.en.srt

     04_manual-dimensionality-reduction.mp4

     05_manual-dimensionality-reduction-case-study.en.txt

     06_ungraded-lab-manual-feature-engineering_C3_W2_Lab_1_Manual_Dimensionality.ipynb

     07_algorithmic-dimensionality-reduction.en.srt

     07_algorithmic-dimensionality-reduction.mp4

     08_principal-components-analysis.en.txt

     09_other-techniques.en.srt

     09_other-techniques.mp4

     10_dimensionality-reduction-techniques_1404.2986.pdf

     11_ungraded-lab-algorithmic-dimensionality-reduction_C3_W2_Lab_2_Algorithmic_Dimensionality.ipynb

    02_quantization-and-pruning

     01_mobile-iot-and-similar-use-cases.en.txt

     02_benefits-and-process-of-quantization.en.srt

     02_benefits-and-process-of-quantization.mp4

     03_post-training-quantization.en.txt

     04_quantization-aware-training.en.srt

     04_quantization-aware-training.mp4

     05_quantization_instructions.html

     06_pruning.en.txt

     07_pruning_1803.03635

     07_pruning_lecun-90b.pdf

     08_ungraded-lab-quantization-and-pruning_instructions.html

   03_week-3-high-performance-modeling

    01_high-performance-modeling

     01_distributed-training.en.txt

     02_ungraded-lab-distributed-strategies-with-tf-and-keras_C3_W3_Lab_1_Distributed_Training.ipynb

     03_high-performance-ingestion.en.srt

     03_high-performance-ingestion.mp4

     04_training-large-models-the-rise-of-giant-neural-nets-and-parallelism.en.txt

     05_high-performance-modeling_1806.03377

     05_high-performance-modeling_instructions.html

    02_knowledge-distillation

     01_teacher-and-student-networks.en.txt

     02_knowledge-distillation-techniques.en.srt

     02_knowledge-distillation-techniques.mp4

     03_ungraded-lab-knowledge-distillation_instructions.html

     04_case-study-how-to-distill-knowledge-for-a-q-a-task.en.txt

     05_knowledge-distillation_1503.02531.pdf

     05_knowledge-distillation_instructions.html

   04_week-4-model-analysis

    01_model-analysis-overview

     01_model-performance-analysis.en.txt

     02_tensorboard_instructions.html

    02_advanced-model-analysis-and-debugging

     01_introduction-to-tensorflow-model-analysis.en.txt

     02_tfma-in-practice.en.srt

     02_tfma-in-practice.mp4

     03_tensorflow-model-analysis_introducing-tensorflow-model-analysis.html

     04_model-debugging-overview.en.txt

     05_benchmark-models.en.srt

     05_benchmark-models.mp4

     06_sensitivity-analysis-and-adversarial-attacks.en.txt

     07_adversarial-attack-demo.en.srt

     07_adversarial-attack-demo.mp4

     08_sensitivity-analysis-and-adversarial-attacks_adversarial_fgsm.ipynb

     09_residual-analysis.en.srt

     09_residual-analysis.mp4

     10_model-remediation.en.txt

     11_fairness.en.srt

     11_fairness.mp4

     12_measuring-fairness.en.txt

     13_model-remediation-and-fairness_1904.13341.pdf

    03_continuous-evaluation-and-monitoring

     01_continuous-evaluation-and-monitoring.en.txt

     02_continuous-evaluation-and-monitoring_1704.00023.pdf

   05_week-5-interpretability

    01_explainable-ai

     01_explainable-ai.en.txt

     02_explainable-ai_1910.10045.pdf

    02_interpretability

     01_model-interpretation-methods.en.txt

     02_intrinsically-interpretable-models.en.srt

     02_intrinsically-interpretable-models.mp4

     03_interpretability_instructions.html

    03_understanding-model-predictions

     01_model-agnostic-methods.en.txt

     02_partial-dependence-plots.en.srt

     02_partial-dependence-plots.mp4

     03_permutation-feature-importance.en.txt

     04_permutation-feature-importance_1801.01489

     05_ungraded-lab-permutation-feature-importance_C3_W5_Lab_2_Permutation_Importance.ipynb

     06_shapley-values.en.srt

     06_shapley-values.mp4

     07_shapley-additive-explanations-shap.en.txt

     08_ungraded-lab-shapley-values_C3_W5_Lab_1_Shap_Values.ipynb

     09_understanding-model-predictions_1905.04610

     09_understanding-model-predictions_instructions.html

     10_testing-concept-activation-vectors.en.txt

     11_lime.en.srt

     11_lime.mp4

     12_tcav-and-lime_instructions.html

     13_ai-explanations.en.txt

     14_ai-explanations_AI_Explainability_Whitepaper.pdf

    04_course-resources

     01_course-3-optional-references_1412.6572.pdf

     01_course-3-optional-references_1503.02531.pdf

     01_course-3-optional-references_1603.01670

     01_course-3-optional-references_1611.01578.pdf

     01_course-3-optional-references_1704.00023.pdf

     01_course-3-optional-references_1711.11279.pdf

     01_course-3-optional-references_1712.05877

     01_course-3-optional-references_1803.03635

     01_course-3-optional-references_1808.05377.pdf

     01_course-3-optional-references_1904.13341.pdf

     01_course-3-optional-references_1911.04252

     01_course-3-optional-references_2011.09926

     01_course-3-optional-references_FPF_WarningSigns_Report.pdf

     01_course-3-optional-references_decomposition.html

     01_course-3-optional-references_instructions.html

     01_course-3-optional-references_introducing-tensorflow-model-analysis.html

     01_course-3-optional-references_parsons.pdf

     01_course-3-optional-references_plot_partial_dependence.html

     01_course-3-optional-references_xai_survey_paper_2017.pdf

    05_acknowledgments