Machine Learning Engineering for Production (MLOps) Specialization

About Course

Free Machine Learning Engineering for Production (MLOps) Specialization

This Specialization, offered by Coursera, provides a comprehensive understanding of machine learning engineering for production, covering concepts, tools, and methodologies for building and maintaining integrated systems that operate continuously.

Learn how to design, deploy, and continuously improve machine learning applications in production environments. This specialization is ideal for individuals who are looking to build an effective AI career. You will gain practical skills in:

Designing end-to-end ML production systems
Establishing model baselines and addressing concept drift
Building data pipelines for gathering, cleaning, and validating datasets
Implementing feature engineering, transformation, and selection with TensorFlow Extended
Managing modeling resources and serving offline/online inference requests
Addressing model fairness and explainability issues
Delivering deployment pipelines for model serving
Maintaining continuously operating production systems

This specialization is offered for free on Theetay. Explore other free online courses from platforms like Udemy, Udacity, Coursera, MasterClass, NearPeer, and more. Enroll today and take the first step towards a successful career in machine learning engineering.

Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.

Course Content

01. Introduction to Machine Learning in Production

A Message from the Professor
0003 01_specialization-overview.mp4

00:00
0009 02_steps-of-an-ml-project.mp4

00:00
0012 03_case-study-speech-recognition.mp4

00:00
0015 04_course-outline.mp4

00:00
0016 05_important-have-questions-issues-or-ideas-join-our-forum_instructions.html

00:00
0020 01_key-challenges.mp4

00:00
0023 02_deployment-patterns.mp4

00:00
0026 03_monitoring.mp4

00:00
0029 04_pipeline-monitoring.mp4

00:00
0033 05_week-1-optional-references_instructions.html

00:00
0039 01_modeling-overview.mp4

00:00
0042 02_key-challenges.mp4

00:00
0045 03_why-low-average-error-isn-t-good-enough.mp4

00:00
0048 04_establish-a-baseline.mp4

00:00
0051 05_tips-for-getting-started.mp4

00:00
0052 06_selecting-and-training-a-model_exam.html

00:00
0055 01_error-analysis-example.mp4

00:00
0058 02_prioritizing-what-to-work-on.mp4

00:00
0061 03_skewed-datasets.mp4

00:00
0064 04_performance-auditing.mp4

00:00
0067 01_data-centric-ai-development.mp4

00:00
0070 02_a-useful-picture-of-data-augmentation.mp4

00:00
0073 03_data-augmentation.mp4

00:00
0076 04_can-adding-data-hurt.mp4

00:00
0079 05_adding-features.mp4

00:00
0082 06_experiment-tracking.mp4

00:00
0085 07_from-big-data-to-good-data.mp4

00:00
0088 08_week-2-optional-references_instructions.html

00:00
0093 01_why-is-data-definition-hard.mp4

00:00
0096 02_more-label-ambiguity-examples.mp4

00:00
0099 03_major-types-of-data-problems.mp4

00:00
0102 04_small-data-and-label-consistency.mp4

00:00
0105 05_improving-label-consistency.mp4

00:00
0108 06_human-level-performance-hlp.mp4

00:00
0111 07_raising-hlp.mp4

00:00
0114 01_obtaining-data.mp4

00:00
0117 02_data-pipeline.mp4

00:00
0120 03_meta-data-data-provenance-and-lineage.mp4

00:00
0123 04_balanced-train-dev-test-splits.mp4

00:00
0124 05_important-reminder-about-end-of-access-to-lab-notebooks_instructions.html

00:00
0125 06_data-stage-of-the-ml-production-lifecycle_exam.html

00:00
0128 01_what-is-scoping.mp4

00:00
0131 02_scoping-process.mp4

00:00
0134 03_diligence-on-feasibility-and-value.mp4

00:00
0137 04_diligence-on-value.mp4

00:00
0140 05_milestones-and-resourcing.mp4

00:00
0142 06_week-3-optional-references_DLDL.html

00:00
0145 07_scoping-optional_quiz.html

00:00
0146 01_lecture-notes-week-3_instructions.html

00:00
Readme.txt

00:00
Section Quiz

02. Machine Learning Data Lifecycle in Production

0003 01_specialization-overview.mp4

00:00
0006 02_course-overview.mp4

00:00
0009 01_overview.mp4

00:00
0012 02_ml-pipelines.mp4

00:00
0013 03_intro-to-mlep_quiz.html

00:00
0014 04_important-have-questions-issues-or-ideas-join-our-forum_instructions.html

00:00
0017 01_importance-of-data.mp4

00:00
0020 02_example-application-suggesting-runs.mp4

00:00
0023 03_responsible-data-security-privacy-fairness.mp4

00:00
0024 04_data-collection_quiz.html

00:00
0027 01_case-study-degraded-model-performance.mp4

00:00
0030 02_data-and-concept-change-in-production-ml.mp4

00:00
0033 03_process-feedback-and-human-labeling.mp4

00:00
0034 04_data-labeling_quiz.html

00:00
0037 01_detecting-data-issues.mp4

00:00
0040 02_tensorflow-data-validation.mp4

00:00
0041 03_issues-in-training-data_quiz.html

00:00
0045 04_week-1-optional-references_instructions.html

00:00
0048 05_optional-downloading-your-notebook-and-refreshing-your-workspace_instructions.html

00:00
0053 01_introduction-to-preprocessing.mp4

00:00
0056 02_preprocessing-operations.mp4

00:00
0059 03_feature-engineering-techniques.mp4

00:00
0062 04_feature-crosses.mp4

00:00
0063 05_feature-engineering-and-preprocessing_quiz.html

00:00
0066 01_preprocessing-data-at-scale.mp4

00:00
0069 02_tensorflow-transform.mp4

00:00
0072 03_hello-world-with-tf-transform.mp4

00:00
0073 04_feature-transformation_quiz.html

00:00
0076 01_feature-spaces.mp4

00:00
0079 02_feature-selection.mp4

00:00
0082 03_filter-methods.mp4

00:00
0085 04_wrapper-methods.mp4

00:00
0088 05_embedded-methods.mp4

00:00
0089 06_week-2-optional-references_instructions.html

00:00
0091 07_feature-selection_quiz.html

00:00
0095 01_data-journey.mp4

00:00
0098 02_introduction-to-ml-metadata.mp4

00:00
0101 03_ml-metadata-in-action.mp4

00:00
0102 04_data-journey_quiz.html

00:00
0105 01_schema-development.mp4

00:00
0108 02_schema-environments.mp4

00:00
0109 03_schema-environments_quiz.html

00:00
0112 01_feature-stores.mp4

00:00
0115 02_data-warehouse.mp4

00:00
0118 03_data-lakes.mp4

00:00
0119 04_week-3-optional-references_instructions.html

00:00
0120 05_enterprise-data-storage_quiz.html

00:00
0125 01_semi-supervised-learning.mp4

00:00
0128 02_active-learning.mp4

00:00
0131 03_weak-supervision.mp4

00:00
0132 04_advanced-labelling_quiz.html

00:00
0135 01_data-augmentation.mp4

00:00
0136 02_data-augmentation_quiz.html

00:00
0139 01_time-series.mp4

00:00
0142 02_sensors-and-signals.mp4

00:00
0144 03_week-4-optional-references_cifar.html

00:00
0148 04_different-data-types_quiz.html

00:00
0149 01_lecture-notes-week-4_instructions.html

00:00
Readme.txt

00:00
Section Quiz

03. Machine Learning Modeling Pipelines in Production

0003 01_course-overview.mp4

00:00
0009 02_keras-autotuner-demo.mp4

00:00
0010 03_important-have-questions-issues-or-ideas-join-our-forum_instructions.html

00:00
0011 04_hyperparameter-tuning-and-neural-architecture-search_quiz.html

00:00
0013 05_ungraded-lab-intro-to-keras-tuner_instructions.html

00:00
0016 01_intro-to-automl.mp4

00:00
0019 02_understanding-search-spaces.mp4

00:00
0022 03_search-strategies.mp4

00:00
0027 04_neural-architecture-search_instructions.html

00:00
0030 05_measuring-automl-efficacy.mp4

00:00
0033 06_automl-on-the-cloud.mp4

00:00
0034 07_automl_instructions.html

00:00
0035 08_automl_quiz.html

00:00
0038 01_assignment-setup.mp4

00:00
0041 02_week-1-wrap-up.mp4

00:00
0045 01_dimensionality-effect-on-performance.mp4

00:00
0048 02_curse-of-dimensionality.mp4

00:00
0051 03_curse-of-dimensionality-an-example.mp4

00:00
0054 04_manual-dimensionality-reduction.mp4

00:00
0057 05_manual-dimensionality-reduction-case-study.mp4

00:00
0059 06_ungraded-lab-manual-feature-engineering_instructions.html

00:00
0062 07_algorithmic-dimensionality-reduction.mp4

00:00
0065 08_principal-components-analysis.mp4

00:00
0068 09_other-techniques.mp4

00:00
0071 10_dimensionality-reduction-techniques_instructions.html

00:00
0072 11_dimensionality-reduction_quiz.html

00:00
0074 12_ungraded-lab-algorithmic-dimensionality-reduction_instructions.html

00:00
0077 01_mobile-iot-and-similar-use-cases.mp4

00:00
0080 02_benefits-and-process-of-quantization.mp4

00:00
0083 03_post-training-quantization.mp4

00:00
0086 04_quantization-aware-training.mp4

00:00
0088 05_quantization_instructions.html

00:00
0092 06_pruning.mp4

00:00
0094 07_pruning_instructions.html

00:00
0096 08_quantization-and-pruning_quiz.html

00:00
0098 09_ungraded-lab-quantization-and-pruning_instructions.html

00:00
0102 01_distributed-training.mp4

00:00
0104 02_ungraded-lab-distributed-strategies-with-tf-and-keras_instructions.html

00:00
0107 03_high-performance-ingestion.mp4

00:00
0110 04_training-large-models-the-rise-of-giant-neural-nets-and-parallelism.mp4

00:00
0113 05_high-performance-modeling_instructions.html

00:00
0115 06_high-performance-modeling_quiz.html

00:00
0118 01_teacher-and-student-networks.mp4

00:00
0121 02_knowledge-distillation-techniques.mp4

00:00
0123 03_ungraded-lab-knowledge-distillation_instructions.html

00:00
0126 04_case-study-how-to-distill-knowledge-for-a-q-a-task.mp4

00:00
0129 05_knowledge-distillation_instructions.html

00:00
0130 06_knowledge-distillation_quiz.html

00:00
0134 01_model-performance-analysis.mp4

00:00
0135 02_tensorboard_instructions.html

00:00
0137 03_model-analysis_quiz.html

00:00
0140 01_introduction-to-tensorflow-model-analysis.mp4

00:00
0143 02_tfma-in-practice.mp4

00:00
0144 03_tensorflow-model-analysis_instructions.html

00:00
0148 04_model-debugging-overview.mp4

00:00
0151 05_benchmark-models.mp4

00:00
0154 06_sensitivity-analysis-and-adversarial-attacks.mp4

00:00
0157 07_adversarial-attack-demo.mp4

00:00
0160 08_sensitivity-analysis-and-adversarial-attacks_instructions.html

00:00
0163 09_residual-analysis.mp4

00:00
0166 10_model-remediation.mp4

00:00
0169 11_fairness.mp4

00:00
0172 12_measuring-fairness.mp4

00:00
0174 13_model-remediation-and-fairness_instructions.html

00:00
0175 14_model-analysis-and-debugging_quiz.html

00:00
0178 01_continuous-evaluation-and-monitoring.mp4

00:00
0180 02_continuous-evaluation-and-monitoring_instructions.html

00:00
0181 03_continuous-evaluation-and-monitoring_quiz.html

00:00
0185 01_explainable-ai.mp4

00:00
0187 02_explainable-ai_instructions.html

00:00
0188 03_explainable-ai_quiz.html

00:00
0191 01_model-interpretation-methods.mp4

00:00
0194 02_intrinsically-interpretable-models.mp4

00:00
0196 03_interpretability_instructions.html

00:00
0197 04_interpretability_quiz.html

00:00
0200 01_model-agnostic-methods.mp4

00:00
0203 02_partial-dependence-plots.mp4

00:00
0206 03_permutation-feature-importance.mp4

00:00
0208 04_permutation-feature-importance_instructions.html

00:00
0210 05_ungraded-lab-permutation-feature-importance_instructions.html

00:00
0213 06_shapley-values.mp4

00:00
0216 07_shapley-additive-explanations-shap.mp4

00:00
0218 08_ungraded-lab-shapley-values_instructions.html

00:00
0221 09_understanding-model-predictions_instructions.html

00:00
0224 10_testing-concept-activation-vectors.mp4

00:00
0227 11_lime.mp4

00:00
0229 12_tcav-and-lime_instructions.html

00:00
0232 13_ai-explanations.mp4

00:00
0234 14_ai-explanations_instructions.html

00:00
0235 15_understanding-model-predictions_quiz.html

00:00
0236 01_important-reminder-about-end-of-access-to-lab-notebooks_instructions.html

00:00
Readme.txt

00:00
Section Quiz

04. Deploying Machine Learning Models in Production

0003 01_course-overview.mp4

00:00
0007 02_introduction-to-model-serving_exam.html

00:00
0008 03_ungraded-labs-best-practices_instructions.html

00:00
0010 04_ungraded-lab-introduction-to-docker_instructions.html

00:00
0011 05_important-have-questions-issues-or-ideas-join-our-forum_instructions.html

00:00
0014 01_introduction-to-model-serving-infrastructure.mp4

00:00
0017 02_deployment-options.mp4

00:00
0020 03_improving-prediction-latency-and-reducing-resource-costs.mp4

00:00
0023 04_creating-and-deploying-models-to-ai-prediction-platform.mp4

00:00
0025 05_optional-build-train-and-deploy-an-xgboost-model-on-cloud-ai-platform_instructions.html

00:00
0026 06_introduction-to-model-serving-infrastructure_exam.html

00:00
0029 01_installing-tensorflow-serving.mp4

00:00
0030 02_tensorflow-serving_exam.html

00:00
0032 03_ungraded-lab-tensorflow-serving-with-docker_instructions.html

00:00
0034 04_ungraded-lab-serve-a-model-with-tensorflow-serving_instructions.html

00:00
0038 01_model-serving-architecture.mp4

00:00
0041 02_model-servers-tensorflow-serving.mp4

00:00
0044 03_model-servers-other-providers.mp4

00:00
0045 04_documentation-on-model-servers_instructions.html

00:00
0046 05_model-serving-architecture_quiz.html

00:00
0047 06_ungraded-lab-deploy-a-ml-model-with-fastapi-and-docker_instructions.html

00:00
0051 01_scaling-infrastructure.mp4

00:00
0052 02_learn-about-scaling-with-boy-bands_instructions.html

00:00
0053 03_explore-kubernetes-and-kubeflow_instructions.html

00:00
0054 04_scaling-infrastructure_quiz.html

00:00
0057 01_online-inference.mp4

00:00
0058 02_online-inference_quiz.html

00:00
0059 03_ungraded-lab-latency-testing-with-docker-compose-and-locust_instructions.html

00:00
0063 01_data-preprocessing.mp4

00:00
0064 02_data-preprocessing_instructions.html

00:00
0065 03_data-preprocessing_quiz.html

00:00
0068 01_batch-inference-scenarios.mp4

00:00
0069 02_batch-inference-scenarios_quiz.html

00:00
0072 01_batch-processing-with-etl.mp4

00:00
0074 02_ungraded-lab-optional-machine-learning-with-apache-beam-and-tensorflow_instructions.html

00:00
0075 03_batch-processing-with-etl_quiz.html

00:00
0079 01_experiment-tracking.mp4

00:00
0082 02_tools-for-experiment-tracking.mp4

00:00
0083 03_experiment-tracking_instructions.html

00:00
0086 04_introduction-to-mlops.mp4

00:00
0087 05_ml-experiments-management-and-workflow-automation_quiz.html

00:00
0090 01_mlops-level-0.mp4

00:00
0093 02_mlops-levels-1-2.mp4

00:00
0094 03_mlops-resources_instructions.html

00:00
0096 04_optional-ungraded-lab-intro-to-kubeflow-pipelines_instructions.html

00:00
0099 05_developing-components-for-an-orchestrated-workflow.mp4

00:00
0100 06_architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build_instructions.html

00:00
0101 07_mlops-methodology_quiz.html

00:00
0104 01_managing-model-versions.mp4

00:00
0105 02_ungraded-lab-model-versioning-with-tf-serving_instructions.html

00:00
0107 03_ml-model-management_instructions.html

00:00
0110 04_continuous-delivery.mp4

00:00
0111 05_ungraded-lab-ci-cd-pipelines-with-github-actions_instructions.html

00:00
0113 06_continuous-delivery_instructions.html

00:00
0116 07_progressive-delivery.mp4

00:00
0117 08_progressive-delivery_instructions.html

00:00
0118 09_model-management-and-deployment-infrastructure_quiz.html

00:00
0122 01_why-monitoring-matters.mp4

00:00
0125 02_observability-in-ml.mp4

00:00
0128 03_monitoring-targets-in-ml.mp4

00:00
0131 04_logging-for-ml-monitoring.mp4

00:00
0134 05_tracing-for-ml-systems.mp4

00:00
0135 06_monitoring-machine-learning-models-in-production_instructions.html

00:00
0136 07_model-monitoring-and-logging_quiz.html

00:00
0137 08_important-reminder-about-end-of-access-to-lab-notebooks_instructions.html

00:00
0140 01_what-is-model-decay.mp4

00:00
0143 02_model-decay-detection.mp4

00:00
0146 03_ways-to-mitigate-model-decay.mp4

00:00
0147 04_addressing-model-decay_instructions.html

00:00
0148 05_model-decay_quiz.html

00:00
0151 01_responsible-ai.mp4

00:00
0152 02_responsible-ai_instructions.html

00:00
0155 03_legal-requirements-for-secure-and-private-ai.mp4

00:00
0156 04_gdpr-and-ccpa_instructions.html

00:00
0159 05_anonymization-and-pseudonymisation.mp4

00:00
0162 06_right-to-be-forgotten.mp4

00:00
0163 07_gdpr-and-privacy_quiz.html

00:00
0167 01_specialization-recap-and-farewell.mp4

00:00
0179 02_optional-opportunity-to-mentor-other-learners_instructions.html

00:00
Readme.txt

00:00
Section Quiz

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About Course

Free Machine Learning Engineering for Production (MLOps) Specialization

What Will You Learn?

Course Content

01. Introduction to Machine Learning in Production

A Message from the Professor

0003 01_specialization-overview.mp4

0009 02_steps-of-an-ml-project.mp4

0012 03_case-study-speech-recognition.mp4

0015 04_course-outline.mp4

0016 05_important-have-questions-issues-or-ideas-join-our-forum_instructions.html

0020 01_key-challenges.mp4

0023 02_deployment-patterns.mp4

0026 03_monitoring.mp4

0029 04_pipeline-monitoring.mp4

0033 05_week-1-optional-references_instructions.html

0039 01_modeling-overview.mp4

0042 02_key-challenges.mp4

0045 03_why-low-average-error-isn-t-good-enough.mp4

0048 04_establish-a-baseline.mp4

0051 05_tips-for-getting-started.mp4

0052 06_selecting-and-training-a-model_exam.html

0055 01_error-analysis-example.mp4

0058 02_prioritizing-what-to-work-on.mp4

0061 03_skewed-datasets.mp4

0064 04_performance-auditing.mp4

0067 01_data-centric-ai-development.mp4

0070 02_a-useful-picture-of-data-augmentation.mp4

0073 03_data-augmentation.mp4

0076 04_can-adding-data-hurt.mp4

0079 05_adding-features.mp4

0082 06_experiment-tracking.mp4

0085 07_from-big-data-to-good-data.mp4

0088 08_week-2-optional-references_instructions.html

0093 01_why-is-data-definition-hard.mp4

0096 02_more-label-ambiguity-examples.mp4

0099 03_major-types-of-data-problems.mp4

0102 04_small-data-and-label-consistency.mp4

0105 05_improving-label-consistency.mp4

0108 06_human-level-performance-hlp.mp4

0111 07_raising-hlp.mp4

0114 01_obtaining-data.mp4

0117 02_data-pipeline.mp4

0120 03_meta-data-data-provenance-and-lineage.mp4

0123 04_balanced-train-dev-test-splits.mp4

0124 05_important-reminder-about-end-of-access-to-lab-notebooks_instructions.html

0125 06_data-stage-of-the-ml-production-lifecycle_exam.html

0128 01_what-is-scoping.mp4

0131 02_scoping-process.mp4

0134 03_diligence-on-feasibility-and-value.mp4

0137 04_diligence-on-value.mp4

0140 05_milestones-and-resourcing.mp4

0142 06_week-3-optional-references_DLDL.html

0145 07_scoping-optional_quiz.html

0146 01_lecture-notes-week-3_instructions.html

Readme.txt

Section Quiz

02. Machine Learning Data Lifecycle in Production

0003 01_specialization-overview.mp4

0006 02_course-overview.mp4

0009 01_overview.mp4

0012 02_ml-pipelines.mp4

0013 03_intro-to-mlep_quiz.html

0014 04_important-have-questions-issues-or-ideas-join-our-forum_instructions.html

0017 01_importance-of-data.mp4

0020 02_example-application-suggesting-runs.mp4

0023 03_responsible-data-security-privacy-fairness.mp4

0024 04_data-collection_quiz.html

0027 01_case-study-degraded-model-performance.mp4

0030 02_data-and-concept-change-in-production-ml.mp4

0033 03_process-feedback-and-human-labeling.mp4

0034 04_data-labeling_quiz.html

0037 01_detecting-data-issues.mp4

0040 02_tensorflow-data-validation.mp4

0041 03_issues-in-training-data_quiz.html

0045 04_week-1-optional-references_instructions.html

0048 05_optional-downloading-your-notebook-and-refreshing-your-workspace_instructions.html

0053 01_introduction-to-preprocessing.mp4

0056 02_preprocessing-operations.mp4

0059 03_feature-engineering-techniques.mp4