MLOPS

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1.A conversation with Andrew Ng, Robert Crowe and Laurence Moroney
- 1.Course Overview.mp4
  
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2. Introduction to Model Serving
- 1.Introduction to Model Serving.mp4
  
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Lab
- link
  
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Quiz
- 1-2-3.png
  
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- 4.png
  
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3. Introduction to Model Serving Infrastructure
- 1.Introduction to Model Serving Infrastructure.mp4
  
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- 2.Deployment Options.mp4
  
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- 3.Improving Prediction Latency and Reducing Resource Costs.mp4
  
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- 4.Creating and deploying models to AI Prediction Platform.mp4
  
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Lab
- link
  
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Quiz
- 1-2-3.png
  
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- 4-5.png
  
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4.Installing TensorFlow Serving
- 1.Installing TensorFlow Serving.mp4
  
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Lab
- C4_W1_Lab_2_TF_Serving.ipynb
  
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- link
  
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Quiz
- Screenshot from 2023-03-01 19-03-04.png
  
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1.Model Serving Architecture
- 1.Model Serving Architecture.mp4
  
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- 2.Model Servers_ TensorFlow Serving.mp4
  
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- 3.Model Servers_ Other Providers.mp4
  
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Lab
- link
  
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Quiz
- Screenshot from 2023-03-01 19-06-31.png
  
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Lab_ Intro to Kubernetes
- link
  
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- 1.Scaling Infrastructure.mp4
  
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Quiz
- Screenshot from 2023-03-01 19-09-26.png
  
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3.Online Inference
- 1.Online Inference.mp4
  
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Lab - Latency testing with Docker Compose and Locust
- link
  
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Quiz
- Screenshot from 2023-03-01 19-12-29.png
  
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4.Data Preprocessing
- 1.Data Preprocessing.mp4
  
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Quiz
- Screenshot from 2023-03-01 19-15-46.png
  
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5.Batch Inference Scenarios
- 1.Batch Inference Scenarios.mp4
  
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Quiz
- Screenshot from 2023-03-01 19-16-28.png
  
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6.Batch Processing with ETL
- 1.Batch Processing with ETL.mp4
  
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Lab
- C4_W2_Lab_4_Apache_Beam_and_Tensorflow.ipynb
  
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Quiz
- Screenshot from 2023-03-01 19-18-18.png
  
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1.ML Experiments Management and Workflow Automation
- 1.Experiment Tracking.mp4
  
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- 2.Tools for Experiment Tracking.mp4
  
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- 3.Introduction to MLOps.mp4
  
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Quiz
- Screenshot from 2023-03-01 19-20-03.png
  
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2.MLOps Methodology
- 1.MLOps Level 0 .mp4
  
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- 2.MLOps Levels 1_2.mp4
  
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- 3.Developing Components for an Orchestrated Workflow.mp4
  
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Lab
- C4_W3_Lab_1_Kubeflow_Pipelines.ipynb
  
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- C4_W3_Lab_2_TFX_Custom_Components.ipynb
  
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Quiz
- Screenshot from 2023-03-01 19-22-23.png
  
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- Screenshot from 2023-03-01 19-22-29.png
  
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Lab
- link
  
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- 1.Managing Model Versions.mp4
  
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- 2.Continuous Delivery.mp4
  
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- 3.Progressive Delivery.mp4
  
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Quiz
- Screenshot from 2023-03-01 19-25-16.png
  
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- Screenshot from 2023-03-01 19-25-20.png
  
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1.Model Monitoring and Logging
- 1.Why Monitoring Matters.mp4
  
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- 2.Observability in ML.mp4
  
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- 3.Monitoring Targets in ML.mp4
  
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- 4.Logging for ML Monitoring.mp4
  
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- 5.Tracing for ML Systems.mp4
  
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Quiz
- Screenshot from 2023-03-01 19-28-27.png
  
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2.Model Decay
- 1.What is Model Decay_.mp4
  
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- 2.Model Decay Detection.mp4
  
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- 3.Ways to Mitigate Model Decay.mp4
  
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Quiz
- Screenshot from 2023-03-01 19-29-26.png
  
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- Screenshot from 2023-03-01 19-29-31.png
  
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3.GDPR and Privacy
- 1.Responsible AI.mp4
  
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- 2.Legal Requirements for Secure and Private AI.mp4
  
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- 3.Anonymization and Pseudonymisation .mp4
  
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- 4.Right to be Forgotten.mp4
  
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Quiz
- Screenshot from 2023-03-01 19-31-15.png
  
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- Screenshot from 2023-03-01 19-31-19.png
  
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4.Specialization recap and farewell
- 1.Specialization recap and farewell.mp4
  
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1. The Machine Learning Project Lifecycle
- 1. Specialization overview.mp4
  
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- 2. Welcome.mp4
  
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- 3. Steps of an ML Project.mp4
  
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- 4. Case study speech recognition.mp4
  
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- 5. Course outline.mp4
  
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Quiz
- 1-2.png
  
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- 3-4-5.png
  
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2. Development
- 1. Key challenges.mp4
  
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- 2. Deployment patterns.mp4
  
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- 3. Monitoring.mp4
  
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- 4. Pipeline monitoring.mp4
  
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Quiz
- 1-2-3.png
  
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- 4-5.png
  
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assets
- token.png
  
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- client.ipynb
  
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images
- apple.jpg
  
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- apples.jpg
  
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- car.jpg
  
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- car1.jpg
  
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- car2.jpg
  
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- car3.jpg
  
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- clock.jpg
  
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- clock2.jpg
  
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- clock3.jpg
  
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- fruits.jpg
  
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- oranges.jpg
  
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- README.md
  
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- server.ipynb
  
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- Week 1 Optional References _ Coursera.pdf
  
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1. Select and Training a Model
- 1. Modeling overview.mp4
  
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- 2. Key challenges.mp4
  
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- 3. Why low average error isn_t good enough.mp4
  
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- 4. Establish a baseline.mp4
  
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- 5. Tips for getting started.mp4
  
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Quiz
- 1-2.png
  
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- 3-4.png
  
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2. Error analysis and performance auditing
- 1. Error analysis example.mp4
  
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- 2. Prioritizing what to work on.mp4
  
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- 3. Skewed datasets.mp4
  
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- 4. Performance auditing.mp4
  
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3. Data iteration
- 1. Data-centric AI development.mp4
  
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- 2. A useful picture of data augmentation.mp4
  
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- 3. Data augmentation.mp4
  
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- 4. Can adding data hurt_.mp4
  
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- 5. Adding features.mp4
  
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- 6. Experiment tracking.mp4
  
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- 7. From big data to good data.mp4
  
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Quiz
- 1-2-3.png
  
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- 4-5.png
  
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Week 2 Lab
- C1W2_Ungraded_Lab_Birds_Cats_Dogs.ipynb
  
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history-augmented
- history-augmented.csv
  
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history-balanced
- history-balanced.csv
  
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history-imbalanced
- history-imbalanced.csv
  
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model-augmented
- saved_model.pb
  
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variables
- variables.data-00000-of-00001
  
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- variables.index
  
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model-balanced
- saved_model.pb
  
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variables
- variables.data-00000-of-00001
  
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- variables.index
  
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model-imbalanced
- saved_model.pb
  
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variables
- variables.data-00000-of-00001
  
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- variables.index
  
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sample_data
- anscombe.json
  
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- california_housing_test.csv
  
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- california_housing_train.csv
  
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- mnist_test.csv
  
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- mnist_train_small.csv
  
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- README.md
  
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- Week 2 Optional References _ Coursera.pdf
  
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1. Define Data and Establish Baseline
- 1. Why is data definition hard_.mp4
  
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- 2. More label ambiguity examples.mp4
  
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- 3. Major types of data problems.mp4
  
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- 4. Small data and label consistency.mp4
  
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- 5. Improving label consistency.mp4
  
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- 6. Human level performance (HLP).mp4
  
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- 7. Raising HLP.mp4
  
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2. Label and orgnaize Data
- 1. Obtaining data.mp4
  
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- 2. Data pipeline.mp4
  
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- 3. Meta-data, data provenance and lineage.mp4
  
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- 4. Balanced train_dev_test splits.mp4
  
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Quiz
- 1-2-3.png
  
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- 4.png
  
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- 5-6.png
  
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- 7-8.png
  
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- 9-10.png
  
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3. Scoping (optional)
- 1. What is scoping_.mp4
  
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- 2. Scoping process.mp4
  
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- 3. Diligence on feasibility and value.mp4
  
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- 4. Diligence on value.mp4
  
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- 5. Milestones and resourcing.mp4
  
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Quiz
- 1-2-3.png
  
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- 4-5.png
  
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- References _ Coursera.pdf
  
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assets
- iguanas1.png
  
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- iguanas2.png
  
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- iguanas3.png
  
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- C1W2_Ungraded_Lab_Redirect.ipynb
  
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- C1W3_Data_Labeling_Ungraded_Lab.ipynb
  
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data
- Youtube01-Psy.csv
  
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- Youtube02-KatyPerry.csv
  
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- Youtube03-LMFAO.csv
  
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- Youtube04-Eminem.csv
  
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- Youtube05-Shakira.csv
  
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Week 1
- 0. Course Overview.mp4
  
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- 0. Specialization overview.mp4
  
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1. Introduction to Machine Learning Engineering in Production
- 1. Overview.mp4
  
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- 2. ML Pipelines.mp4
  
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Quiz
- 1-2-3.png
  
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- 4-5-6-7.png
  
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- 8-9-10.png
  
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2. Collecting data
- 1. Importance of Data.mp4
  
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- 2. Example Application Suggesting Runs.mp4
  
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- 3. Responsible Data Security, Privacy _ Fairness.mp4
  
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Quiz
- 1-2-3-4.png
  
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- 5-6-7.png
  
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3. Labeling Data
- 1. Case Study Degraded Model Performance.mp4
  
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- 2. Data and Concept Change in Production ML.mp4
  
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- 3. Process Feedback and Human Labeling.mp4
  
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Quiz
- 1-2-3.png
  
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4. Validating Data
- 1. Detecting Data.mp4
  
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- 2. TensorFlow Data Validation.mp4
  
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Quiz
- 1-2.png
  
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- 3-4.png
  
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Assignments
- C2W1_Assignment.ipynb
  
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dataset_diabetes
- diabetic_data.csv
  
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- IDs_mapping.csv
  
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Week 1 Lab
- C2_W1_Lab_1_TFDV_Exercise.ipynb
  
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data
- adult.data
  
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img
- tfdv.png
  
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- util.py
  
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- Week 1 Optional References _ Coursera.pdf
  
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1. Feature Engineering
- 1. Introduction to Preprocessing.mp4
  
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- 2. Preprocessing Operations.mp4
  
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- 3. Feature Engineering Techniques.mp4
  
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- 4. Feature Crosses.mp4
  
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2. Feature Transformation at Scale
- 1. Preprocessing Data at Scale.mp4
  
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- 2. TensorFlow Transform.mp4
  
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- 3. Hello World with tf.Transform.mp4
  
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Quiz
- 1-2-3.png
  
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- 4-5-6.png
  
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3. Feature Selection
- 1. Feature Spaces.mp4
  
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- 2. Feature Selection.mp4
  
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- 3. Filter Methods.mp4
  
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- 4. Wrapper Methods.mp4
  
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- 5. Embedded Methods.mp4
  
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Assignments
- C2W2_Assignment.ipynb
  
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data
- metro_traffic_volume.csv
  
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Week 2 Lab
- C2_W2_Lab_1_Simple_Feature_Engineering.ipynb
  
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- C2_W2_Lab_2_Feature_Engineering_Pipeline.ipynb
  
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- C2_W2_Lab_3_Feature_Selection.ipynb
  
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data
- breast_cancer_data.csv
  
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census_data
- adult.data
  
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- data.zip
  
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img
- feature_eng_pipeline.png
  
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- Week 2 Optional References _ Coursera.pdf
  
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1. Data Journey and Data Storage
- 1. Data Journey.mp4
  
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- 2. Introduction to ML Metadata.mp4
  
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- 3. ML Metadata in Action.mp4
  
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Quiz
- 1-2-3.png
  
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2. Evolving Data
- 1. Schema Developments.mp4
  
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- 2. Schema Environments.mp4
  
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Quiz
- 1-2-3.png
  
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3. Enterprise Data Storage
- 1. Feature Stores.mp4
  
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- 2. Data Warehouse.mp4
  
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- 3. Data Lakes.mp4
  
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Quiz
- 1-2.png
  
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- 3-4.png
  
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Assignments
- C2W3_Assignment.ipynb
  
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- util.py
  
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Week 3 Lab
- C2_W3_Lab_1_MLMetadata.ipynb
  
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- C2_W3_Lab_2_IterativeSchema.ipynb
  
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census_data
- adult.data
  
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images
- mlmd_overview.png
  
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- Week 3 Optional References _ Coursera.pdf
  
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1. Advanced Labeling (optional)
- 1. Semi-supervised Learning.mp4
  
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- 2. Active Learning.mp4
  
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- 3. Weak Supervision.mp4
  
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Quiz
- 1-2-3.png
  
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2. Data Augmentation (Optional)
- Data Augmentation.mp4
  
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Quiz
- 1-2-3.png
  
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3. Preprocessing Different Data Type (Optional)
- 1. Time Series.mp4
  
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- 2. Sensors and Signals.mp4
  
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Quiz
- 1-2-3.png
  
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Week 4 Lab
- C2_W4_Lab_1_WeatherData.ipynb
  
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- C2_W4_Lab_2_Signals.ipynb
  
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- C2_W4_Lab_3_Images.ipynb
  
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- Week 4 Optional References _ Coursera.pdf
  
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Week 1
- 0. Course Overview.mp4
  
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1. Hyperparameter tuning searching for the best architecture
- Hyperparameter Tuning.mp4
  
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- Keras Autotuner Demo.mp4
  
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2. Auto ML
- 1. Intro to AutoML.mp4
  
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- 2. Understanding Search Spaces.mp4
  
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- 4. Search Strategies.mp4
  
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- 5. Measuring AutoML Efficacy.mp4
  
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- 6. AutoML on the Cloud.mp4
  
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- 7. Assignment Setup.mp4
  
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Quiz
- 1-2-3.png
  
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- 4-5-6.png
  
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- 7-8-9.png
  
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- A Tour of Qwiklabs and Google Cloud _ Qwiklabs.mht
  
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- AutoML _ Coursera.pdf
  
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- Classify Images of Clouds in the Cloud with AutoML Vision _ Qwiklabs.mht
  
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- Neural Architecture Search _ Coursera.pdf
  
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Quiz
- 1-2-3.png
  
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Week 1 Lab
- C3_W1_Lab_1_Keras_Tuner.ipynb
  
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- C3_W1_Lab_2_TFX_Tuner_and_Trainer.ipynb
  
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1. Dimentionality Reduction
- 2. Curse of Dimensionality.mp4
  
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- 2. Dimensionality Effect on Performance.mp4
  
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- 3. Curse of Dimensionality an Example.mp4
  
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- 4. Manual Dimensionality Reduction.mp4
  
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- 5. Manual Dimensionality Reduction Case Study.mp4
  
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- 6. Algorithmic Dimensionality Reduction.mp4
  
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- 7. Principal Components Analysis.mp4
  
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- 8. Other Techniques.mp4
  
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- Dimensionality Reduction Techniques _ Coursera.pdf
  
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untitled folder
- 1.png
  
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- 2.png
  
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- 3-4-5-6.png
  
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2. Quantization and Pruning
- 1. Mobile, IoT, and Similar Use Cases.mp4
  
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- 2. Benefits and Process of Quantization.mp4
  
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- 3. Post Training Quantization.mp4
  
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- 4. Quantization Aware Training.mp4
  
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- 5. Pruning.mp4
  
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- Pruning _ Coursera.pdf
  
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- Quantization _ Coursera.pdf
  
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Quiz
- 1-2-3.png
  
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- 4-5.png
  
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- 6-7.png
  
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Week 2 Lab
- C3_W2_Lab_1_Manual_Dimensionality.ipynb
  
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- C3_W2_Lab_2_Algorithmic_Dimensionality.ipynb
  
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- C3_W2_Lab_3_Quantization_and_Pruning.ipynb
  
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1. High-Performance Modeling
- 1. Distributed Training.mp4
  
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- 2. High-Performance Ingestion.mp4
  
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- 3. Training Large Models - The Rise of Giant Neural Nets and Parallelism.mp4
  
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- Distributed Multi-worker TensorFlow Training on Kubernetes _ Qwiklabs.mht
  
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- High-Performance Modeling _ Coursera.pdf
  
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Quiz
- 1-2-3.png
  
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- 4-5-6.png
  
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2. Knowledge Distillation
- 1. Teacher and Student Networks.mp4
  
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- 2. Knowledge Distillation Techniques.mp4
  
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- 3. Case Study - How to Distill Knowledge for a Q_A Task.mp4
  
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- Knowledge Distillation _ Coursera.pdf
  
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Quiz
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- 5-6.png
  
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- 7.png
  
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Week 3 Lab
- C3_W3_Lab_1_Distributed_Training.ipynb
  
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- C3_W3_Lab_2_Knowledge_Distillation.ipynb
  
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1. Model Analysis Overview
- 1-2-3.png
  
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- 1. Model Performance Analysis.mp4
  
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- TensorBoard _ Coursera.pdf
  
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2. Advanced Model Analysis and Debugging
- 1. Introduction to TensorFlow Model Analysis.mp4
  
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- 2. TFMA in Practice.mp4
  
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- 3. Model Debugging Overview.mp4
  
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- 4. Benchmark Models.mp4
  
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- 5. Sensitivity Analysis and Adversarial Attacks.mp4
  
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- 6. Adversarial Attack Demo.mp4
  
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- 7. Residual Analysis.mp4
  
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- 8. Model Remediation.mp4
  
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- 9. Fairness.mp4
  
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- 10. Measuring Fairness.mp4
  
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- Identifying Bias in Mortgage Data using Cloud AI Platform and the What-if Tool _ Qwiklabs.mht
  
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- Model Remediation and Fairness _ Coursera.pdf
  
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Quiz
- 1-2.png
  
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- 3-4.png
  
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- 5-6.png
  
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- Sensitivity Analysis and Adversarial Attacks _ Coursera.pdf
  
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- TensorFlow Model Analysis _ Coursera.pdf
  
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3. Continuous Evaluation and Monitoring
- Continuous Evaluation and Monitoring _ Coursera.pdf
  
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- Continuous Evaluation and Monitoring.mp4
  
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Quiz
- 1-2-3.png
  
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- 4-5.png
  
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- 6.png
  
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Week 4 Lab
- C3_W4_Lab_1_TFMA.ipynb
  
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- C3_W4_Lab_2_TFX_Evaluator.ipynb
  
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- C3_W4_Lab_3_Fairness_Indicators.ipynb
  
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Explainable AI
- 1-2-3.png
  
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- Explainable AI _ Coursera.pdf
  
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- Explainable AI.mp4
  
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Interpretability
- 1-2-3.png
  
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- 1. Model Interpretation Methods.mp4
  
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- 2. Intrinsically Interpretable Models.mp4
  
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- Interpretability _ Coursera.pdf
  
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Understanding Model Predictions
- 1-2-3-4.png
  
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- 1. Model Agnostic Methods.mp4
  
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- 2. Partial Dependence Plots.mp4
  
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- 3. Shapley Values.mp4
  
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- 4. SHapley Additive exPlanations (SHAP).mp4
  
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- 5. Testing Concept Activation Vectors.mp4
  
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- 6. LIME.mp4
  
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- 7. AI Explanations.mp4
  
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- AI Explanations _ Coursera.pdf
  
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- Permutation Feature Importance _ Coursera.pdf
  
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- TCAV and LIME _ Coursera.pdf
  
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- Understanding Model Predictions _ Coursera.pdf
  
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Week 5 Lab
- C3_W5_Lab_1_Shap_Values.ipynb
  
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- C3_W5_Lab_2_Permutation_Importance.ipynb
  
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