https://github.com/edaaydinea/mlops
This repository includes all materials and project solutions for MLOps Specialization prepared by DeepLearning.AI.
https://github.com/edaaydinea/mlops
machine-learning-production mlops production
Last synced: 7 months ago
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This repository includes all materials and project solutions for MLOps Specialization prepared by DeepLearning.AI.
- Host: GitHub
- URL: https://github.com/edaaydinea/mlops
- Owner: edaaydinea
- License: apache-2.0
- Created: 2023-04-05T23:26:06.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-10T15:59:27.000Z (about 1 year ago)
- Last Synced: 2024-09-10T23:57:48.782Z (about 1 year ago)
- Topics: machine-learning-production, mlops, production
- Language: Jupyter Notebook
- Homepage:
- Size: 4.64 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Machine Learning Engineering for Production (MLOps) Specialization
* **Where:** Coursera
* **University/Institute:** Deeplearning.AI
* **Status:** In Progress
## Courses in this Specialization
Course 1: Machine Learning in Production
* **Status:** Completed
* **Link:** \<>
Course 2: Machine Learning Data Lifecycle in Production
* **Status:** Completed
* **Link:** \<>
Course 3: Machine Learning Modeling Pipelines in Production
* **Status:** Completed
* **Link:** \<\<>
Course 4: Deploying Machine Learning Models in Production
* **Status:** In progress
* **Link:** \<>
---
## Courses
### Course 1: Machine Learning in Production
**Week1: Overview of the ML Lifecycle and Deployment**
* [Week 1 Notes](L1/W1/LectureNotes.md)
**Week2: Select and Train a Model**
* [Week 2 Notes](L1/W2/LectureNote.md)
* [Week 2 - Ungraded Lab: A journey through data](/L1/W2/C1W2_Ungraded_Lab_Birds_Cats_Dogs.ipynb)
**Week3: Data Definition and Baseline**
* [Week 3 Notes](L1/W3/LectureNotes.md)
* [Week 3 - Ungraded Lab: Data Labeling](/L1/W3/C1W3_Data_Labeling_Ungraded_Lab.ipynb)
---
### Course 2: Machine Learning Data Lifecycle in Production
**Week 1: Collecting, Labeling and Validating Data**
* [Week 1 Notes](L2/W1/lecture_note.ipynb)
* [Ungraded Lab: TFDV Exercise](L2/W1/C2_W1_Lab_1_TFDV_Exercise.ipynb)
* [Week 1 Assignment - Data Validation](L2/W1/C2W1_Assignment.ipynb)
**Week 2: Feature Engineering, Transformation and Selection**
* [Week 2 Notes](L2/W2/lecture_note.ipynb)
* [Ungraded Lab: Simple Feature Engineering](L2/W2/C2_W2_Lab_1_Simple_Feature_Engineering.ipynb)
* [Ungraded Lab: Feature Engineering Pipeline](L2/W2/C2_W2_Lab_2_Feature_Engineering_Pipeline.ipynb)
* [Ungraded Lab: Feature Selection](L2/W2/C2_W2_Lab_3_Feature_Selection.ipynb)
* [Week 2 Assignment - Feature Engineering](L2/W2/C2W2_Assignment.ipynb)
**Week 3: Data Journey and Data Storage**
* [Week 3 Notes](L2/W3/lecture_note.ipynb)
* [Ungraded Lab: ML Metadata](L2/W3/C2_W3_Lab_1_MLMetadata.ipynb)
* [Ungraded Lab: Iterative Schema](L2/W3/C2_W3_Lab_2_IterativeSchema.ipynb)
* [Week 3 Assignment - Data Pipeline Components for Production ML](L2/W3/C2W3_Assignment.ipynb)
**Week 4 (Optional): Advanced Labeling, Augmentation and Data Preprocessing**
* [Week 4 Notes](L2/W4/lecture_note.ipynb)
* [Ungraded Lab: Feature Engineering with Weather Data](L2/W4/C2_W4_Lab_1_WeatherData.ipynb)
* [Ungraded Lab: Feature Engineering with Accelerometer Data](L2/W4/C2_W4_Lab_2_Signals.ipynb)
* [Ungraded Lab: Feature Engineering with Images](L2/W4/C2_W4_Lab_3_Images.ipynb)
---
### Course 3: Machine Learning Modeling Pipelines in Production
**Week 1: Neural Architecture Search**
* [Week 1 Notes](L3/W1/lecture_note.ipynb)
* [Ungraded Lab: Intro to Keras Tuner](L3/W1/C3_W1_Lab_1_Keras_Tuner.ipynb)
* [Ungraded Lab - Hyperparameter Tuning and Model Training with TFX](L3/W1/C3_W1_Lab_2_TFX_Tuner_and_Trainer.ipynb)
**Week 2: Model Resource Management Techniques**
* [Week 2 Notes](L3/W2/lecture_note.ipynb)
**Week 3: High-Performance Modeling**
* [Week 3 Notes](L3/W3/lecture_note.ipynb)
**Week 4: Model Analysis**
* [Week 4 Notes](L3/W4/lecture_note.ipynb)
* [Ungraded Lab: TensorFlow Model Analysis](L3/W4/C3_W4_Lab_1_TFMA.ipynb)
* [Ungraded Lab: Model Analysis with TFX Evaluator](L3/W4/C3_W4_Lab_2_TFX_Evaluator.ipynb)
* [Ungraded Lab: Fairness Indicators](L3/W4/C3_W4_Lab_3_Fairness_Indicators.ipynb)
**Week 5: Interpretability**
* [Week 5 Notes](L3/W5/lecture_note.ipynb)
* [Ungraded Lab: Shapley Values](L3/W5/C3_W5_Lab_1_Shap_Values.ipynb)
* [Ungraded Lab: Permutation Feature Importance](L3/W5/C3_W5_Lab_2_Permutation_Importance.ipynb)
---
### Course 4: Deploying Machine Learning Models in Production
**Week 1: Model Serving: Introduction**
* [Week 1 Notes](L4/W1/lecture_note.ipynb)
**Week 2: Model Serving: Patterns and Infrastructure**
* [Week 2 Notes](L4/W2/lecture_note.ipynb)
**Week 3: Model Management and Delivery**
* [Week 3 Notes](L4/W3/lecture_note.ipynb)
* [Ungraded Lab: Building ML Pipelines with Kubeflow](L4/W3/C4_W3_Lab_1_Kubeflow_Pipelines.ipynb)
* [Ungraded Lab: Developing Custom TFX Components](L4/W3/C4_W3_Lab_2_TFX_Custom_Components.ipynb)
**Week 4: Model Monitoring and Logging**
* [Week 4 Notes](L4/W4/lecture_note.ipynb)
---
## Certificates
* [**Course 1: Machine Learning in Production**](https://coursera.org/share/864eb76cce20548d9968d9ad06cf5fe0)
* [**Course 2: Machine Learning Data Lifecycle in Production**](https://coursera.org/share/c5f6f626e56f2feca4d1b5bc55e1d25a)
* [**Course 3: Machine Learning Modeling Pipelines in Production**](https://coursera.org/share/0ca2af8bdafa6773b84e3144dbe9db61)
* [**Course 4: Deploying Machine Learning Models in Production**](https://coursera.org/share/02d7215d8789adb2eba05713415c142d)
* [**Specialization Certificate**](https://coursera.org/share/3cc6bb943f70861830a92f149f4883c1)