[Udemy] Deployment of Machine Learning Models (2020) [En]

Creation Time	Sept. 19, 2024, 11:36 a.m.
Last Access Time	Dec. 22, 2024, 9:33 p.m.
File Size	3.6 GB
Keywords	En Models of Udemy Machine 2020 Learning Deployment
Total Requests	12
Total Files	298
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4. Building a Reproducible Machine Learning Pipeline/3. Designing a Custom Pipeline.mp4 152.9 MB
2. Machine Learning Pipeline - Research Environment/6. Data Analysis - Demo.mp4 135.4 MB
2. Machine Learning Pipeline - Research Environment/7. Feature Engineering - Demo.mp4 98.2 MB
13. A Deep Learning Model with Big Data/3. Building a CNN in the Research Environment.mp4 88.8 MB
4. Building a Reproducible Machine Learning Pipeline/2. Procedural Programming Pipeline.mp4 86.2 MB
4. Building a Reproducible Machine Learning Pipeline/5. Third Party Pipeline Create Scikit-Learn compatible Feature Transformers.mp4 84.3 MB
13. A Deep Learning Model with Big Data/4. Production Code for a CNN Learning Pipeline.mp4 79.5 MB
5. Course Setup and Key Tools/9. Section5.5b - Virtualenv Introduction.mp4 79.3 MB
7. Serving the model via REST API/7. 7.6 - API Schema Validation.mp4 78.1 MB
3. Machine Learning System Architecture/5. Building a Reproducible Machine Learning Pipeline.mp4 77.1 MB
6. Creating a Machine Learning Pipeline Application/9. 6.8 - Building the Package.mp4 75.9 MB
13. A Deep Learning Model with Big Data/8. 13.8 - Packaging the CNN.mp4 71.9 MB
6. Creating a Machine Learning Pipeline Application/8. 6.7 - Versioning and Logging.mp4 70.4 MB
8. Continuous Integration and Deployment Pipelines/4. 8.4 - Publishing the Model to Gemfury.mp4 69.1 MB
2. Machine Learning Pipeline - Research Environment/10. Getting Ready for Deployment - Demo.mp4 67.8 MB
2. Machine Learning Pipeline - Research Environment/1. Machine Learning Pipeline Overview.mp4 60.5 MB
12. Deploying to IaaS (AWS ECS)/12. 12.11 - Creating the ECS Cluster with the EC2 Launch Method.mp4 59.9 MB
2. Machine Learning Pipeline - Research Environment/2. Machine Learning Pipeline Feature Engineering.mp4 57.7 MB
4. Building a Reproducible Machine Learning Pipeline/4. Leveraging a Third Party Pipeline Scikit-Learn.mp4 56.7 MB
8. Continuous Integration and Deployment Pipelines/3. 8.3 - Setup Circle CI Config.mp4 50.8 MB
9. Differential Testing/2. 9.2 - Setting up Differential Tests.mp4 50.2 MB
8. Continuous Integration and Deployment Pipelines/5. 8.5 - Testing the CI Pipeline.mp4 50.1 MB
12. Deploying to IaaS (AWS ECS)/10. 12.9 - Uploading Images to the Elastic Container Registry (ECR).mp4 49.8 MB
2. Machine Learning Pipeline - Research Environment/3. Machine Learning Pipeline Feature Selection.mp4 48.4 MB
1. Introduction/2. Course curriculum overview.mp4 48.2 MB
11. Running Apps with Containers (Docker)/5. 11.5 Releasing to Heroku with Docker.mp4 46.9 MB
6. Creating a Machine Learning Pipeline Application/2. 6.2 - Training the Model.mp4 46.2 MB
6. Creating a Machine Learning Pipeline Application/3. 6.3 - Connecting the Pipeline.mp4 45.3 MB
6. Creating a Machine Learning Pipeline Application/5. 6.4 - Making Predictions with the Model.mp4 44.7 MB
8. Continuous Integration and Deployment Pipelines/1.1 section8.1.mp4.mp4 41.9 MB
13. A Deep Learning Model with Big Data/9. 13.9 - Adding the CNN to the API.mp4 41.3 MB
3. Machine Learning System Architecture/2. Specific Challenges of Machine Learning Systems.mp4 39.1 MB
7. Serving the model via REST API/5. 7.4 - Adding the Prediction Endpoint.mp4 38.9 MB
12. Deploying to IaaS (AWS ECS)/11. 12.10 - Creating the ECS Cluster with Fargate Launch Method.mp4 38.1 MB
5. Course Setup and Key Tools/3. Section 5.3 - How to Use the Course Resources, Monorepos + Git Refresher.mp4 37.8 MB
1. Introduction/1. Introduction to the course.mp4 37.6 MB
5. Course Setup and Key Tools/4. Section5.3b - Opening Pull Requests.mp4 36.2 MB
2. Machine Learning Pipeline - Research Environment/8. Feature Selection - Demo.mp4 35.4 MB
7. Serving the model via REST API/2. 7.2 - Creating the API Skeleton.mp4 35.3 MB
2. Machine Learning Pipeline - Research Environment/9. Model Building - Demo.mp4 34.1 MB
9. Differential Testing/3. 9.3 - Differential Tests in CI (Part 1 of 2).mp4 33.6 MB
7. Serving the model via REST API/4. 7.3 - Adding Config and Logging.mp4 33.0 MB
9. Differential Testing/4. 9.4 - Differential Tests in CI (Part 2 of 2).mp4 32.8 MB
6. Creating a Machine Learning Pipeline Application/6. 6.5 - Data Validation in the Model Package.mp4 32.5 MB
10. Deploying to a PaaS (Heroku) without Containers/3. 10.3 - Heroku Config.mp4 32.2 MB
11. Running Apps with Containers (Docker)/1. 11.1 Introduction to Containers and Docker.mp4 31.5 MB
5. Course Setup and Key Tools/13. Section 5.7 - Engineering and Python Best Practices.mp4 31.4 MB
12. Deploying to IaaS (AWS ECS)/13. 12.12 - Updating the Cluster Containers.mp4 31.0 MB
3. Machine Learning System Architecture/3. Machine Learning System Approaches.mp4 30.0 MB
3. Machine Learning System Architecture/4. Machine Learning System Component Breakdown.mp4 29.5 MB
10. Deploying to a PaaS (Heroku) without Containers/5. 10.5 - Deploying to Heroku via CI.mp4 29.1 MB
12. Deploying to IaaS (AWS ECS)/7. 12.6 - Installing the AWS CLI.mp4 28.9 MB
4. Building a Reproducible Machine Learning Pipeline/8. Bonus Should feature selection be part of the pipeline.mp4 28.8 MB
8. Continuous Integration and Deployment Pipelines/1. 8.1 - Introduction to CICD.mp4 28.4 MB
5. Course Setup and Key Tools/2. Section 5.2 - Installing and Configuring Git.mp4 27.8 MB
10. Deploying to a PaaS (Heroku) without Containers/1. 10.1 - Introduction.mp4 26.9 MB
11. Running Apps with Containers (Docker)/4. 11.4 Building and Running the Docker Container.mp4 26.7 MB
11. Running Apps with Containers (Docker)/2. 11.2 Installing Docker.mp4 26.7 MB
6. Creating a Machine Learning Pipeline Application/7. 6.6 - Feature Engineering in the Pipeline.mp4 26.2 MB
12. Deploying to IaaS (AWS ECS)/2. 12.2 - AWS Costs and Caution.mp4 25.5 MB
2. Machine Learning Pipeline - Research Environment/11. Bonus Machine Learning Pipeline Additional Resources.mp4 25.4 MB
5. Course Setup and Key Tools/11. Section5.5d - Virtualenv refresher.mp4 25.4 MB
7. Serving the model via REST API/1. 7.1 - Introduction.mp4 25.0 MB
12. Deploying to IaaS (AWS ECS)/4. 12.3b - Container Orchestration Options Kubernetes, ECS, Docker Swarm.mp4 23.8 MB
12. Deploying to IaaS (AWS ECS)/15. 12.14 - Deploying to ECS via the CI pipeline.mp4 23.5 MB
12. Deploying to IaaS (AWS ECS)/6. 12.5 - Setting Permissions with IAM.mp4 23.2 MB
12. Deploying to IaaS (AWS ECS)/3. 12.3a - Intro to AWS ECS.mp4 22.8 MB
5. Course Setup and Key Tools/7. Section 5.4b - System Path and Pythonpath Demo.mp4 21.8 MB
11. Running Apps with Containers (Docker)/3. 11.3 Creating Our API App Dockerfile.mp4 21.6 MB
10. Deploying to a PaaS (Heroku) without Containers/2. 10.2 - Heroku Account Creation.mp4 20.9 MB
12. Deploying to IaaS (AWS ECS)/8. 12.7 - Configuring the AWS CLI.mp4 20.8 MB
13. A Deep Learning Model with Big Data/10. 13.10 - Additional Considerations and Wrap Up.mp4 20.8 MB
4. Building a Reproducible Machine Learning Pipeline/1. Production Code overview.mp4 19.2 MB
12. Deploying to IaaS (AWS ECS)/1. 12.1 - Introduction to AWS.mp4 18.7 MB
9. Differential Testing/1. 9.1 - Introduction.mp4 18.6 MB
5. Course Setup and Key Tools/5. Section5.3c - Primer on Monorepos.mp4 18.3 MB
2. Machine Learning Pipeline - Research Environment/4. Machine Learning Pipeline Model Building.mp4 17.8 MB
7. Serving the model via REST API/3. 7.2b - Flask Crash Course.mp4 17.8 MB
1. Introduction/3. Knowledge requirements.mp4 17.2 MB
13. A Deep Learning Model with Big Data/5. Reproducibility in Neural Networks.mp4 17.0 MB
13. A Deep Learning Model with Big Data/2. Introduction to a Large Dataset - Plant Seedlings Images.mp4 16.7 MB
5. Course Setup and Key Tools/1. Section 5.1 - Introduction.mp4 15.9 MB
7. Serving the model via REST API/6. 7.5 - Adding a Version Endpoint.mp4 15.4 MB
13. A Deep Learning Model with Big Data/1. Challenges of using Big Data in Machine Learning.mp4 15.3 MB
6. Creating a Machine Learning Pipeline Application/1. 6.1 - Introduction.mp4 14.3 MB
10. Deploying to a PaaS (Heroku) without Containers/6. 10.6 - Wrap Up.mp4 13.5 MB
1. Introduction/6.1 DMLM_Slides.zip.zip 13.4 MB
6. Creating a Machine Learning Pipeline Application/10. 6.9 - Wrap Up.mp4 13.2 MB
9. Differential Testing/5. 9.5 Wrap Up.mp4 12.7 MB
5. Course Setup and Key Tools/12. Section 5.6 - Text Editors IDEs.mp4 12.4 MB
10. Deploying to a PaaS (Heroku) without Containers/4. 10.4 - Testing the Deployment Manually.mp4 12.3 MB
3. Machine Learning System Architecture/1. Machine Learning System Architecture and Why it Matters.mp4 10.9 MB
8. Continuous Integration and Deployment Pipelines/2. 8.2 - Setting up CircleCI.mp4 10.6 MB
4. Building a Reproducible Machine Learning Pipeline/6. Third Party Pipeline Closing Remarks.mp4 10.0 MB
5. Course Setup and Key Tools/10. Section5.5c - Requirements files Introduction.mp4 9.5 MB
12. Deploying to IaaS (AWS ECS)/9. 12.8 - Intro the Elastic Container Registry (ECR).mp4 9.4 MB
12. Deploying to IaaS (AWS ECS)/16. 12.15 - Wrap Up.mp4 9.0 MB
5. Course Setup and Key Tools/6. Section 5.4a - Operating System Differences and Gotchas.mp4 8.2 MB
5. Course Setup and Key Tools/8. Section 5.5a - Quick Word for More Advanced Students.mp4 8.2 MB
11. Running Apps with Containers (Docker)/6. 11.6 - Wrap Up.mp4 7.7 MB
12. Deploying to IaaS (AWS ECS)/14. 12.13 - Tearing down the ECS Cluster.mp4 7.0 MB
7. Serving the model via REST API/8. 7.7 - Wrap Up.mp4 6.3 MB
5. Course Setup and Key Tools/14. Section 5.8 - Wrap Up.mp4 6.0 MB
8. Continuous Integration and Deployment Pipelines/6. 8.6 - Wrap Up.mp4 5.3 MB
12. Deploying to IaaS (AWS ECS)/5. 12.4 - Create an AWS Account.mp4 4.9 MB
1. Introduction/7.1 DMLM_Notes.zip.zip 1.5 MB
13. A Deep Learning Model with Big Data/3.1 CNN_Analysis_and Model.zip.zip 1.5 MB
2. Machine Learning Pipeline - Research Environment/5.1 MLPipeline-Notebooks.zip.zip 1.2 MB
14. Common Issues found during deployment/1.1 Troubleshooting.pdf.pdf 223.4 KB
7. Serving the model via REST API/2.1 Section7.2_Notes.pdf.pdf 146.3 KB
8. Continuous Integration and Deployment Pipelines/4.1 Section8.4_Notes.pdf.pdf 100.8 KB
9. Differential Testing/4.1 Section9.4_Notes.pdf.pdf 100.6 KB
5. Course Setup and Key Tools/4.1 Section5.3b_Notes.pdf.pdf 99.6 KB
6. Creating a Machine Learning Pipeline Application/4.1 Section6.4_Notes.pdf.pdf 98.8 KB
6. Creating a Machine Learning Pipeline Application/5.1 Section6.4_Notes.pdf.pdf 98.8 KB
5. Course Setup and Key Tools/2.1 Section5.2_Notes.pdf.pdf 96.5 KB
11. Running Apps with Containers (Docker)/4.1 Section11.4_Notes.pdf.pdf 94.1 KB
5. Course Setup and Key Tools/9.1 Section5.5b_Notes.pdf.pdf 92.2 KB
5. Course Setup and Key Tools/13.1 Section5.7_Notes.pdf.pdf 88.9 KB
8. Continuous Integration and Deployment Pipelines/5.1 Section8.5_Notes.pdf.pdf 88.8 KB
6. Creating a Machine Learning Pipeline Application/9.1 Section6.8_Notes.pdf.pdf 86.0 KB
5. Course Setup and Key Tools/3.1 Section5.3a_Notes.pdf.pdf 85.5 KB
5. Course Setup and Key Tools/12.1 Section5.6_Notes.pdf.pdf 84.9 KB
7. Serving the model via REST API/6.1 Section7.5_Notes.pdf.pdf 84.3 KB
5. Course Setup and Key Tools/11.1 Section5.5_Notes.pdf.pdf 83.8 KB
7. Serving the model via REST API/7.1 Section7.6_Notes.pdf.pdf 83.7 KB
7. Serving the model via REST API/4.1 Section7.3_Notes.pdf.pdf 83.1 KB
12. Deploying to IaaS (AWS ECS)/8.1 Section12.7_Notes.pdf.pdf 83.0 KB
11. Running Apps with Containers (Docker)/6.1 Section11.6_Notes.pdf.pdf 82.4 KB
7. Serving the model via REST API/3.1 Section7.2b_Notes.pdf.pdf 81.6 KB
7. Serving the model via REST API/5.1 Section7.4_Notes.pdf.pdf 81.5 KB
6. Creating a Machine Learning Pipeline Application/8.1 Section6.7_Notes.pdf.pdf 81.3 KB
6. Creating a Machine Learning Pipeline Application/6.1 Section6.5_Notes.pdf.pdf 79.3 KB
6. Creating a Machine Learning Pipeline Application/7.1 Section6.6_Notes.pdf.pdf 78.9 KB
3. Machine Learning System Architecture/4.1 Section3.4_Notes.pdf.pdf 78.8 KB
11. Running Apps with Containers (Docker)/2.1 Section11.2_Notes.pdf.pdf 77.8 KB
10. Deploying to a PaaS (Heroku) without Containers/1.1 Section10.1_Notes.pdf.pdf 76.7 KB
6. Creating a Machine Learning Pipeline Application/3.1 Section6.3_Notes.pdf.pdf 75.4 KB
12. Deploying to IaaS (AWS ECS)/2.1 Section12.2_Notes.pdf.pdf 74.7 KB
5. Course Setup and Key Tools/10.1 Section5.5c_Notes.pdf.pdf 73.8 KB
12. Deploying to IaaS (AWS ECS)/10.1 Section12.9_Notes.pdf.pdf 72.4 KB
13. A Deep Learning Model with Big Data/8.1 Section13.8_Notes.pdf.pdf 71.6 KB
3. Machine Learning System Architecture/3.1 Section3.3_Notes.pdf.pdf 71.0 KB
11. Running Apps with Containers (Docker)/1.1 Section11.1_Notes.pdf.pdf 70.2 KB
10. Deploying to a PaaS (Heroku) without Containers/4.1 Section10.4_Notes.pdf.pdf 69.8 KB
9. Differential Testing/5.1 Section9.5_Notes.pdf.pdf 69.5 KB
10. Deploying to a PaaS (Heroku) without Containers/3.1 Section10.3_Notes.pdf.pdf 69.0 KB
10. Deploying to a PaaS (Heroku) without Containers/5.1 Section10.5_Notes.pdf.pdf 67.9 KB
12. Deploying to IaaS (AWS ECS)/13.1 Section12.12_Notes.pdf.pdf 67.5 KB
9. Differential Testing/2.1 Section9.2_Notes.pdf.pdf 64.9 KB
5. Course Setup and Key Tools/6.1 Section5.4_Notes.pdf.pdf 64.5 KB
5. Course Setup and Key Tools/7.1 Section5.4_Notes.pdf.pdf 64.5 KB
12. Deploying to IaaS (AWS ECS)/15.1 Section12.14_Notes.pdf.pdf 64.2 KB
10. Deploying to a PaaS (Heroku) without Containers/6.1 Section10.6_Notes.pdf.pdf 63.9 KB
8. Continuous Integration and Deployment Pipelines/3.1 Section8.3_Notes.pdf.pdf 63.8 KB
7. Serving the model via REST API/1.1 Section7.1_Notes.pdf.pdf 63.4 KB
10. Deploying to a PaaS (Heroku) without Containers/2.1 Section10.2_Notes.pdf.pdf 61.4 KB
12. Deploying to IaaS (AWS ECS)/14.1 Section12.13_Notes.pdf.pdf 60.1 KB
13. A Deep Learning Model with Big Data/10.1 Section13.10_Notes.pdf.pdf 60.0 KB
8. Continuous Integration and Deployment Pipelines/2.1 Section8.2_Notes.pdf.pdf 58.8 KB
11. Running Apps with Containers (Docker)/3.1 Section11.3_Notes.pdf.pdf 58.5 KB
12. Deploying to IaaS (AWS ECS)/6.1 Section12.5_Notes.pdf.pdf 57.9 KB
12. Deploying to IaaS (AWS ECS)/12.1 Section12.11_Notes.pdf.pdf 57.5 KB
12. Deploying to IaaS (AWS ECS)/7.1 Section12.6_Notes.pdf.pdf 56.9 KB
12. Deploying to IaaS (AWS ECS)/11.1 Section12.10_Notes.pdf.pdf 56.9 KB
12. Deploying to IaaS (AWS ECS)/16.1 Section12.15_Notes.pdf.pdf 56.6 KB
11. Running Apps with Containers (Docker)/5.1 Section11.5_Notes.pdf.pdf 56.6 KB
12. Deploying to IaaS (AWS ECS)/9.1 Section12.8_Notes.pdf.pdf 55.3 KB
12. Deploying to IaaS (AWS ECS)/3.1 Section12.3_Notes.pdf.pdf 55.1 KB
12. Deploying to IaaS (AWS ECS)/4.1 Section12.3_Notes.pdf.pdf 55.1 KB
5. Course Setup and Key Tools/5.1 Section5.3c_Notes.pdf.pdf 53.9 KB
12. Deploying to IaaS (AWS ECS)/5.1 Section12.4_Notes.pdf.pdf 53.5 KB
13. A Deep Learning Model with Big Data/9.1 Section13.9_Notes.pdf.pdf 53.2 KB
2. Machine Learning Pipeline - Research Environment/6. Data Analysis - Demo.vtt 21.3 KB
4. Building a Reproducible Machine Learning Pipeline/3. Designing a Custom Pipeline.vtt 19.8 KB
2. Machine Learning Pipeline - Research Environment/7. Feature Engineering - Demo.vtt 14.1 KB
4. Building a Reproducible Machine Learning Pipeline/5. Third Party Pipeline Create Scikit-Learn compatible Feature Transformers.vtt 13.8 KB
4. Building a Reproducible Machine Learning Pipeline/2. Procedural Programming Pipeline.vtt 12.7 KB
3. Machine Learning System Architecture/5. Building a Reproducible Machine Learning Pipeline.vtt 12.7 KB
2. Machine Learning Pipeline - Research Environment/3. Machine Learning Pipeline Feature Selection.vtt 11.2 KB
13. A Deep Learning Model with Big Data/3. Building a CNN in the Research Environment.vtt 10.3 KB
4. Building a Reproducible Machine Learning Pipeline/4. Leveraging a Third Party Pipeline Scikit-Learn.vtt 9.6 KB
2. Machine Learning Pipeline - Research Environment/2. Machine Learning Pipeline Feature Engineering.vtt 9.3 KB
1. Introduction/2. Course curriculum overview.vtt 9.2 KB
13. A Deep Learning Model with Big Data/4. Production Code for a CNN Learning Pipeline.vtt 9.1 KB
2. Machine Learning Pipeline - Research Environment/10. Getting Ready for Deployment - Demo.vtt 9.0 KB
2. Machine Learning Pipeline - Research Environment/1. Machine Learning Pipeline Overview.vtt 8.8 KB
1. Introduction/1. Introduction to the course.vtt 7.7 KB
6. Creating a Machine Learning Pipeline Application/9. 6.8 - Building the Package.vtt 7.3 KB
8. Continuous Integration and Deployment Pipelines/4. 8.4 - Publishing the Model to Gemfury.vtt 7.3 KB
7. Serving the model via REST API/7. 7.6 - API Schema Validation.vtt 7.1 KB
6. Creating a Machine Learning Pipeline Application/8. 6.7 - Versioning and Logging.vtt 7.0 KB
5. Course Setup and Key Tools/9. Section5.5b - Virtualenv Introduction.vtt 6.9 KB
4. Building a Reproducible Machine Learning Pipeline/8. Bonus Should feature selection be part of the pipeline.vtt 6.8 KB
3. Machine Learning System Architecture/2. Specific Challenges of Machine Learning Systems.vtt 6.7 KB
12. Deploying to IaaS (AWS ECS)/12. 12.11 - Creating the ECS Cluster with the EC2 Launch Method.vtt 6.4 KB
8. Continuous Integration and Deployment Pipelines/3. 8.3 - Setup Circle CI Config.vtt 6.2 KB
13. A Deep Learning Model with Big Data/8. 13.8 - Packaging the CNN.vtt 6.1 KB
3. Machine Learning System Architecture/4. Machine Learning System Component Breakdown.vtt 6.1 KB
5. Course Setup and Key Tools/13. Section 5.7 - Engineering and Python Best Practices.vtt 6.0 KB
4. Building a Reproducible Machine Learning Pipeline/5.1 preprocessors.py.py 5.5 KB
6. Creating a Machine Learning Pipeline Application/2. 6.2 - Training the Model.vtt 5.3 KB
11. Running Apps with Containers (Docker)/5. 11.5 Releasing to Heroku with Docker.vtt 5.2 KB
3. Machine Learning System Architecture/3. Machine Learning System Approaches.vtt 5.2 KB
2. Machine Learning Pipeline - Research Environment/9. Model Building - Demo.vtt 5.2 KB
8. Continuous Integration and Deployment Pipelines/1. 8.1 - Introduction to CICD.vtt 4.9 KB
8. Continuous Integration and Deployment Pipelines/5. 8.5 - Testing the CI Pipeline.vtt 4.9 KB
10. Deploying to a PaaS (Heroku) without Containers/3. 10.3 - Heroku Config.vtt 4.8 KB
6. Creating a Machine Learning Pipeline Application/5. 6.4 - Making Predictions with the Model.vtt 4.8 KB
13. A Deep Learning Model with Big Data/4.1 CNNProdCode.zip.zip 4.7 KB
12. Deploying to IaaS (AWS ECS)/3. 12.3a - Intro to AWS ECS.vtt 4.5 KB
10. Deploying to a PaaS (Heroku) without Containers/1. 10.1 - Introduction.vtt 4.4 KB
1. Introduction/3. Knowledge requirements.vtt 4.4 KB
11. Running Apps with Containers (Docker)/1. 11.1 Introduction to Containers and Docker.vtt 4.4 KB
12. Deploying to IaaS (AWS ECS)/10. 12.9 - Uploading Images to the Elastic Container Registry (ECR).vtt 4.2 KB
9. Differential Testing/2. 9.2 - Setting up Differential Tests.vtt 4.2 KB
2. Machine Learning Pipeline - Research Environment/8. Feature Selection - Demo.vtt 4.1 KB
6. Creating a Machine Learning Pipeline Application/3. 6.3 - Connecting the Pipeline.vtt 4.0 KB
7. Serving the model via REST API/5. 7.4 - Adding the Prediction Endpoint.vtt 4.0 KB
13. A Deep Learning Model with Big Data/9. 13.9 - Adding the CNN to the API.vtt 3.9 KB
1. Introduction/5. Guide to Setting up your Computer.html 3.9 KB
7. Serving the model via REST API/4. 7.3 - Adding Config and Logging.vtt 3.9 KB
12. Deploying to IaaS (AWS ECS)/11. 12.10 - Creating the ECS Cluster with Fargate Launch Method.vtt 3.8 KB
13. A Deep Learning Model with Big Data/5. Reproducibility in Neural Networks.vtt 3.7 KB
7. Serving the model via REST API/1. 7.1 - Introduction.vtt 3.7 KB
10. Deploying to a PaaS (Heroku) without Containers/5. 10.5 - Deploying to Heroku via CI.vtt 3.7 KB
11. Running Apps with Containers (Docker)/4. 11.4 Building and Running the Docker Container.vtt 3.6 KB
13. A Deep Learning Model with Big Data/6. Setting the Seed for Keras.html 3.6 KB
12. Deploying to IaaS (AWS ECS)/4. 12.3b - Container Orchestration Options Kubernetes, ECS, Docker Swarm.vtt 3.6 KB
12. Deploying to IaaS (AWS ECS)/13. 12.12 - Updating the Cluster Containers.vtt 3.5 KB
2. Machine Learning Pipeline - Research Environment/4. Machine Learning Pipeline Model Building.vtt 3.5 KB
1. Introduction/4. How to Approach this course.html 3.3 KB
4. Building a Reproducible Machine Learning Pipeline/1. Production Code overview.vtt 3.3 KB
5. Course Setup and Key Tools/3. Section 5.3 - How to Use the Course Resources, Monorepos + Git Refresher.vtt 3.2 KB
12. Deploying to IaaS (AWS ECS)/1. 12.1 - Introduction to AWS.vtt 3.2 KB
9. Differential Testing/4. 9.4 - Differential Tests in CI (Part 2 of 2).vtt 3.2 KB
5. Course Setup and Key Tools/2. Section 5.2 - Installing and Configuring Git.vtt 3.2 KB
6. Creating a Machine Learning Pipeline Application/6. 6.5 - Data Validation in the Model Package.vtt 3.1 KB
7. Serving the model via REST API/3. 7.2b - Flask Crash Course.vtt 3.1 KB
13. A Deep Learning Model with Big Data/10. 13.10 - Additional Considerations and Wrap Up.vtt 3.1 KB
12. Deploying to IaaS (AWS ECS)/6. 12.5 - Setting Permissions with IAM.vtt 3.0 KB
9. Differential Testing/3. 9.3 - Differential Tests in CI (Part 1 of 2).vtt 3.0 KB
5. Course Setup and Key Tools/4. Section5.3b - Opening Pull Requests.vtt 3.0 KB
11. Running Apps with Containers (Docker)/3. 11.3 Creating Our API App Dockerfile.vtt 2.9 KB
5. Course Setup and Key Tools/11. Section5.5d - Virtualenv refresher.vtt 2.8 KB
4. Building a Reproducible Machine Learning Pipeline/3.1 CustomPipeline.zip.zip 2.8 KB
12. Deploying to IaaS (AWS ECS)/8. 12.7 - Configuring the AWS CLI.vtt 2.8 KB
4. Building a Reproducible Machine Learning Pipeline/2.1 ProceduralPrograming.zip.zip 2.7 KB
5. Course Setup and Key Tools/7. Section 5.4b - System Path and Pythonpath Demo.vtt 2.6 KB
9. Differential Testing/1. 9.1 - Introduction.vtt 2.6 KB
12. Deploying to IaaS (AWS ECS)/15. 12.14 - Deploying to ECS via the CI pipeline.vtt 2.6 KB
2. Machine Learning Pipeline - Research Environment/11. Bonus Machine Learning Pipeline Additional Resources.vtt 2.5 KB
6. Creating a Machine Learning Pipeline Application/7. 6.6 - Feature Engineering in the Pipeline.vtt 2.5 KB
2. Machine Learning Pipeline - Research Environment/12. Randomness in Machine Learning - Setting the Seed.html 2.5 KB
11. Running Apps with Containers (Docker)/2. 11.2 Installing Docker.vtt 2.4 KB
13. A Deep Learning Model with Big Data/1. Challenges of using Big Data in Machine Learning.vtt 2.4 KB
12. Deploying to IaaS (AWS ECS)/2. 12.2 - AWS Costs and Caution.vtt 2.4 KB
4. Building a Reproducible Machine Learning Pipeline/6. Third Party Pipeline Closing Remarks.vtt 2.3 KB
12. Deploying to IaaS (AWS ECS)/7. 12.6 - Installing the AWS CLI.vtt 2.3 KB
6. Creating a Machine Learning Pipeline Application/10. 6.9 - Wrap Up.vtt 2.3 KB
10. Deploying to a PaaS (Heroku) without Containers/2. 10.2 - Heroku Account Creation.vtt 2.3 KB
6. Creating a Machine Learning Pipeline Application/1. 6.1 - Introduction.vtt 2.2 KB
3. Machine Learning System Architecture/1. Machine Learning System Architecture and Why it Matters.vtt 2.2 KB
5. Course Setup and Key Tools/10. Section5.5c - Requirements files Introduction.vtt 2.2 KB
10. Deploying to a PaaS (Heroku) without Containers/6. 10.6 - Wrap Up.vtt 2.1 KB
5. Course Setup and Key Tools/5. Section5.3c - Primer on Monorepos.vtt 2.1 KB
5. Course Setup and Key Tools/1. Section 5.1 - Introduction.vtt 2.0 KB
9. Differential Testing/5. 9.5 Wrap Up.vtt 2.0 KB
13. A Deep Learning Model with Big Data/2. Introduction to a Large Dataset - Plant Seedlings Images.vtt 1.9 KB
7. Serving the model via REST API/6. 7.5 - Adding a Version Endpoint.vtt 1.9 KB
12. Deploying to IaaS (AWS ECS)/16. 12.15 - Wrap Up.vtt 1.9 KB
4. Building a Reproducible Machine Learning Pipeline/7. Scikit-Learn Pipeline - Code.html 1.9 KB
5. Course Setup and Key Tools/6. Section 5.4a - Operating System Differences and Gotchas.vtt 1.8 KB
10. Deploying to a PaaS (Heroku) without Containers/4. 10.4 - Testing the Deployment Manually.vtt 1.6 KB
5. Course Setup and Key Tools/12. Section 5.6 - Text Editors IDEs.vtt 1.6 KB
11. Running Apps with Containers (Docker)/6. 11.6 - Wrap Up.vtt 1.6 KB
8. Continuous Integration and Deployment Pipelines/2. 8.2 - Setting up CircleCI.vtt 1.5 KB
4. Building a Reproducible Machine Learning Pipeline/9. Bonus Additional Resources on Scikit-Learn.html 1.4 KB
5. Course Setup and Key Tools/14. Section 5.8 - Wrap Up.vtt 1.3 KB
7. Serving the model via REST API/8. 7.7 - Wrap Up.vtt 1.2 KB
6. Creating a Machine Learning Pipeline Application/4. 6.4a - Gotchas.html 1.2 KB
12. Deploying to IaaS (AWS ECS)/9. 12.8 - Intro the Elastic Container Registry (ECR).vtt 1.2 KB
4. Building a Reproducible Machine Learning Pipeline/10. Bonus Resources to Improve as a Python Developer.html 1.1 KB
3. Machine Learning System Architecture/6. Additional Reading Resources.html 1.0 KB
1. Introduction/8. FAQ Where can I learn more about the required skills.html 1.0 KB
8. Continuous Integration and Deployment Pipelines/6. 8.6 - Wrap Up.vtt 998 bytes
15. Final Section/1. Bonus Discount for other courses.html 814 bytes
5. Course Setup and Key Tools/8. Section 5.5a - Quick Word for More Advanced Students.vtt 768 bytes
12. Deploying to IaaS (AWS ECS)/14. 12.13 - Tearing down the ECS Cluster.vtt 740 bytes
2. Machine Learning Pipeline - Research Environment/14. FAQ Where can I learn more about the pipeline steps.html 623 bytes
12. Deploying to IaaS (AWS ECS)/5. 12.4 - Create an AWS Account.vtt 603 bytes
2. Machine Learning Pipeline - Research Environment/13. Randomness in Machine Learning - Additional reading resources.html 522 bytes
13. A Deep Learning Model with Big Data/7. Seed for Neural Networks - Additional reading resources.html 397 bytes
14. Common Issues found during deployment/1. Troubleshooting.html 105 bytes
2. Machine Learning Pipeline - Research Environment/5. Jupyter notebooks covered in this section.html 93 bytes
1. Introduction/6. Slides covered in this course.html 92 bytes
1. Introduction/7. Notes covered in this course.html 91 bytes
7. Serving the model via REST API/2. 7.2 - Creating the API Skeleton.vtt 0 bytes

[Udemy] Deployment of Machine Learning Models (2020) [En]

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