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

Creation Time	Aug. 28, 2024, 12:02 a.m.
Last Access Time	May 25, 2025, 10:26 p.m.
File Size	3.6 GB
Keywords	En Models of Udemy Machine 2020 Learning Deployment
Total Requests	20
Total Files	298
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

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]

File List

Download Torrent

Related Resources

Copyright Infringement