[FTUForum.com] [UDEMY] Deployment of Machine Learning Models [FTU]

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[FTUForum.com] [UDEMY] Deployment of Machine Learning Models [FTU]

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文件列表

  • 4. Building a Reproducible Machine Learning Pipeline/3. Designing a Custom Pipeline.mp4 160.3 MB
  • 2. Machine Learning Pipeline - Research Environment/6. Data Analysis - Demo.mp4 142.0 MB
  • 2. Machine Learning Pipeline - Research Environment/7. Feature Engineering - Demo.mp4 102.9 MB
  • 13. A Deep Learning Model with Big Data/3. Building a CNN in the Research Environment.mp4 93.1 MB
  • 4. Building a Reproducible Machine Learning Pipeline/2. Procedural Programming Pipeline.mp4 90.4 MB
  • 4. Building a Reproducible Machine Learning Pipeline/5. Third Party Pipeline Create Scikit-Learn compatible Feature Transformers.mp4 88.4 MB
  • 13. A Deep Learning Model with Big Data/4. Production Code for a CNN Learning Pipeline.mp4 83.4 MB
  • 5. Course Setup and Key Tools/9. Section5.5b - Virtualenv Introduction.mp4 83.2 MB
  • 7. Serving the model via REST API/7. 7.6 - API Schema Validation.mp4 81.9 MB
  • 3. Machine Learning System Architecture/5. Building a Reproducible Machine Learning Pipeline.mp4 80.8 MB
  • 6. Creating a Machine Learning Pipeline Application/9. 6.8 - Building the Package.mp4 79.6 MB
  • 13. A Deep Learning Model with Big Data/8. 13.8 - Packaging the CNN.mp4 75.4 MB
  • 6. Creating a Machine Learning Pipeline Application/8. 6.7 - Versioning and Logging.mp4 73.9 MB
  • 8. Continuous Integration and Deployment Pipelines/4. 8.4 - Publishing the Model to Gemfury.mp4 72.5 MB
  • 2. Machine Learning Pipeline - Research Environment/10. Getting Ready for Deployment - Demo.mp4 71.1 MB
  • 2. Machine Learning Pipeline - Research Environment/1. Machine Learning Pipeline Overview.mp4 63.5 MB
  • 12. Deploying to IaaS (AWS ECS)/12. 12.11 - Creating the ECS Cluster with the EC2 Launch Method.mp4 62.8 MB
  • 2. Machine Learning Pipeline - Research Environment/2. Machine Learning Pipeline Feature Engineering.mp4 60.5 MB
  • 4. Building a Reproducible Machine Learning Pipeline/4. Leveraging a Third Party Pipeline Scikit-Learn.mp4 59.4 MB
  • 8. Continuous Integration and Deployment Pipelines/3. 8.3 - Setup Circle CI Config.mp4 53.3 MB
  • 9. Differential Testing/2. 9.2 - Setting up Differential Tests.mp4 52.7 MB
  • 8. Continuous Integration and Deployment Pipelines/5. 8.5 - Testing the CI Pipeline.mp4 52.6 MB
  • 12. Deploying to IaaS (AWS ECS)/10. 12.9 - Uploading Images to the Elastic Container Registry (ECR).mp4 52.2 MB
  • 2. Machine Learning Pipeline - Research Environment/3. Machine Learning Pipeline Feature Selection.mp4 50.8 MB
  • 1. Introduction/2. Course curriculum overview.mp4 50.6 MB
  • 11. Running Apps with Containers (Docker)/5. 11.5 Releasing to Heroku with Docker.mp4 49.2 MB
  • 6. Creating a Machine Learning Pipeline Application/2. 6.2 - Training the Model.mp4 48.5 MB
  • 6. Creating a Machine Learning Pipeline Application/3. 6.3 - Connecting the Pipeline.mp4 47.6 MB
  • 6. Creating a Machine Learning Pipeline Application/5. 6.4 - Making Predictions with the Model.mp4 46.9 MB
  • 8. Continuous Integration and Deployment Pipelines/1.1 section8.1.mp4.mp4 43.9 MB
  • 13. A Deep Learning Model with Big Data/9. 13.9 - Adding the CNN to the API.mp4 43.3 MB
  • 3. Machine Learning System Architecture/2. Specific Challenges of Machine Learning Systems.mp4 41.0 MB
  • 7. Serving the model via REST API/5. 7.4 - Adding the Prediction Endpoint.mp4 40.8 MB
  • 12. Deploying to IaaS (AWS ECS)/11. 12.10 - Creating the ECS Cluster with Fargate Launch Method.mp4 40.0 MB
  • 5. Course Setup and Key Tools/3. Section 5.3 - How to Use the Course Resources, Monorepos + Git Refresher.mp4 39.6 MB
  • 1. Introduction/1. Introduction to the course.mp4 39.4 MB
  • 5. Course Setup and Key Tools/4. Section5.3b - Opening Pull Requests.mp4 37.9 MB
  • 2. Machine Learning Pipeline - Research Environment/8. Feature Selection - Demo.mp4 37.1 MB
  • 7. Serving the model via REST API/2. 7.2 - Creating the API Skeleton.mp4 37.1 MB
  • 2. Machine Learning Pipeline - Research Environment/9. Model Building - Demo.mp4 35.8 MB
  • 9. Differential Testing/3. 9.3 - Differential Tests in CI (Part 1 of 2).mp4 35.2 MB
  • 7. Serving the model via REST API/4. 7.3 - Adding Config and Logging.mp4 34.6 MB
  • 9. Differential Testing/4. 9.4 - Differential Tests in CI (Part 2 of 2).mp4 34.4 MB
  • 6. Creating a Machine Learning Pipeline Application/6. 6.5 - Data Validation in the Model Package.mp4 34.1 MB
  • 10. Deploying to a PaaS (Heroku) without Containers/3. 10.3 - Heroku Config.mp4 33.8 MB
  • 11. Running Apps with Containers (Docker)/1. 11.1 Introduction to Containers and Docker.mp4 33.0 MB
  • 5. Course Setup and Key Tools/13. Section 5.7 - Engineering and Python Best Practices.mp4 32.9 MB
  • 12. Deploying to IaaS (AWS ECS)/13. 12.12 - Updating the Cluster Containers.mp4 32.5 MB
  • 3. Machine Learning System Architecture/3. Machine Learning System Approaches.mp4 31.5 MB
  • 3. Machine Learning System Architecture/4. Machine Learning System Component Breakdown.mp4 30.9 MB
  • 10. Deploying to a PaaS (Heroku) without Containers/5. 10.5 - Deploying to Heroku via CI.mp4 30.5 MB
  • 12. Deploying to IaaS (AWS ECS)/7. 12.6 - Installing the AWS CLI.mp4 30.3 MB
  • 4. Building a Reproducible Machine Learning Pipeline/8. Bonus Should feature selection be part of the pipeline.mp4 30.2 MB
  • 8. Continuous Integration and Deployment Pipelines/1. 8.1 - Introduction to CICD.mp4 29.8 MB
  • 5. Course Setup and Key Tools/2. Section 5.2 - Installing and Configuring Git.mp4 29.2 MB
  • 10. Deploying to a PaaS (Heroku) without Containers/1. 10.1 - Introduction.mp4 28.2 MB
  • 11. Running Apps with Containers (Docker)/4. 11.4 Building and Running the Docker Container.mp4 28.0 MB
  • 11. Running Apps with Containers (Docker)/2. 11.2 Installing Docker.mp4 27.9 MB
  • 6. Creating a Machine Learning Pipeline Application/7. 6.6 - Feature Engineering in the Pipeline.mp4 27.5 MB
  • 12. Deploying to IaaS (AWS ECS)/2. 12.2 - AWS Costs and Caution.mp4 26.7 MB
  • 2. Machine Learning Pipeline - Research Environment/11. Bonus Machine Learning Pipeline Additional Resources.mp4 26.7 MB
  • 5. Course Setup and Key Tools/11. Section5.5d - Virtualenv refresher.mp4 26.6 MB
  • 7. Serving the model via REST API/1. 7.1 - Introduction.mp4 26.3 MB
  • 12. Deploying to IaaS (AWS ECS)/4. 12.3b - Container Orchestration Options Kubernetes, ECS, Docker Swarm.mp4 24.9 MB
  • 12. Deploying to IaaS (AWS ECS)/15. 12.14 - Deploying to ECS via the CI pipeline.mp4 24.7 MB
  • 12. Deploying to IaaS (AWS ECS)/6. 12.5 - Setting Permissions with IAM.mp4 24.3 MB
  • 12. Deploying to IaaS (AWS ECS)/3. 12.3a - Intro to AWS ECS.mp4 23.9 MB
  • 5. Course Setup and Key Tools/7. Section 5.4b - System Path and Pythonpath Demo.mp4 22.9 MB
  • 11. Running Apps with Containers (Docker)/3. 11.3 Creating Our API App Dockerfile.mp4 22.6 MB
  • 10. Deploying to a PaaS (Heroku) without Containers/2. 10.2 - Heroku Account Creation.mp4 21.9 MB
  • 12. Deploying to IaaS (AWS ECS)/8. 12.7 - Configuring the AWS CLI.mp4 21.8 MB
  • 13. A Deep Learning Model with Big Data/10. 13.10 - Additional Considerations and Wrap Up.mp4 21.8 MB
  • 4. Building a Reproducible Machine Learning Pipeline/1. Production Code overview.mp4 20.2 MB
  • 12. Deploying to IaaS (AWS ECS)/1. 12.1 - Introduction to AWS.mp4 19.6 MB
  • 9. Differential Testing/1. 9.1 - Introduction.mp4 19.5 MB
  • 5. Course Setup and Key Tools/5. Section5.3c - Primer on Monorepos.mp4 19.2 MB
  • 2. Machine Learning Pipeline - Research Environment/4. Machine Learning Pipeline Model Building.mp4 18.7 MB
  • 7. Serving the model via REST API/3. 7.2b - Flask Crash Course.mp4 18.7 MB
  • 1. Introduction/3. Knowledge requirements.mp4 18.0 MB
  • 13. A Deep Learning Model with Big Data/5. Reproducibility in Neural Networks.mp4 17.8 MB
  • 13. A Deep Learning Model with Big Data/2. Introduction to a Large Dataset - Plant Seedlings Images.mp4 17.5 MB
  • 5. Course Setup and Key Tools/1. Section 5.1 - Introduction.mp4 16.7 MB
  • 7. Serving the model via REST API/6. 7.5 - Adding a Version Endpoint.mp4 16.2 MB
  • 13. A Deep Learning Model with Big Data/1. Challenges of using Big Data in Machine Learning.mp4 16.0 MB
  • 6. Creating a Machine Learning Pipeline Application/1. 6.1 - Introduction.mp4 15.0 MB
  • 10. Deploying to a PaaS (Heroku) without Containers/6. 10.6 - Wrap Up.mp4 14.2 MB
  • 1. Introduction/6.1 DMLM_Slides.zip.zip 14.0 MB
  • 6. Creating a Machine Learning Pipeline Application/10. 6.9 - Wrap Up.mp4 13.9 MB
  • 9. Differential Testing/5. 9.5 Wrap Up.mp4 13.3 MB
  • 5. Course Setup and Key Tools/12. Section 5.6 - Text Editors IDEs.mp4 13.0 MB
  • 10. Deploying to a PaaS (Heroku) without Containers/4. 10.4 - Testing the Deployment Manually.mp4 12.9 MB
  • 3. Machine Learning System Architecture/1. Machine Learning System Architecture and Why it Matters.mp4 11.4 MB
  • 8. Continuous Integration and Deployment Pipelines/2. 8.2 - Setting up CircleCI.mp4 11.1 MB
  • 4. Building a Reproducible Machine Learning Pipeline/6. Third Party Pipeline Closing Remarks.mp4 10.5 MB
  • 5. Course Setup and Key Tools/10. Section5.5c - Requirements files Introduction.mp4 10.0 MB
  • 12. Deploying to IaaS (AWS ECS)/9. 12.8 - Intro the Elastic Container Registry (ECR).mp4 9.8 MB
  • 12. Deploying to IaaS (AWS ECS)/16. 12.15 - Wrap Up.mp4 9.4 MB
  • 5. Course Setup and Key Tools/6. Section 5.4a - Operating System Differences and Gotchas.mp4 8.6 MB
  • 5. Course Setup and Key Tools/8. Section 5.5a - Quick Word for More Advanced Students.mp4 8.6 MB
  • 11. Running Apps with Containers (Docker)/6. 11.6 - Wrap Up.mp4 8.1 MB
  • 12. Deploying to IaaS (AWS ECS)/14. 12.13 - Tearing down the ECS Cluster.mp4 7.3 MB
  • 7. Serving the model via REST API/8. 7.7 - Wrap Up.mp4 6.6 MB
  • 5. Course Setup and Key Tools/14. Section 5.8 - Wrap Up.mp4 6.3 MB
  • 8. Continuous Integration and Deployment Pipelines/6. 8.6 - Wrap Up.mp4 5.6 MB
  • 12. Deploying to IaaS (AWS ECS)/5. 12.4 - Create an AWS Account.mp4 5.1 MB
  • 1. Introduction/7.1 DMLM_Notes.zip.zip 1.6 MB
  • 13. A Deep Learning Model with Big Data/3.1 CNN_Analysis_and Model.zip.zip 1.6 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 228.7 kB
  • 7. Serving the model via REST API/2.1 Section7.2_Notes.pdf.pdf 149.8 kB
  • FreeCoursesOnline.Me.html 110.9 kB
  • 8. Continuous Integration and Deployment Pipelines/4.1 Section8.4_Notes.pdf.pdf 103.2 kB
  • 9. Differential Testing/4.1 Section9.4_Notes.pdf.pdf 103.0 kB
  • FTUForum.com.html 102.8 kB
  • 5. Course Setup and Key Tools/4.1 Section5.3b_Notes.pdf.pdf 102.0 kB
  • 6. Creating a Machine Learning Pipeline Application/4.1 Section6.4_Notes.pdf.pdf 101.1 kB
  • 6. Creating a Machine Learning Pipeline Application/5.1 Section6.4_Notes.pdf.pdf 101.1 kB
  • 5. Course Setup and Key Tools/2.1 Section5.2_Notes.pdf.pdf 98.8 kB
  • 11. Running Apps with Containers (Docker)/4.1 Section11.4_Notes.pdf.pdf 96.3 kB
  • 5. Course Setup and Key Tools/9.1 Section5.5b_Notes.pdf.pdf 94.4 kB
  • 5. Course Setup and Key Tools/13.1 Section5.7_Notes.pdf.pdf 91.0 kB
  • 8. Continuous Integration and Deployment Pipelines/5.1 Section8.5_Notes.pdf.pdf 91.0 kB
  • 6. Creating a Machine Learning Pipeline Application/9.1 Section6.8_Notes.pdf.pdf 88.0 kB
  • 5. Course Setup and Key Tools/3.1 Section5.3a_Notes.pdf.pdf 87.6 kB
  • 5. Course Setup and Key Tools/12.1 Section5.6_Notes.pdf.pdf 86.9 kB
  • 7. Serving the model via REST API/6.1 Section7.5_Notes.pdf.pdf 86.3 kB
  • 5. Course Setup and Key Tools/11.1 Section5.5_Notes.pdf.pdf 85.8 kB
  • 7. Serving the model via REST API/7.1 Section7.6_Notes.pdf.pdf 85.7 kB
  • 7. Serving the model via REST API/4.1 Section7.3_Notes.pdf.pdf 85.1 kB
  • 12. Deploying to IaaS (AWS ECS)/8.1 Section12.7_Notes.pdf.pdf 85.0 kB
  • 11. Running Apps with Containers (Docker)/6.1 Section11.6_Notes.pdf.pdf 84.4 kB
  • 7. Serving the model via REST API/3.1 Section7.2b_Notes.pdf.pdf 83.5 kB
  • 7. Serving the model via REST API/5.1 Section7.4_Notes.pdf.pdf 83.5 kB
  • 6. Creating a Machine Learning Pipeline Application/8.1 Section6.7_Notes.pdf.pdf 83.2 kB
  • 6. Creating a Machine Learning Pipeline Application/6.1 Section6.5_Notes.pdf.pdf 81.2 kB
  • 6. Creating a Machine Learning Pipeline Application/7.1 Section6.6_Notes.pdf.pdf 80.8 kB
  • 3. Machine Learning System Architecture/4.1 Section3.4_Notes.pdf.pdf 80.7 kB
  • 11. Running Apps with Containers (Docker)/2.1 Section11.2_Notes.pdf.pdf 79.7 kB
  • 10. Deploying to a PaaS (Heroku) without Containers/1.1 Section10.1_Notes.pdf.pdf 78.5 kB
  • 6. Creating a Machine Learning Pipeline Application/3.1 Section6.3_Notes.pdf.pdf 77.2 kB
  • 12. Deploying to IaaS (AWS ECS)/2.1 Section12.2_Notes.pdf.pdf 76.5 kB
  • 5. Course Setup and Key Tools/10.1 Section5.5c_Notes.pdf.pdf 75.5 kB
  • 12. Deploying to IaaS (AWS ECS)/10.1 Section12.9_Notes.pdf.pdf 74.1 kB
  • 13. A Deep Learning Model with Big Data/8.1 Section13.8_Notes.pdf.pdf 73.3 kB
  • 3. Machine Learning System Architecture/3.1 Section3.3_Notes.pdf.pdf 72.7 kB
  • 11. Running Apps with Containers (Docker)/1.1 Section11.1_Notes.pdf.pdf 71.9 kB
  • 10. Deploying to a PaaS (Heroku) without Containers/4.1 Section10.4_Notes.pdf.pdf 71.4 kB
  • 9. Differential Testing/5.1 Section9.5_Notes.pdf.pdf 71.1 kB
  • 10. Deploying to a PaaS (Heroku) without Containers/3.1 Section10.3_Notes.pdf.pdf 70.6 kB
  • 10. Deploying to a PaaS (Heroku) without Containers/5.1 Section10.5_Notes.pdf.pdf 69.5 kB
  • 12. Deploying to IaaS (AWS ECS)/13.1 Section12.12_Notes.pdf.pdf 69.1 kB
  • 9. Differential Testing/2.1 Section9.2_Notes.pdf.pdf 66.5 kB
  • 5. Course Setup and Key Tools/6.1 Section5.4_Notes.pdf.pdf 66.0 kB
  • 5. Course Setup and Key Tools/7.1 Section5.4_Notes.pdf.pdf 66.0 kB
  • 12. Deploying to IaaS (AWS ECS)/15.1 Section12.14_Notes.pdf.pdf 65.7 kB
  • 10. Deploying to a PaaS (Heroku) without Containers/6.1 Section10.6_Notes.pdf.pdf 65.4 kB
  • 8. Continuous Integration and Deployment Pipelines/3.1 Section8.3_Notes.pdf.pdf 65.4 kB
  • 7. Serving the model via REST API/1.1 Section7.1_Notes.pdf.pdf 64.9 kB
  • 10. Deploying to a PaaS (Heroku) without Containers/2.1 Section10.2_Notes.pdf.pdf 62.9 kB
  • 12. Deploying to IaaS (AWS ECS)/14.1 Section12.13_Notes.pdf.pdf 61.5 kB
  • 13. A Deep Learning Model with Big Data/10.1 Section13.10_Notes.pdf.pdf 61.5 kB
  • 8. Continuous Integration and Deployment Pipelines/2.1 Section8.2_Notes.pdf.pdf 60.2 kB
  • 11. Running Apps with Containers (Docker)/3.1 Section11.3_Notes.pdf.pdf 59.9 kB
  • 12. Deploying to IaaS (AWS ECS)/6.1 Section12.5_Notes.pdf.pdf 59.3 kB
  • 12. Deploying to IaaS (AWS ECS)/12.1 Section12.11_Notes.pdf.pdf 58.8 kB
  • 12. Deploying to IaaS (AWS ECS)/7.1 Section12.6_Notes.pdf.pdf 58.3 kB
  • 12. Deploying to IaaS (AWS ECS)/11.1 Section12.10_Notes.pdf.pdf 58.2 kB
  • 12. Deploying to IaaS (AWS ECS)/16.1 Section12.15_Notes.pdf.pdf 57.9 kB
  • 11. Running Apps with Containers (Docker)/5.1 Section11.5_Notes.pdf.pdf 57.9 kB
  • 12. Deploying to IaaS (AWS ECS)/9.1 Section12.8_Notes.pdf.pdf 56.6 kB
  • 12. Deploying to IaaS (AWS ECS)/3.1 Section12.3_Notes.pdf.pdf 56.5 kB
  • 12. Deploying to IaaS (AWS ECS)/4.1 Section12.3_Notes.pdf.pdf 56.5 kB
  • 5. Course Setup and Key Tools/5.1 Section5.3c_Notes.pdf.pdf 55.2 kB
  • 12. Deploying to IaaS (AWS ECS)/5.1 Section12.4_Notes.pdf.pdf 54.8 kB
  • 13. A Deep Learning Model with Big Data/9.1 Section13.9_Notes.pdf.pdf 54.5 kB
  • Discuss.FTUForum.com.html 32.7 kB
  • 2. Machine Learning Pipeline - Research Environment/6. Data Analysis - Demo.vtt 21.8 kB
  • 4. Building a Reproducible Machine Learning Pipeline/3. Designing a Custom Pipeline.vtt 20.3 kB
  • 2. Machine Learning Pipeline - Research Environment/7. Feature Engineering - Demo.vtt 14.4 kB
  • 4. Building a Reproducible Machine Learning Pipeline/5. Third Party Pipeline Create Scikit-Learn compatible Feature Transformers.vtt 14.1 kB
  • 4. Building a Reproducible Machine Learning Pipeline/2. Procedural Programming Pipeline.vtt 13.0 kB
  • 3. Machine Learning System Architecture/5. Building a Reproducible Machine Learning Pipeline.vtt 13.0 kB
  • 2. Machine Learning Pipeline - Research Environment/3. Machine Learning Pipeline Feature Selection.vtt 11.5 kB
  • 13. A Deep Learning Model with Big Data/3. Building a CNN in the Research Environment.vtt 10.5 kB
  • 4. Building a Reproducible Machine Learning Pipeline/4. Leveraging a Third Party Pipeline Scikit-Learn.vtt 9.8 kB
  • 2. Machine Learning Pipeline - Research Environment/2. Machine Learning Pipeline Feature Engineering.vtt 9.5 kB
  • 1. Introduction/2. Course curriculum overview.vtt 9.5 kB
  • 13. A Deep Learning Model with Big Data/4. Production Code for a CNN Learning Pipeline.vtt 9.4 kB
  • 2. Machine Learning Pipeline - Research Environment/10. Getting Ready for Deployment - Demo.vtt 9.2 kB
  • 2. Machine Learning Pipeline - Research Environment/1. Machine Learning Pipeline Overview.vtt 9.0 kB
  • 1. Introduction/1. Introduction to the course.vtt 7.8 kB
  • 6. Creating a Machine Learning Pipeline Application/9. 6.8 - Building the Package.vtt 7.4 kB
  • 8. Continuous Integration and Deployment Pipelines/4. 8.4 - Publishing the Model to Gemfury.vtt 7.4 kB
  • 7. Serving the model via REST API/7. 7.6 - API Schema Validation.vtt 7.3 kB
  • 6. Creating a Machine Learning Pipeline Application/8. 6.7 - Versioning and Logging.vtt 7.2 kB
  • 5. Course Setup and Key Tools/9. Section5.5b - Virtualenv Introduction.vtt 7.1 kB
  • 4. Building a Reproducible Machine Learning Pipeline/8. Bonus Should feature selection be part of the pipeline.vtt 7.0 kB
  • 3. Machine Learning System Architecture/2. Specific Challenges of Machine Learning Systems.vtt 6.9 kB
  • 12. Deploying to IaaS (AWS ECS)/12. 12.11 - Creating the ECS Cluster with the EC2 Launch Method.vtt 6.5 kB
  • 8. Continuous Integration and Deployment Pipelines/3. 8.3 - Setup Circle CI Config.vtt 6.4 kB
  • 13. A Deep Learning Model with Big Data/8. 13.8 - Packaging the CNN.vtt 6.3 kB
  • 3. Machine Learning System Architecture/4. Machine Learning System Component Breakdown.vtt 6.2 kB
  • 5. Course Setup and Key Tools/13. Section 5.7 - Engineering and Python Best Practices.vtt 6.1 kB
  • 4. Building a Reproducible Machine Learning Pipeline/5.1 preprocessors.py.py 5.7 kB
  • 6. Creating a Machine Learning Pipeline Application/2. 6.2 - Training the Model.vtt 5.4 kB
  • 11. Running Apps with Containers (Docker)/5. 11.5 Releasing to Heroku with Docker.vtt 5.3 kB
  • 3. Machine Learning System Architecture/3. Machine Learning System Approaches.vtt 5.3 kB
  • 2. Machine Learning Pipeline - Research Environment/9. Model Building - Demo.vtt 5.3 kB
  • 8. Continuous Integration and Deployment Pipelines/1. 8.1 - Introduction to CICD.vtt 5.1 kB
  • 8. Continuous Integration and Deployment Pipelines/5. 8.5 - Testing the CI Pipeline.vtt 5.0 kB
  • 10. Deploying to a PaaS (Heroku) without Containers/3. 10.3 - Heroku Config.vtt 5.0 kB
  • 6. Creating a Machine Learning Pipeline Application/5. 6.4 - Making Predictions with the Model.vtt 4.9 kB
  • 13. A Deep Learning Model with Big Data/4.1 CNNProdCode.zip.zip 4.8 kB
  • 12. Deploying to IaaS (AWS ECS)/3. 12.3a - Intro to AWS ECS.vtt 4.6 kB
  • 10. Deploying to a PaaS (Heroku) without Containers/1. 10.1 - Introduction.vtt 4.5 kB
  • 1. Introduction/3. Knowledge requirements.vtt 4.5 kB
  • 11. Running Apps with Containers (Docker)/1. 11.1 Introduction to Containers and Docker.vtt 4.5 kB
  • 12. Deploying to IaaS (AWS ECS)/10. 12.9 - Uploading Images to the Elastic Container Registry (ECR).vtt 4.3 kB
  • 9. Differential Testing/2. 9.2 - Setting up Differential Tests.vtt 4.3 kB
  • 2. Machine Learning Pipeline - Research Environment/8. Feature Selection - Demo.vtt 4.2 kB
  • 6. Creating a Machine Learning Pipeline Application/3. 6.3 - Connecting the Pipeline.vtt 4.1 kB
  • 7. Serving the model via REST API/5. 7.4 - Adding the Prediction Endpoint.vtt 4.1 kB
  • 13. A Deep Learning Model with Big Data/9. 13.9 - Adding the CNN to the API.vtt 4.0 kB
  • 1. Introduction/5. Guide to Setting up your Computer.html 4.0 kB
  • 7. Serving the model via REST API/4. 7.3 - Adding Config and Logging.vtt 4.0 kB
  • 12. Deploying to IaaS (AWS ECS)/11. 12.10 - Creating the ECS Cluster with Fargate Launch Method.vtt 3.9 kB
  • 13. A Deep Learning Model with Big Data/5. Reproducibility in Neural Networks.vtt 3.8 kB
  • 7. Serving the model via REST API/1. 7.1 - Introduction.vtt 3.8 kB
  • 10. Deploying to a PaaS (Heroku) without Containers/5. 10.5 - Deploying to Heroku via CI.vtt 3.8 kB
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