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

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FTUForum com UDEMY Deployment of Machine Learning Models FTU

文件列表
  1. 10. Deploying to a PaaS (Heroku) without Containers/1. 10.1 - Introduction.mp426.9MB
  2. 10. Deploying to a PaaS (Heroku) without Containers/2. 10.2 - Heroku Account Creation.mp420.91MB
  3. 10. Deploying to a PaaS (Heroku) without Containers/3. 10.3 - Heroku Config.mp432.22MB
  4. 10. Deploying to a PaaS (Heroku) without Containers/4. 10.4 - Testing the Deployment Manually.mp412.35MB
  5. 10. Deploying to a PaaS (Heroku) without Containers/5. 10.5 - Deploying to Heroku via CI.mp429.07MB
  6. 10. Deploying to a PaaS (Heroku) without Containers/6. 10.6 - Wrap Up.mp413.51MB
  7. 11. Running Apps with Containers (Docker)/1. 11.1 Introduction to Containers and Docker.mp431.51MB
  8. 11. Running Apps with Containers (Docker)/2. 11.2 Installing Docker.mp426.65MB
  9. 11. Running Apps with Containers (Docker)/3. 11.3 Creating Our API App Dockerfile.mp421.6MB
  10. 11. Running Apps with Containers (Docker)/4. 11.4 Building and Running the Docker Container.mp426.73MB
  11. 11. Running Apps with Containers (Docker)/5. 11.5 Releasing to Heroku with Docker.mp446.93MB
  12. 11. Running Apps with Containers (Docker)/6. 11.6 - Wrap Up.mp47.73MB
  13. 12. Deploying to IaaS (AWS ECS)/10. 12.9 - Uploading Images to the Elastic Container Registry (ECR).mp449.77MB
  14. 12. Deploying to IaaS (AWS ECS)/11. 12.10 - Creating the ECS Cluster with Fargate Launch Method.mp438.12MB
  15. 12. Deploying to IaaS (AWS ECS)/1. 12.1 - Introduction to AWS.mp418.7MB
  16. 12. Deploying to IaaS (AWS ECS)/12. 12.11 - Creating the ECS Cluster with the EC2 Launch Method.mp459.91MB
  17. 12. Deploying to IaaS (AWS ECS)/13. 12.12 - Updating the Cluster Containers.mp430.97MB
  18. 12. Deploying to IaaS (AWS ECS)/14. 12.13 - Tearing down the ECS Cluster.mp46.96MB
  19. 12. Deploying to IaaS (AWS ECS)/15. 12.14 - Deploying to ECS via the CI pipeline.mp423.54MB
  20. 12. Deploying to IaaS (AWS ECS)/16. 12.15 - Wrap Up.mp48.97MB
  21. 12. Deploying to IaaS (AWS ECS)/2. 12.2 - AWS Costs and Caution.mp425.46MB
  22. 12. Deploying to IaaS (AWS ECS)/3. 12.3a - Intro to AWS ECS.mp422.79MB
  23. 12. Deploying to IaaS (AWS ECS)/4. 12.3b - Container Orchestration Options Kubernetes, ECS, Docker Swarm.mp423.79MB
  24. 12. Deploying to IaaS (AWS ECS)/5. 12.4 - Create an AWS Account.mp44.9MB
  25. 12. Deploying to IaaS (AWS ECS)/6. 12.5 - Setting Permissions with IAM.mp423.21MB
  26. 12. Deploying to IaaS (AWS ECS)/7. 12.6 - Installing the AWS CLI.mp428.93MB
  27. 12. Deploying to IaaS (AWS ECS)/8. 12.7 - Configuring the AWS CLI.mp420.84MB
  28. 12. Deploying to IaaS (AWS ECS)/9. 12.8 - Intro the Elastic Container Registry (ECR).mp49.37MB
  29. 13. A Deep Learning Model with Big Data/10. 13.10 - Additional Considerations and Wrap Up.mp420.76MB
  30. 13. A Deep Learning Model with Big Data/1. Challenges of using Big Data in Machine Learning.mp415.3MB
  31. 13. A Deep Learning Model with Big Data/2. Introduction to a Large Dataset - Plant Seedlings Images.mp416.65MB
  32. 13. A Deep Learning Model with Big Data/3. Building a CNN in the Research Environment.mp488.8MB
  33. 13. A Deep Learning Model with Big Data/4. Production Code for a CNN Learning Pipeline.mp479.51MB
  34. 13. A Deep Learning Model with Big Data/5. Reproducibility in Neural Networks.mp417.02MB
  35. 13. A Deep Learning Model with Big Data/8. 13.8 - Packaging the CNN.mp471.88MB
  36. 13. A Deep Learning Model with Big Data/9. 13.9 - Adding the CNN to the API.mp441.32MB
  37. 1. Introduction/1. Introduction to the course.mp437.59MB
  38. 1. Introduction/2. Course curriculum overview.mp448.21MB
  39. 1. Introduction/3. Knowledge requirements.mp417.17MB
  40. 2. Machine Learning Pipeline - Research Environment/10. Getting Ready for Deployment - Demo.mp467.84MB
  41. 2. Machine Learning Pipeline - Research Environment/11. Bonus Machine Learning Pipeline Additional Resources.mp425.43MB
  42. 2. Machine Learning Pipeline - Research Environment/1. Machine Learning Pipeline Overview.mp460.54MB
  43. 2. Machine Learning Pipeline - Research Environment/2. Machine Learning Pipeline Feature Engineering.mp457.72MB
  44. 2. Machine Learning Pipeline - Research Environment/3. Machine Learning Pipeline Feature Selection.mp448.42MB
  45. 2. Machine Learning Pipeline - Research Environment/4. Machine Learning Pipeline Model Building.mp417.81MB
  46. 2. Machine Learning Pipeline - Research Environment/6. Data Analysis - Demo.mp4135.44MB
  47. 2. Machine Learning Pipeline - Research Environment/7. Feature Engineering - Demo.mp498.16MB
  48. 2. Machine Learning Pipeline - Research Environment/8. Feature Selection - Demo.mp435.38MB
  49. 2. Machine Learning Pipeline - Research Environment/9. Model Building - Demo.mp434.1MB
  50. 3. Machine Learning System Architecture/1. Machine Learning System Architecture and Why it Matters.mp410.9MB
  51. 3. Machine Learning System Architecture/2. Specific Challenges of Machine Learning Systems.mp439.06MB
  52. 3. Machine Learning System Architecture/3. Machine Learning System Approaches.mp430.02MB
  53. 3. Machine Learning System Architecture/4. Machine Learning System Component Breakdown.mp429.51MB
  54. 3. Machine Learning System Architecture/5. Building a Reproducible Machine Learning Pipeline.mp477.09MB
  55. 4. Building a Reproducible Machine Learning Pipeline/1. Production Code overview.mp419.22MB
  56. 4. Building a Reproducible Machine Learning Pipeline/2. Procedural Programming Pipeline.mp486.19MB
  57. 4. Building a Reproducible Machine Learning Pipeline/3. Designing a Custom Pipeline.mp4152.88MB
  58. 4. Building a Reproducible Machine Learning Pipeline/4. Leveraging a Third Party Pipeline Scikit-Learn.mp456.67MB
  59. 4. Building a Reproducible Machine Learning Pipeline/5. Third Party Pipeline Create Scikit-Learn compatible Feature Transformers.mp484.34MB
  60. 4. Building a Reproducible Machine Learning Pipeline/6. Third Party Pipeline Closing Remarks.mp49.97MB
  61. 4. Building a Reproducible Machine Learning Pipeline/8. Bonus Should feature selection be part of the pipeline.mp428.84MB
  62. 5. Course Setup and Key Tools/10. Section5.5c - Requirements files Introduction.mp49.55MB
  63. 5. Course Setup and Key Tools/11. Section5.5d - Virtualenv refresher.mp425.36MB
  64. 5. Course Setup and Key Tools/12. Section 5.6 - Text Editors IDEs.mp412.39MB
  65. 5. Course Setup and Key Tools/13. Section 5.7 - Engineering and Python Best Practices.mp431.4MB
  66. 5. Course Setup and Key Tools/14. Section 5.8 - Wrap Up.mp46.02MB
  67. 5. Course Setup and Key Tools/1. Section 5.1 - Introduction.mp415.94MB
  68. 5. Course Setup and Key Tools/2. Section 5.2 - Installing and Configuring Git.mp427.83MB
  69. 5. Course Setup and Key Tools/3. Section 5.3 - How to Use the Course Resources, Monorepos + Git Refresher.mp437.81MB
  70. 5. Course Setup and Key Tools/4. Section5.3b - Opening Pull Requests.mp436.17MB
  71. 5. Course Setup and Key Tools/5. Section5.3c - Primer on Monorepos.mp418.35MB
  72. 5. Course Setup and Key Tools/6. Section 5.4a - Operating System Differences and Gotchas.mp48.21MB
  73. 5. Course Setup and Key Tools/7. Section 5.4b - System Path and Pythonpath Demo.mp421.83MB
  74. 5. Course Setup and Key Tools/8. Section 5.5a - Quick Word for More Advanced Students.mp48.2MB
  75. 5. Course Setup and Key Tools/9. Section5.5b - Virtualenv Introduction.mp479.34MB
  76. 6. Creating a Machine Learning Pipeline Application/10. 6.9 - Wrap Up.mp413.25MB
  77. 6. Creating a Machine Learning Pipeline Application/1. 6.1 - Introduction.mp414.28MB
  78. 6. Creating a Machine Learning Pipeline Application/2. 6.2 - Training the Model.mp446.22MB
  79. 6. Creating a Machine Learning Pipeline Application/3. 6.3 - Connecting the Pipeline.mp445.35MB
  80. 6. Creating a Machine Learning Pipeline Application/5. 6.4 - Making Predictions with the Model.mp444.75MB
  81. 6. Creating a Machine Learning Pipeline Application/6. 6.5 - Data Validation in the Model Package.mp432.49MB
  82. 6. Creating a Machine Learning Pipeline Application/7. 6.6 - Feature Engineering in the Pipeline.mp426.21MB
  83. 6. Creating a Machine Learning Pipeline Application/8. 6.7 - Versioning and Logging.mp470.44MB
  84. 6. Creating a Machine Learning Pipeline Application/9. 6.8 - Building the Package.mp475.89MB
  85. 7. Serving the model via REST API/1. 7.1 - Introduction.mp425.05MB
  86. 7. Serving the model via REST API/2. 7.2 - Creating the API Skeleton.mp435.35MB
  87. 7. Serving the model via REST API/3. 7.2b - Flask Crash Course.mp417.81MB
  88. 7. Serving the model via REST API/4. 7.3 - Adding Config and Logging.mp433.04MB
  89. 7. Serving the model via REST API/5. 7.4 - Adding the Prediction Endpoint.mp438.94MB
  90. 7. Serving the model via REST API/6. 7.5 - Adding a Version Endpoint.mp415.41MB
  91. 7. Serving the model via REST API/7. 7.6 - API Schema Validation.mp478.1MB
  92. 7. Serving the model via REST API/8. 7.7 - Wrap Up.mp46.33MB
  93. 8. Continuous Integration and Deployment Pipelines/1.1 section8.1.mp4.mp441.89MB
  94. 8. Continuous Integration and Deployment Pipelines/1. 8.1 - Introduction to CICD.mp428.39MB
  95. 8. Continuous Integration and Deployment Pipelines/2. 8.2 - Setting up CircleCI.mp410.6MB
  96. 8. Continuous Integration and Deployment Pipelines/3. 8.3 - Setup Circle CI Config.mp450.83MB
  97. 8. Continuous Integration and Deployment Pipelines/4. 8.4 - Publishing the Model to Gemfury.mp469.12MB
  98. 8. Continuous Integration and Deployment Pipelines/5. 8.5 - Testing the CI Pipeline.mp450.13MB
  99. 8. Continuous Integration and Deployment Pipelines/6. 8.6 - Wrap Up.mp45.33MB
  100. 9. Differential Testing/1. 9.1 - Introduction.mp418.61MB
  101. 9. Differential Testing/2. 9.2 - Setting up Differential Tests.mp450.22MB
  102. 9. Differential Testing/3. 9.3 - Differential Tests in CI (Part 1 of 2).mp433.56MB
  103. 9. Differential Testing/4. 9.4 - Differential Tests in CI (Part 2 of 2).mp432.83MB
  104. 9. Differential Testing/5. 9.5 Wrap Up.mp412.68MB
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