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[] Udemy - Deployment of Machine Learning Models

  • 收录时间:2020-02-25 03:04:19
  • 文件大小:4GB
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  • 最近下载:2021-01-14 15:08:25
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文件列表

  1. 4. Building a Reproducible Machine Learning Pipeline/3. Designing a Custom Pipeline.mp4 153MB
  2. 2. Machine Learning Pipeline - Research Environment/6. Data Analysis - Demo.mp4 135MB
  3. 2. Machine Learning Pipeline - Research Environment/7. Feature Engineering - Demo.mp4 98MB
  4. 13. A Deep Learning Model with Big Data/3. Building a CNN in the Research Environment.mp4 89MB
  5. 4. Building a Reproducible Machine Learning Pipeline/2. Procedural Programming Pipeline.mp4 86MB
  6. 4. Building a Reproducible Machine Learning Pipeline/5. Third Party Pipeline Create Scikit-Learn compatible Feature Transformers.mp4 84MB
  7. 13. A Deep Learning Model with Big Data/4. Production Code for a CNN Learning Pipeline.mp4 80MB
  8. 5. Course Setup and Key Tools/9. Section5.5b - Virtualenv Introduction.mp4 79MB
  9. 7. Serving the model via REST API/7. 7.6 - API Schema Validation.mp4 78MB
  10. 3. Machine Learning System Architecture/5. Building a Reproducible Machine Learning Pipeline.mp4 77MB
  11. 6. Creating a Machine Learning Pipeline Application/9. 6.8 - Building the Package.mp4 76MB
  12. 13. A Deep Learning Model with Big Data/8. 13.8 - Packaging the CNN.mp4 72MB
  13. 6. Creating a Machine Learning Pipeline Application/8. 6.7 - Versioning and Logging.mp4 70MB
  14. 8. Continuous Integration and Deployment Pipelines/4. 8.4 - Publishing the Model to Gemfury.mp4 69MB
  15. 2. Machine Learning Pipeline - Research Environment/10. Getting Ready for Deployment - Demo.mp4 68MB
  16. 2. Machine Learning Pipeline - Research Environment/1. Machine Learning Pipeline Overview.mp4 61MB
  17. 12. Deploying to IaaS (AWS ECS)/12. 12.11 - Creating the ECS Cluster with the EC2 Launch Method.mp4 60MB
  18. 2. Machine Learning Pipeline - Research Environment/2. Machine Learning Pipeline Feature Engineering.mp4 58MB
  19. 4. Building a Reproducible Machine Learning Pipeline/4. Leveraging a Third Party Pipeline Scikit-Learn.mp4 57MB
  20. 8. Continuous Integration and Deployment Pipelines/3. 8.3 - Setup Circle CI Config.mp4 51MB
  21. 9. Differential Testing/2. 9.2 - Setting up Differential Tests.mp4 50MB
  22. 8. Continuous Integration and Deployment Pipelines/5. 8.5 - Testing the CI Pipeline.mp4 50MB
  23. 12. Deploying to IaaS (AWS ECS)/10. 12.9 - Uploading Images to the Elastic Container Registry (ECR).mp4 50MB
  24. 2. Machine Learning Pipeline - Research Environment/3. Machine Learning Pipeline Feature Selection.mp4 48MB
  25. 1. Introduction/2. Course curriculum overview.mp4 48MB
  26. 11. Running Apps with Containers (Docker)/5. 11.5 Releasing to Heroku with Docker.mp4 47MB
  27. 6. Creating a Machine Learning Pipeline Application/2. 6.2 - Training the Model.mp4 46MB
  28. 6. Creating a Machine Learning Pipeline Application/3. 6.3 - Connecting the Pipeline.mp4 45MB
  29. 6. Creating a Machine Learning Pipeline Application/5. 6.4 - Making Predictions with the Model.mp4 45MB
  30. 8. Continuous Integration and Deployment Pipelines/1.1 section8.1.mp4.mp4 42MB
  31. 13. A Deep Learning Model with Big Data/9. 13.9 - Adding the CNN to the API.mp4 41MB
  32. 3. Machine Learning System Architecture/2. Specific Challenges of Machine Learning Systems.mp4 39MB
  33. 7. Serving the model via REST API/5. 7.4 - Adding the Prediction Endpoint.mp4 39MB
  34. 12. Deploying to IaaS (AWS ECS)/11. 12.10 - Creating the ECS Cluster with Fargate Launch Method.mp4 38MB
  35. 5. Course Setup and Key Tools/3. Section 5.3 - How to Use the Course Resources, Monorepos + Git Refresher.mp4 38MB
  36. 1. Introduction/1. Introduction to the course.mp4 38MB
  37. 5. Course Setup and Key Tools/4. Section5.3b - Opening Pull Requests.mp4 36MB
  38. 2. Machine Learning Pipeline - Research Environment/8. Feature Selection - Demo.mp4 35MB
  39. 7. Serving the model via REST API/2. 7.2 - Creating the API Skeleton.mp4 35MB
  40. 2. Machine Learning Pipeline - Research Environment/9. Model Building - Demo.mp4 34MB
  41. 9. Differential Testing/3. 9.3 - Differential Tests in CI (Part 1 of 2).mp4 34MB
  42. 7. Serving the model via REST API/4. 7.3 - Adding Config and Logging.mp4 33MB
  43. 9. Differential Testing/4. 9.4 - Differential Tests in CI (Part 2 of 2).mp4 33MB
  44. 6. Creating a Machine Learning Pipeline Application/6. 6.5 - Data Validation in the Model Package.mp4 32MB
  45. 10. Deploying to a PaaS (Heroku) without Containers/3. 10.3 - Heroku Config.mp4 32MB
  46. 11. Running Apps with Containers (Docker)/1. 11.1 Introduction to Containers and Docker.mp4 32MB
  47. 5. Course Setup and Key Tools/13. Section 5.7 - Engineering and Python Best Practices.mp4 31MB
  48. 12. Deploying to IaaS (AWS ECS)/13. 12.12 - Updating the Cluster Containers.mp4 31MB
  49. 3. Machine Learning System Architecture/3. Machine Learning System Approaches.mp4 30MB
  50. 3. Machine Learning System Architecture/4. Machine Learning System Component Breakdown.mp4 30MB
  51. 10. Deploying to a PaaS (Heroku) without Containers/5. 10.5 - Deploying to Heroku via CI.mp4 29MB
  52. 12. Deploying to IaaS (AWS ECS)/7. 12.6 - Installing the AWS CLI.mp4 29MB
  53. 4. Building a Reproducible Machine Learning Pipeline/8. Bonus Should feature selection be part of the pipeline.mp4 29MB
  54. 8. Continuous Integration and Deployment Pipelines/1. 8.1 - Introduction to CICD.mp4 28MB
  55. 5. Course Setup and Key Tools/2. Section 5.2 - Installing and Configuring Git.mp4 28MB
  56. 10. Deploying to a PaaS (Heroku) without Containers/1. 10.1 - Introduction.mp4 27MB
  57. 11. Running Apps with Containers (Docker)/4. 11.4 Building and Running the Docker Container.mp4 27MB
  58. 11. Running Apps with Containers (Docker)/2. 11.2 Installing Docker.mp4 27MB
  59. 6. Creating a Machine Learning Pipeline Application/7. 6.6 - Feature Engineering in the Pipeline.mp4 26MB
  60. 12. Deploying to IaaS (AWS ECS)/2. 12.2 - AWS Costs and Caution.mp4 25MB
  61. 2. Machine Learning Pipeline - Research Environment/11. Bonus Machine Learning Pipeline Additional Resources.mp4 25MB
  62. 5. Course Setup and Key Tools/11. Section5.5d - Virtualenv refresher.mp4 25MB
  63. 7. Serving the model via REST API/1. 7.1 - Introduction.mp4 25MB
  64. 12. Deploying to IaaS (AWS ECS)/4. 12.3b - Container Orchestration Options Kubernetes, ECS, Docker Swarm.mp4 24MB
  65. 12. Deploying to IaaS (AWS ECS)/15. 12.14 - Deploying to ECS via the CI pipeline.mp4 24MB
  66. 12. Deploying to IaaS (AWS ECS)/6. 12.5 - Setting Permissions with IAM.mp4 23MB
  67. 12. Deploying to IaaS (AWS ECS)/3. 12.3a - Intro to AWS ECS.mp4 23MB
  68. 5. Course Setup and Key Tools/7. Section 5.4b - System Path and Pythonpath Demo.mp4 22MB
  69. 11. Running Apps with Containers (Docker)/3. 11.3 Creating Our API App Dockerfile.mp4 22MB
  70. 10. Deploying to a PaaS (Heroku) without Containers/2. 10.2 - Heroku Account Creation.mp4 21MB
  71. 12. Deploying to IaaS (AWS ECS)/8. 12.7 - Configuring the AWS CLI.mp4 21MB
  72. 13. A Deep Learning Model with Big Data/10. 13.10 - Additional Considerations and Wrap Up.mp4 21MB
  73. 4. Building a Reproducible Machine Learning Pipeline/1. Production Code overview.mp4 19MB
  74. 12. Deploying to IaaS (AWS ECS)/1. 12.1 - Introduction to AWS.mp4 19MB
  75. 9. Differential Testing/1. 9.1 - Introduction.mp4 19MB
  76. 5. Course Setup and Key Tools/5. Section5.3c - Primer on Monorepos.mp4 18MB
  77. 2. Machine Learning Pipeline - Research Environment/4. Machine Learning Pipeline Model Building.mp4 18MB
  78. 7. Serving the model via REST API/3. 7.2b - Flask Crash Course.mp4 18MB
  79. 1. Introduction/3. Knowledge requirements.mp4 17MB
  80. 13. A Deep Learning Model with Big Data/5. Reproducibility in Neural Networks.mp4 17MB
  81. 13. A Deep Learning Model with Big Data/2. Introduction to a Large Dataset - Plant Seedlings Images.mp4 17MB
  82. 5. Course Setup and Key Tools/1. Section 5.1 - Introduction.mp4 16MB
  83. 7. Serving the model via REST API/6. 7.5 - Adding a Version Endpoint.mp4 15MB
  84. 13. A Deep Learning Model with Big Data/1. Challenges of using Big Data in Machine Learning.mp4 15MB
  85. 6. Creating a Machine Learning Pipeline Application/1. 6.1 - Introduction.mp4 14MB
  86. 10. Deploying to a PaaS (Heroku) without Containers/6. 10.6 - Wrap Up.mp4 14MB
  87. 1. Introduction/6.1 DMLM_Slides.zip.zip 13MB
  88. 6. Creating a Machine Learning Pipeline Application/10. 6.9 - Wrap Up.mp4 13MB
  89. 9. Differential Testing/5. 9.5 Wrap Up.mp4 13MB
  90. 5. Course Setup and Key Tools/12. Section 5.6 - Text Editors IDEs.mp4 12MB
  91. 10. Deploying to a PaaS (Heroku) without Containers/4. 10.4 - Testing the Deployment Manually.mp4 12MB
  92. 3. Machine Learning System Architecture/1. Machine Learning System Architecture and Why it Matters.mp4 11MB
  93. 8. Continuous Integration and Deployment Pipelines/2. 8.2 - Setting up CircleCI.mp4 11MB
  94. 4. Building a Reproducible Machine Learning Pipeline/6. Third Party Pipeline Closing Remarks.mp4 10MB
  95. 5. Course Setup and Key Tools/10. Section5.5c - Requirements files Introduction.mp4 10MB
  96. 12. Deploying to IaaS (AWS ECS)/9. 12.8 - Intro the Elastic Container Registry (ECR).mp4 9MB
  97. 12. Deploying to IaaS (AWS ECS)/16. 12.15 - Wrap Up.mp4 9MB
  98. 5. Course Setup and Key Tools/6. Section 5.4a - Operating System Differences and Gotchas.mp4 8MB
  99. 5. Course Setup and Key Tools/8. Section 5.5a - Quick Word for More Advanced Students.mp4 8MB
  100. 11. Running Apps with Containers (Docker)/6. 11.6 - Wrap Up.mp4 8MB
  101. 12. Deploying to IaaS (AWS ECS)/14. 12.13 - Tearing down the ECS Cluster.mp4 7MB
  102. 7. Serving the model via REST API/8. 7.7 - Wrap Up.mp4 6MB
  103. 5. Course Setup and Key Tools/14. Section 5.8 - Wrap Up.mp4 6MB
  104. 8. Continuous Integration and Deployment Pipelines/6. 8.6 - Wrap Up.mp4 5MB
  105. 12. Deploying to IaaS (AWS ECS)/5. 12.4 - Create an AWS Account.mp4 5MB
  106. 1. Introduction/7.1 DMLM_Notes.zip.zip 2MB
  107. 13. A Deep Learning Model with Big Data/3.1 CNN_Analysis_and Model.zip.zip 2MB
  108. 2. Machine Learning Pipeline - Research Environment/5.1 MLPipeline-Notebooks.zip.zip 1MB
  109. 14. Common Issues found during deployment/1.1 Troubleshooting.pdf.pdf 223KB
  110. 7. Serving the model via REST API/2.1 Section7.2_Notes.pdf.pdf 146KB
  111. 8. Continuous Integration and Deployment Pipelines/4.1 Section8.4_Notes.pdf.pdf 101KB
  112. 9. Differential Testing/4.1 Section9.4_Notes.pdf.pdf 101KB
  113. 5. Course Setup and Key Tools/4.1 Section5.3b_Notes.pdf.pdf 100KB
  114. 6. Creating a Machine Learning Pipeline Application/4.1 Section6.4_Notes.pdf.pdf 99KB
  115. 6. Creating a Machine Learning Pipeline Application/5.1 Section6.4_Notes.pdf.pdf 99KB
  116. 5. Course Setup and Key Tools/2.1 Section5.2_Notes.pdf.pdf 96KB
  117. 11. Running Apps with Containers (Docker)/4.1 Section11.4_Notes.pdf.pdf 94KB
  118. 5. Course Setup and Key Tools/9.1 Section5.5b_Notes.pdf.pdf 92KB
  119. 5. Course Setup and Key Tools/13.1 Section5.7_Notes.pdf.pdf 89KB
  120. 8. Continuous Integration and Deployment Pipelines/5.1 Section8.5_Notes.pdf.pdf 89KB
  121. 6. Creating a Machine Learning Pipeline Application/9.1 Section6.8_Notes.pdf.pdf 86KB
  122. 5. Course Setup and Key Tools/3.1 Section5.3a_Notes.pdf.pdf 86KB
  123. 5. Course Setup and Key Tools/12.1 Section5.6_Notes.pdf.pdf 85KB
  124. 7. Serving the model via REST API/6.1 Section7.5_Notes.pdf.pdf 84KB
  125. 5. Course Setup and Key Tools/11.1 Section5.5_Notes.pdf.pdf 84KB
  126. 7. Serving the model via REST API/7.1 Section7.6_Notes.pdf.pdf 84KB
  127. 7. Serving the model via REST API/4.1 Section7.3_Notes.pdf.pdf 83KB
  128. 12. Deploying to IaaS (AWS ECS)/8.1 Section12.7_Notes.pdf.pdf 83KB
  129. 11. Running Apps with Containers (Docker)/6.1 Section11.6_Notes.pdf.pdf 82KB
  130. 7. Serving the model via REST API/3.1 Section7.2b_Notes.pdf.pdf 82KB
  131. 7. Serving the model via REST API/5.1 Section7.4_Notes.pdf.pdf 82KB
  132. 6. Creating a Machine Learning Pipeline Application/8.1 Section6.7_Notes.pdf.pdf 81KB
  133. 6. Creating a Machine Learning Pipeline Application/6.1 Section6.5_Notes.pdf.pdf 79KB
  134. 6. Creating a Machine Learning Pipeline Application/7.1 Section6.6_Notes.pdf.pdf 79KB
  135. 3. Machine Learning System Architecture/4.1 Section3.4_Notes.pdf.pdf 79KB
  136. 11. Running Apps with Containers (Docker)/2.1 Section11.2_Notes.pdf.pdf 78KB
  137. 10. Deploying to a PaaS (Heroku) without Containers/1.1 Section10.1_Notes.pdf.pdf 77KB
  138. 6. Creating a Machine Learning Pipeline Application/3.1 Section6.3_Notes.pdf.pdf 75KB
  139. 12. Deploying to IaaS (AWS ECS)/2.1 Section12.2_Notes.pdf.pdf 75KB
  140. 5. Course Setup and Key Tools/10.1 Section5.5c_Notes.pdf.pdf 74KB
  141. 12. Deploying to IaaS (AWS ECS)/10.1 Section12.9_Notes.pdf.pdf 72KB
  142. 13. A Deep Learning Model with Big Data/8.1 Section13.8_Notes.pdf.pdf 72KB
  143. 3. Machine Learning System Architecture/3.1 Section3.3_Notes.pdf.pdf 71KB
  144. 11. Running Apps with Containers (Docker)/1.1 Section11.1_Notes.pdf.pdf 70KB
  145. 10. Deploying to a PaaS (Heroku) without Containers/4.1 Section10.4_Notes.pdf.pdf 70KB
  146. 9. Differential Testing/5.1 Section9.5_Notes.pdf.pdf 69KB
  147. 10. Deploying to a PaaS (Heroku) without Containers/3.1 Section10.3_Notes.pdf.pdf 69KB
  148. 10. Deploying to a PaaS (Heroku) without Containers/5.1 Section10.5_Notes.pdf.pdf 68KB
  149. 12. Deploying to IaaS (AWS ECS)/13.1 Section12.12_Notes.pdf.pdf 67KB
  150. 9. Differential Testing/2.1 Section9.2_Notes.pdf.pdf 65KB
  151. 5. Course Setup and Key Tools/6.1 Section5.4_Notes.pdf.pdf 64KB
  152. 5. Course Setup and Key Tools/7.1 Section5.4_Notes.pdf.pdf 64KB
  153. 12. Deploying to IaaS (AWS ECS)/15.1 Section12.14_Notes.pdf.pdf 64KB
  154. 10. Deploying to a PaaS (Heroku) without Containers/6.1 Section10.6_Notes.pdf.pdf 64KB
  155. 8. Continuous Integration and Deployment Pipelines/3.1 Section8.3_Notes.pdf.pdf 64KB
  156. 7. Serving the model via REST API/1.1 Section7.1_Notes.pdf.pdf 63KB
  157. 10. Deploying to a PaaS (Heroku) without Containers/2.1 Section10.2_Notes.pdf.pdf 61KB
  158. 12. Deploying to IaaS (AWS ECS)/14.1 Section12.13_Notes.pdf.pdf 60KB
  159. 13. A Deep Learning Model with Big Data/10.1 Section13.10_Notes.pdf.pdf 60KB
  160. 8. Continuous Integration and Deployment Pipelines/2.1 Section8.2_Notes.pdf.pdf 59KB
  161. 11. Running Apps with Containers (Docker)/3.1 Section11.3_Notes.pdf.pdf 58KB
  162. 12. Deploying to IaaS (AWS ECS)/6.1 Section12.5_Notes.pdf.pdf 58KB
  163. 12. Deploying to IaaS (AWS ECS)/12.1 Section12.11_Notes.pdf.pdf 57KB
  164. 12. Deploying to IaaS (AWS ECS)/7.1 Section12.6_Notes.pdf.pdf 57KB
  165. 12. Deploying to IaaS (AWS ECS)/11.1 Section12.10_Notes.pdf.pdf 57KB
  166. 12. Deploying to IaaS (AWS ECS)/16.1 Section12.15_Notes.pdf.pdf 57KB
  167. 11. Running Apps with Containers (Docker)/5.1 Section11.5_Notes.pdf.pdf 57KB
  168. 12. Deploying to IaaS (AWS ECS)/9.1 Section12.8_Notes.pdf.pdf 55KB
  169. 12. Deploying to IaaS (AWS ECS)/3.1 Section12.3_Notes.pdf.pdf 55KB
  170. 12. Deploying to IaaS (AWS ECS)/4.1 Section12.3_Notes.pdf.pdf 55KB
  171. 5. Course Setup and Key Tools/5.1 Section5.3c_Notes.pdf.pdf 54KB
  172. 12. Deploying to IaaS (AWS ECS)/5.1 Section12.4_Notes.pdf.pdf 54KB
  173. 13. A Deep Learning Model with Big Data/9.1 Section13.9_Notes.pdf.pdf 53KB
  174. 2. Machine Learning Pipeline - Research Environment/6. Data Analysis - Demo.vtt 21KB
  175. 4. Building a Reproducible Machine Learning Pipeline/3. Designing a Custom Pipeline.vtt 20KB
  176. 2. Machine Learning Pipeline - Research Environment/7. Feature Engineering - Demo.vtt 14KB
  177. 4. Building a Reproducible Machine Learning Pipeline/5. Third Party Pipeline Create Scikit-Learn compatible Feature Transformers.vtt 14KB
  178. 4. Building a Reproducible Machine Learning Pipeline/2. Procedural Programming Pipeline.vtt 13KB
  179. 3. Machine Learning System Architecture/5. Building a Reproducible Machine Learning Pipeline.vtt 13KB
  180. 2. Machine Learning Pipeline - Research Environment/3. Machine Learning Pipeline Feature Selection.vtt 11KB
  181. 13. A Deep Learning Model with Big Data/3. Building a CNN in the Research Environment.vtt 10KB
  182. 4. Building a Reproducible Machine Learning Pipeline/4. Leveraging a Third Party Pipeline Scikit-Learn.vtt 10KB
  183. 2. Machine Learning Pipeline - Research Environment/2. Machine Learning Pipeline Feature Engineering.vtt 9KB
  184. 1. Introduction/2. Course curriculum overview.vtt 9KB
  185. 13. A Deep Learning Model with Big Data/4. Production Code for a CNN Learning Pipeline.vtt 9KB
  186. 2. Machine Learning Pipeline - Research Environment/10. Getting Ready for Deployment - Demo.vtt 9KB
  187. 2. Machine Learning Pipeline - Research Environment/1. Machine Learning Pipeline Overview.vtt 9KB
  188. 1. Introduction/1. Introduction to the course.vtt 8KB
  189. 6. Creating a Machine Learning Pipeline Application/9. 6.8 - Building the Package.vtt 7KB
  190. 8. Continuous Integration and Deployment Pipelines/4. 8.4 - Publishing the Model to Gemfury.vtt 7KB
  191. 7. Serving the model via REST API/7. 7.6 - API Schema Validation.vtt 7KB
  192. 6. Creating a Machine Learning Pipeline Application/8. 6.7 - Versioning and Logging.vtt 7KB
  193. 5. Course Setup and Key Tools/9. Section5.5b - Virtualenv Introduction.vtt 7KB
  194. 4. Building a Reproducible Machine Learning Pipeline/8. Bonus Should feature selection be part of the pipeline.vtt 7KB
  195. 3. Machine Learning System Architecture/2. Specific Challenges of Machine Learning Systems.vtt 7KB
  196. 12. Deploying to IaaS (AWS ECS)/12. 12.11 - Creating the ECS Cluster with the EC2 Launch Method.vtt 6KB
  197. 8. Continuous Integration and Deployment Pipelines/3. 8.3 - Setup Circle CI Config.vtt 6KB
  198. 13. A Deep Learning Model with Big Data/8. 13.8 - Packaging the CNN.vtt 6KB
  199. 3. Machine Learning System Architecture/4. Machine Learning System Component Breakdown.vtt 6KB
  200. 5. Course Setup and Key Tools/13. Section 5.7 - Engineering and Python Best Practices.vtt 6KB
  201. 4. Building a Reproducible Machine Learning Pipeline/5.1 preprocessors.py.py 5KB
  202. 6. Creating a Machine Learning Pipeline Application/2. 6.2 - Training the Model.vtt 5KB
  203. 11. Running Apps with Containers (Docker)/5. 11.5 Releasing to Heroku with Docker.vtt 5KB
  204. 3. Machine Learning System Architecture/3. Machine Learning System Approaches.vtt 5KB
  205. 2. Machine Learning Pipeline - Research Environment/9. Model Building - Demo.vtt 5KB
  206. 8. Continuous Integration and Deployment Pipelines/1. 8.1 - Introduction to CICD.vtt 5KB
  207. 8. Continuous Integration and Deployment Pipelines/5. 8.5 - Testing the CI Pipeline.vtt 5KB
  208. 10. Deploying to a PaaS (Heroku) without Containers/3. 10.3 - Heroku Config.vtt 5KB
  209. 6. Creating a Machine Learning Pipeline Application/5. 6.4 - Making Predictions with the Model.vtt 5KB
  210. 13. A Deep Learning Model with Big Data/4.1 CNNProdCode.zip.zip 5KB
  211. 12. Deploying to IaaS (AWS ECS)/3. 12.3a - Intro to AWS ECS.vtt 4KB
  212. 10. Deploying to a PaaS (Heroku) without Containers/1. 10.1 - Introduction.vtt 4KB
  213. 1. Introduction/3. Knowledge requirements.vtt 4KB
  214. 11. Running Apps with Containers (Docker)/1. 11.1 Introduction to Containers and Docker.vtt 4KB
  215. 12. Deploying to IaaS (AWS ECS)/10. 12.9 - Uploading Images to the Elastic Container Registry (ECR).vtt 4KB
  216. 9. Differential Testing/2. 9.2 - Setting up Differential Tests.vtt 4KB
  217. 2. Machine Learning Pipeline - Research Environment/8. Feature Selection - Demo.vtt 4KB
  218. 6. Creating a Machine Learning Pipeline Application/3. 6.3 - Connecting the Pipeline.vtt 4KB
  219. 7. Serving the model via REST API/5. 7.4 - Adding the Prediction Endpoint.vtt 4KB
  220. 13. A Deep Learning Model with Big Data/9. 13.9 - Adding the CNN to the API.vtt 4KB
  221. 1. Introduction/5. Guide to Setting up your Computer.html 4KB
  222. 7. Serving the model via REST API/4. 7.3 - Adding Config and Logging.vtt 4KB
  223. 12. Deploying to IaaS (AWS ECS)/11. 12.10 - Creating the ECS Cluster with Fargate Launch Method.vtt 4KB
  224. 13. A Deep Learning Model with Big Data/5. Reproducibility in Neural Networks.vtt 4KB
  225. 7. Serving the model via REST API/1. 7.1 - Introduction.vtt 4KB
  226. 10. Deploying to a PaaS (Heroku) without Containers/5. 10.5 - Deploying to Heroku via CI.vtt 4KB
  227. 11. Running Apps with Containers (Docker)/4. 11.4 Building and Running the Docker Container.vtt 4KB
  228. 13. A Deep Learning Model with Big Data/6. Setting the Seed for Keras.html 4KB
  229. 12. Deploying to IaaS (AWS ECS)/4. 12.3b - Container Orchestration Options Kubernetes, ECS, Docker Swarm.vtt 4KB
  230. 12. Deploying to IaaS (AWS ECS)/13. 12.12 - Updating the Cluster Containers.vtt 4KB
  231. 2. Machine Learning Pipeline - Research Environment/4. Machine Learning Pipeline Model Building.vtt 4KB
  232. 1. Introduction/4. How to Approach this course.html 3KB
  233. 4. Building a Reproducible Machine Learning Pipeline/1. Production Code overview.vtt 3KB
  234. 5. Course Setup and Key Tools/3. Section 5.3 - How to Use the Course Resources, Monorepos + Git Refresher.vtt 3KB
  235. 12. Deploying to IaaS (AWS ECS)/1. 12.1 - Introduction to AWS.vtt 3KB
  236. 9. Differential Testing/4. 9.4 - Differential Tests in CI (Part 2 of 2).vtt 3KB
  237. 5. Course Setup and Key Tools/2. Section 5.2 - Installing and Configuring Git.vtt 3KB
  238. 6. Creating a Machine Learning Pipeline Application/6. 6.5 - Data Validation in the Model Package.vtt 3KB
  239. 7. Serving the model via REST API/3. 7.2b - Flask Crash Course.vtt 3KB
  240. 13. A Deep Learning Model with Big Data/10. 13.10 - Additional Considerations and Wrap Up.vtt 3KB
  241. 12. Deploying to IaaS (AWS ECS)/6. 12.5 - Setting Permissions with IAM.vtt 3KB
  242. 9. Differential Testing/3. 9.3 - Differential Tests in CI (Part 1 of 2).vtt 3KB
  243. 5. Course Setup and Key Tools/4. Section5.3b - Opening Pull Requests.vtt 3KB
  244. 11. Running Apps with Containers (Docker)/3. 11.3 Creating Our API App Dockerfile.vtt 3KB
  245. 5. Course Setup and Key Tools/11. Section5.5d - Virtualenv refresher.vtt 3KB
  246. 4. Building a Reproducible Machine Learning Pipeline/3.1 CustomPipeline.zip.zip 3KB
  247. 12. Deploying to IaaS (AWS ECS)/8. 12.7 - Configuring the AWS CLI.vtt 3KB
  248. 4. Building a Reproducible Machine Learning Pipeline/2.1 ProceduralPrograming.zip.zip 3KB
  249. 5. Course Setup and Key Tools/7. Section 5.4b - System Path and Pythonpath Demo.vtt 3KB
  250. 9. Differential Testing/1. 9.1 - Introduction.vtt 3KB
  251. 12. Deploying to IaaS (AWS ECS)/15. 12.14 - Deploying to ECS via the CI pipeline.vtt 3KB
  252. 2. Machine Learning Pipeline - Research Environment/11. Bonus Machine Learning Pipeline Additional Resources.vtt 3KB
  253. 6. Creating a Machine Learning Pipeline Application/7. 6.6 - Feature Engineering in the Pipeline.vtt 3KB
  254. 2. Machine Learning Pipeline - Research Environment/12. Randomness in Machine Learning - Setting the Seed.html 3KB
  255. 11. Running Apps with Containers (Docker)/2. 11.2 Installing Docker.vtt 2KB
  256. 13. A Deep Learning Model with Big Data/1. Challenges of using Big Data in Machine Learning.vtt 2KB
  257. 12. Deploying to IaaS (AWS ECS)/2. 12.2 - AWS Costs and Caution.vtt 2KB
  258. 4. Building a Reproducible Machine Learning Pipeline/6. Third Party Pipeline Closing Remarks.vtt 2KB
  259. 12. Deploying to IaaS (AWS ECS)/7. 12.6 - Installing the AWS CLI.vtt 2KB
  260. 6. Creating a Machine Learning Pipeline Application/10. 6.9 - Wrap Up.vtt 2KB
  261. 10. Deploying to a PaaS (Heroku) without Containers/2. 10.2 - Heroku Account Creation.vtt 2KB
  262. 6. Creating a Machine Learning Pipeline Application/1. 6.1 - Introduction.vtt 2KB
  263. 3. Machine Learning System Architecture/1. Machine Learning System Architecture and Why it Matters.vtt 2KB
  264. 5. Course Setup and Key Tools/10. Section5.5c - Requirements files Introduction.vtt 2KB
  265. 10. Deploying to a PaaS (Heroku) without Containers/6. 10.6 - Wrap Up.vtt 2KB
  266. 5. Course Setup and Key Tools/5. Section5.3c - Primer on Monorepos.vtt 2KB
  267. 5. Course Setup and Key Tools/1. Section 5.1 - Introduction.vtt 2KB
  268. 9. Differential Testing/5. 9.5 Wrap Up.vtt 2KB
  269. 13. A Deep Learning Model with Big Data/2. Introduction to a Large Dataset - Plant Seedlings Images.vtt 2KB
  270. 7. Serving the model via REST API/6. 7.5 - Adding a Version Endpoint.vtt 2KB
  271. 12. Deploying to IaaS (AWS ECS)/16. 12.15 - Wrap Up.vtt 2KB
  272. 4. Building a Reproducible Machine Learning Pipeline/7. Scikit-Learn Pipeline - Code.html 2KB
  273. 5. Course Setup and Key Tools/6. Section 5.4a - Operating System Differences and Gotchas.vtt 2KB
  274. 10. Deploying to a PaaS (Heroku) without Containers/4. 10.4 - Testing the Deployment Manually.vtt 2KB
  275. 5. Course Setup and Key Tools/12. Section 5.6 - Text Editors IDEs.vtt 2KB
  276. 11. Running Apps with Containers (Docker)/6. 11.6 - Wrap Up.vtt 2KB
  277. 8. Continuous Integration and Deployment Pipelines/2. 8.2 - Setting up CircleCI.vtt 2KB
  278. 4. Building a Reproducible Machine Learning Pipeline/9. Bonus Additional Resources on Scikit-Learn.html 1KB
  279. 5. Course Setup and Key Tools/14. Section 5.8 - Wrap Up.vtt 1KB
  280. 7. Serving the model via REST API/8. 7.7 - Wrap Up.vtt 1KB
  281. 6. Creating a Machine Learning Pipeline Application/4. 6.4a - Gotchas.html 1KB
  282. 12. Deploying to IaaS (AWS ECS)/9. 12.8 - Intro the Elastic Container Registry (ECR).vtt 1KB
  283. 4. Building a Reproducible Machine Learning Pipeline/10. Bonus Resources to Improve as a Python Developer.html 1KB
  284. 3. Machine Learning System Architecture/6. Additional Reading Resources.html 1KB
  285. 1. Introduction/8. FAQ Where can I learn more about the required skills.html 1KB
  286. 8. Continuous Integration and Deployment Pipelines/6. 8.6 - Wrap Up.vtt 998B
  287. 15. Final Section/1. Bonus Discount for other courses.html 814B
  288. 5. Course Setup and Key Tools/8. Section 5.5a - Quick Word for More Advanced Students.vtt 768B
  289. 12. Deploying to IaaS (AWS ECS)/14. 12.13 - Tearing down the ECS Cluster.vtt 740B
  290. 2. Machine Learning Pipeline - Research Environment/14. FAQ Where can I learn more about the pipeline steps.html 623B
  291. 12. Deploying to IaaS (AWS ECS)/5. 12.4 - Create an AWS Account.vtt 603B
  292. 2. Machine Learning Pipeline - Research Environment/13. Randomness in Machine Learning - Additional reading resources.html 522B
  293. 13. A Deep Learning Model with Big Data/7. Seed for Neural Networks - Additional reading resources.html 397B
  294. 14. Common Issues found during deployment/1. Troubleshooting.html 105B
  295. 2. Machine Learning Pipeline - Research Environment/5. Jupyter notebooks covered in this section.html 93B
  296. 1. Introduction/6. Slides covered in this course.html 92B
  297. 1. Introduction/7. Notes covered in this course.html 91B
  298. [DesireCourse.Com].url 51B
  299. 7. Serving the model via REST API/2. 7.2 - Creating the API Skeleton.vtt 0B