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Deploying Scalable Machine Learning for Data Science

  • 收录时间:2018-08-22 05:12:02
  • 文件大小:178MB
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  • 最近下载:2021-01-19 02:58:19
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文件列表

  1. 5.4. Running ML Services in Containers/19.Example Docker build process.mp4 11MB
  2. 2.1. The Need to Scale ML Models/06.Overview of tools and techniques for scalable ML.mp4 10MB
  3. 2.1. The Need to Scale ML Models/03.Building and running ML models for data scientists.mp4 10MB
  4. 4.3. Deploying ML Models as Services/14.Using Flask to create APIs for Python programs.mp4 9MB
  5. 3.2. Design Patterns for Scalable ML Applications/09.APIs for ML model services.mp4 8MB
  6. 5.4. Running ML Services in Containers/18.Building Docker images with Dockerfiles.mp4 8MB
  7. 2.1. The Need to Scale ML Models/04.Building and deploying ML models for production use.mp4 8MB
  8. 6.5. Scaling ML Services with Kubernetes/23.Creating a Kubernetes cluster.mp4 8MB
  9. 2.1. The Need to Scale ML Models/05.Definition of scaling ML for production.mp4 7MB
  10. 5.4. Running ML Services in Containers/16.Containers bundle ML model components.mp4 7MB
  11. 3.2. Design Patterns for Scalable ML Applications/10.Load balancing and clusters of servers.mp4 7MB
  12. 3.2. Design Patterns for Scalable ML Applications/07.Horizontal vs. vertical scaling.mp4 7MB
  13. 5.4. Running ML Services in Containers/20.Using Docker registries to manage images.mp4 6MB
  14. 4.3. Deploying ML Models as Services/13.Using Plumber to create APIs for R programs.mp4 6MB
  15. 5.4. Running ML Services in Containers/17.Introduction to Docker.mp4 6MB
  16. 6.5. Scaling ML Services with Kubernetes/22.Introduction to Kubernetes.mp4 6MB
  17. 6.5. Scaling ML Services with Kubernetes/24.Deploying containers in a Kubernetes cluster.mp4 5MB
  18. 6.5. Scaling ML Services with Kubernetes/25.Scaling up a Kubernetes cluster.mp4 5MB
  19. 3.2. Design Patterns for Scalable ML Applications/11.Scaling horizontally with containers.mp4 5MB
  20. 7.6. ML Services in Production/28.Service performance data.mp4 5MB
  21. 6.5. Scaling ML Services with Kubernetes/21.Running services in clusters.mp4 5MB
  22. 4.3. Deploying ML Models as Services/12.Services encapsulate ML models.mp4 4MB
  23. 7.6. ML Services in Production/27.Monitoring service performance.mp4 4MB
  24. 8.Conclusion/31.Best practices for scaling ML.mp4 4MB
  25. 1.Introduction/01.Scaling ML models.mp4 3MB
  26. 3.2. Design Patterns for Scalable ML Applications/08.Running models as services.mp4 3MB
  27. 7.6. ML Services in Production/30.Kubernetes monitoring.mp4 3MB
  28. 7.6. ML Services in Production/29.Docker container monitoring.mp4 3MB
  29. 1.Introduction/02.What you should know.mp4 3MB
  30. 8.Conclusion/32.Next steps.mp4 2MB
  31. 4.3. Deploying ML Models as Services/15.Best practices for API design for ML services.mp4 2MB
  32. 6.5. Scaling ML Services with Kubernetes/26.Autoscaling a Kubernetes cluster.mp4 2MB
  33. 2.1. The Need to Scale ML Models/04.Building and deploying ML models for production use.en.srt 9KB
  34. 2.1. The Need to Scale ML Models/03.Building and running ML models for data scientists.en.srt 9KB
  35. 4.3. Deploying ML Models as Services/14.Using Flask to create APIs for Python programs.en.srt 8KB
  36. 5.4. Running ML Services in Containers/18.Building Docker images with Dockerfiles.en.srt 8KB
  37. 2.1. The Need to Scale ML Models/06.Overview of tools and techniques for scalable ML.en.srt 8KB
  38. 2.1. The Need to Scale ML Models/05.Definition of scaling ML for production.en.srt 8KB
  39. 3.2. Design Patterns for Scalable ML Applications/09.APIs for ML model services.en.srt 8KB
  40. 3.2. Design Patterns for Scalable ML Applications/10.Load balancing and clusters of servers.en.srt 7KB
  41. 5.4. Running ML Services in Containers/20.Using Docker registries to manage images.en.srt 7KB
  42. 3.2. Design Patterns for Scalable ML Applications/07.Horizontal vs. vertical scaling.en.srt 7KB
  43. 4.3. Deploying ML Models as Services/13.Using Plumber to create APIs for R programs.en.srt 6KB
  44. 5.4. Running ML Services in Containers/19.Example Docker build process.en.srt 6KB
  45. 6.5. Scaling ML Services with Kubernetes/23.Creating a Kubernetes cluster.en.srt 6KB
  46. 5.4. Running ML Services in Containers/17.Introduction to Docker.en.srt 6KB
  47. 6.5. Scaling ML Services with Kubernetes/22.Introduction to Kubernetes.en.srt 6KB
  48. 6.5. Scaling ML Services with Kubernetes/24.Deploying containers in a Kubernetes cluster.en.srt 6KB
  49. 5.4. Running ML Services in Containers/16.Containers bundle ML model components.en.srt 5KB
  50. 6.5. Scaling ML Services with Kubernetes/21.Running services in clusters.en.srt 5KB
  51. 6.5. Scaling ML Services with Kubernetes/25.Scaling up a Kubernetes cluster.en.srt 5KB
  52. 3.2. Design Patterns for Scalable ML Applications/11.Scaling horizontally with containers.en.srt 5KB
  53. 4.3. Deploying ML Models as Services/12.Services encapsulate ML models.en.srt 4KB
  54. 7.6. ML Services in Production/27.Monitoring service performance.en.srt 4KB
  55. 3.2. Design Patterns for Scalable ML Applications/08.Running models as services.en.srt 4KB
  56. 7.6. ML Services in Production/28.Service performance data.en.srt 4KB
  57. 8.Conclusion/31.Best practices for scaling ML.en.srt 3KB
  58. 7.6. ML Services in Production/30.Kubernetes monitoring.en.srt 3KB
  59. 7.6. ML Services in Production/29.Docker container monitoring.en.srt 3KB
  60. 8.Conclusion/32.Next steps.en.srt 2KB
  61. Exercise Files/Ex_Files_Scalable_ML_Data.zip 2KB
  62. 4.3. Deploying ML Models as Services/15.Best practices for API design for ML services.en.srt 2KB
  63. 1.Introduction/02.What you should know.en.srt 2KB
  64. 6.5. Scaling ML Services with Kubernetes/26.Autoscaling a Kubernetes cluster.en.srt 2KB
  65. 1.Introduction/01.Scaling ML models.en.srt 2KB