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Lynda - DevOps for Data Scientists

  • 收录时间:2018-05-29 14:23:02
  • 文件大小:53MB
  • 下载次数:188
  • 最近下载:2020-12-09 00:41:02
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文件列表

  1. 4.3. Deployment Practices/12.Securing the data science models in production.mp4 6MB
  2. 2.1. Data Science Development Practices/05.Experimenting with data, features, and algorithms.mp4 4MB
  3. 4.3. Deployment Practices/13.Monitoring models in production.mp4 4MB
  4. 3.2. Data Science Models to Production/07.Version control for data science models.mp4 4MB
  5. 1.Introduction/01.Welcome.mp4 4MB
  6. 2.1. Data Science Development Practices/04.Collecting and munging data.mp4 4MB
  7. 3.2. Data Science Models to Production/08.Predictive Model Markup Language.mp4 4MB
  8. 5.4. Data Science Models in Containers/15.Creating a Dockerfile for data science models.mp4 3MB
  9. 5.4. Data Science Models in Containers/16.Data science Docker image repository.mp4 3MB
  10. 2.1. Data Science Development Practices/03.Data science and software engineering.mp4 3MB
  11. 2.1. Data Science Development Practices/06.Testing and validating models.mp4 2MB
  12. 6.Conclusion/17.Overview of DevOps best practices for data science.mp4 2MB
  13. 5.4. Data Science Models in Containers/14.Introduction to Docker.mp4 2MB
  14. 3.2. Data Science Models to Production/09.Deploying models with automation tools.mp4 2MB
  15. 4.3. Deployment Practices/11.Canary deployments.mp4 2MB
  16. 4.3. Deployment Practices/10.Deploying to staging environment.mp4 2MB
  17. 1.Introduction/02.Target audience.mp4 812KB
  18. 4.3. Deployment Practices/12.Securing the data science models in production.en.srt 8KB
  19. 3.2. Data Science Models to Production/07.Version control for data science models.en.srt 5KB
  20. 2.1. Data Science Development Practices/04.Collecting and munging data.en.srt 4KB
  21. 4.3. Deployment Practices/13.Monitoring models in production.en.srt 4KB
  22. 5.4. Data Science Models in Containers/15.Creating a Dockerfile for data science models.en.srt 4KB
  23. 3.2. Data Science Models to Production/08.Predictive Model Markup Language.en.srt 3KB
  24. 6.Conclusion/17.Overview of DevOps best practices for data science.en.srt 3KB
  25. 2.1. Data Science Development Practices/06.Testing and validating models.en.srt 3KB
  26. 5.4. Data Science Models in Containers/14.Introduction to Docker.en.srt 3KB
  27. 2.1. Data Science Development Practices/05.Experimenting with data, features, and algorithms.en.srt 3KB
  28. 5.4. Data Science Models in Containers/16.Data science Docker image repository.en.srt 3KB
  29. 3.2. Data Science Models to Production/09.Deploying models with automation tools.en.srt 3KB
  30. 2.1. Data Science Development Practices/03.Data science and software engineering.en.srt 2KB
  31. 4.3. Deployment Practices/11.Canary deployments.en.srt 2KB
  32. 1.Introduction/01.Welcome.en.srt 2KB
  33. 4.3. Deployment Practices/10.Deploying to staging environment.en.srt 2KB
  34. 1.Introduction/02.Target audience.en.srt 1KB