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Lynda - DevOps for Data Scientists

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  1. Magnet Link：Magnet LinkMagnet Link
  2. File Size：54.74 MB
  3. Creat Time：2024-05-19
  4. Active Degree：110
  5. Last Active：2024-11-07
  6. File Tags：Lynda  DevOps  for  Data  Scientists  
  7. Statement：This site does not provide download links, only text displays, and does not contain any infringement.
File List:

    Lynda - DevOps for Data Scientists

  1. 4.3. Deployment Practices/12.Securing the data science models in production.mp4 6.02 MB
  2. 2.1. Data Science Development Practices/05.Experimenting with data, features, and algorithms.mp4 4.43 MB
  3. 4.3. Deployment Practices/13.Monitoring models in production.mp4 4.37 MB
  4. 3.2. Data Science Models to Production/07.Version control for data science models.mp4 4.18 MB
  5. 1.Introduction/01.Welcome.mp4 4.08 MB
  6. 2.1. Data Science Development Practices/04.Collecting and munging data.mp4 4.02 MB
  7. 3.2. Data Science Models to Production/08.Predictive Model Markup Language.mp4 3.91 MB
  8. 5.4. Data Science Models in Containers/15.Creating a Dockerfile for data science models.mp4 3.51 MB
  9. 5.4. Data Science Models in Containers/16.Data science Docker image repository.mp4 3.38 MB
  10. 2.1. Data Science Development Practices/03.Data science and software engineering.mp4 2.77 MB
  11. 2.1. Data Science Development Practices/06.Testing and validating models.mp4 2.52 MB
  12. 6.Conclusion/17.Overview of DevOps best practices for data science.mp4 2.50 MB
  13. 5.4. Data Science Models in Containers/14.Introduction to Docker.mp4 2.43 MB
  14. 3.2. Data Science Models to Production/09.Deploying models with automation tools.mp4 2.17 MB
  15. 4.3. Deployment Practices/11.Canary deployments.mp4 1.80 MB
  16. 4.3. Deployment Practices/10.Deploying to staging environment.mp4 1.77 MB
  17. 1.Introduction/02.Target audience.mp4 831 KB
  18. 4.3. Deployment Practices/12.Securing the data science models in production.en.srt 7 KB
  19. 3.2. Data Science Models to Production/07.Version control for data science models.en.srt 4 KB
  20. 2.1. Data Science Development Practices/04.Collecting and munging data.en.srt 4 KB
  21. 4.3. Deployment Practices/13.Monitoring models in production.en.srt 4 KB
  22. 5.4. Data Science Models in Containers/15.Creating a Dockerfile for data science models.en.srt 4 KB
  23. 3.2. Data Science Models to Production/08.Predictive Model Markup Language.en.srt 3 KB
  24. 6.Conclusion/17.Overview of DevOps best practices for data science.en.srt 2 KB
  25. 2.1. Data Science Development Practices/06.Testing and validating models.en.srt 2 KB
  26. 5.4. Data Science Models in Containers/14.Introduction to Docker.en.srt 2 KB
  27. 2.1. Data Science Development Practices/05.Experimenting with data, features, and algorithms.en.srt 2 KB
  28. 5.4. Data Science Models in Containers/16.Data science Docker image repository.en.srt 2 KB
  29. 3.2. Data Science Models to Production/09.Deploying models with automation tools.en.srt 2 KB
  30. 2.1. Data Science Development Practices/03.Data science and software engineering.en.srt 2 KB
  31. 4.3. Deployment Practices/11.Canary deployments.en.srt 2 KB
  32. 1.Introduction/01.Welcome.en.srt 2 KB
  33. 4.3. Deployment Practices/10.Deploying to staging environment.en.srt 2 KB
  34. 1.Introduction/02.Target audience.en.srt 1 KB
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