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内容由Bret Fisher提供。所有播客内容(包括剧集、图形和播客描述)均由 Bret Fisher 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal
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MLOps for DevOps People

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Manage episode 438453184 series 2483573
内容由Bret Fisher提供。所有播客内容(包括剧集、图形和播客描述)均由 Bret Fisher 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Bret and Nirmal are joined by Maria Vechtomova, a MLOps Tech Lead and co-founder of Marvelous MLOps, to discuss the obvious and not-so obvious differences between a MLOps Engineer and traditional DevOps jobs.
Maria is here to discuss how DevOps engineers can adopt and operate machine learning workloads, also known as MLOps. With her expertise, we'll explore the challenges and best practices for implementing ML in a DevOps environment, including some hot takes on using Kubernetes.

Be sure to check out the live recording of the complete show from June 20, 2024 on YouTube (Stream 271).

★Topics★
Marvelous MLOps on LinkedIn
Marvelous MLOps Substack
Marvelous MLOps YouTube Channel

Creators & Guests

  • (00:00) - Intro
  • (02:04) - Maria's Content
  • (03:22) - Tools and Technologies in MLOps
  • (09:21) - DevOps vs MLOps: Key Differences
  • (19:22) - Transitioning from DevOps to MLOps
  • (22:52) - Model Accuracy vs Computational Efficiency
  • (24:46) - MLOps with Sensitive Data
  • (29:10) - MLOps Roadmap and Getting Started
  • (32:36) - Tools and Platforms for MLOps
  • (37:14) - Adapting MLOps Practices to Future Trends
  • (44:08) - Is Golang an Option for CI/CD Automation?

You can also support my free material by subscribing to my YouTube channel and my weekly newsletter at bret.news!

Grab the best coupons for my Docker and Kubernetes courses.
Join my cloud native DevOps community on Discord.
Grab some merch at Bret's Loot Box
Homepage bretfisher.com

  continue reading

172集单集

Artwork
icon分享
 
Manage episode 438453184 series 2483573
内容由Bret Fisher提供。所有播客内容(包括剧集、图形和播客描述)均由 Bret Fisher 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Bret and Nirmal are joined by Maria Vechtomova, a MLOps Tech Lead and co-founder of Marvelous MLOps, to discuss the obvious and not-so obvious differences between a MLOps Engineer and traditional DevOps jobs.
Maria is here to discuss how DevOps engineers can adopt and operate machine learning workloads, also known as MLOps. With her expertise, we'll explore the challenges and best practices for implementing ML in a DevOps environment, including some hot takes on using Kubernetes.

Be sure to check out the live recording of the complete show from June 20, 2024 on YouTube (Stream 271).

★Topics★
Marvelous MLOps on LinkedIn
Marvelous MLOps Substack
Marvelous MLOps YouTube Channel

Creators & Guests

  • (00:00) - Intro
  • (02:04) - Maria's Content
  • (03:22) - Tools and Technologies in MLOps
  • (09:21) - DevOps vs MLOps: Key Differences
  • (19:22) - Transitioning from DevOps to MLOps
  • (22:52) - Model Accuracy vs Computational Efficiency
  • (24:46) - MLOps with Sensitive Data
  • (29:10) - MLOps Roadmap and Getting Started
  • (32:36) - Tools and Platforms for MLOps
  • (37:14) - Adapting MLOps Practices to Future Trends
  • (44:08) - Is Golang an Option for CI/CD Automation?

You can also support my free material by subscribing to my YouTube channel and my weekly newsletter at bret.news!

Grab the best coupons for my Docker and Kubernetes courses.
Join my cloud native DevOps community on Discord.
Grab some merch at Bret's Loot Box
Homepage bretfisher.com

  continue reading

172集单集

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