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Indhold leveret af Bret Fisher. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Bret Fisher eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
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MLOps for DevOps People

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Manage episode 438453184 series 2483573
Indhold leveret af Bret Fisher. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Bret Fisher eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.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.

There's also a video version to watch on YouTube.

★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

180 episoder

Artwork
iconDel
 
Manage episode 438453184 series 2483573
Indhold leveret af Bret Fisher. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Bret Fisher eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.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.

There's also a video version to watch on YouTube.

★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

180 episoder

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