Marvelous MLOps

[Marvelous MLOps @MarvelousMLops]

When your reusable #githubactions #cicd workflows are used in 30 projects, you need to be careful about each version of reusable workflows you create. Read about how we test it here: https://t.co/xgWUxFCZDK

[Marvelous MLOps @MarvelousMLops]

By the way, we are talking about serverless API deployment on @databricks at @Data_AI_Summit in June in San Francisco!

[Marvelous MLOps @MarvelousMLops]

@databricks #serverless model endpoint has finally arrived and we already started playing with it. Read our blog post if you want to lear more: https://t.co/vW15cBCPaQ

[GitHub @github]

Wait, what?!

[Marvelous MLOps @MarvelousMLops]

You don't need to build a whole train network. Start with a minimum setup by laying down enough track to get your train moving. In this article, we define the key components in #MLOps setup, that are required for your locomotive to move: https://t.co/cEWGtrIgRF

[Marvelous MLOps @MarvelousMLops]

Are you ready to take your #mlops to the next level? Even if you are a pro in #mlops, or you are just starting, this article will provide you some tips and insights on how to implement traceability and reproducibility with @Databricks: https://t.co/XbUlR41Y8M

[Marvelous MLOps @MarvelousMLops]

Do you think you've got a mature #MLOps solution? How about your documentation? We bet documentation is somewhere in the bottom of your backlog. Let's embrace this necessity. It will boost your MLOps maturity. Check out our article: https://t.co/zmOZecdPgU

[Marvelous MLOps @MarvelousMLops]

Hey there! We’re Başak and Maria, friends and colleagues who share a love for Marvel and MLOps. When we decided to start a blog, we asked ChatGPT for help in finding a name, and it came up with “Marvelous MLOps” — that’s how we got our start!