Jump to content

The rise of MLOps


Recommended Posts

Developing machine learning (ML) models are a taunting task for data scientists, however, managing these models in production can be even harder. In order to have successful results, data scientists need to recognize the model drift, retrain the model with updated data sets, improve performance, and maintain the underlying technology platforms. Hence, developing production-ready models are something difficult and long to achieve.

New challenges always appear once ML models are deployed to production and used within the business processes. With more organizations adopting ML, there is a need to be aware of model management and operations. This is where MLOps – Machine Learning Operations – comes into play to make model management and operations easier and faster...

The post The rise of MLOps appeared first on DevOps Online.

View the full article

Link to comment
Share on other sites

Join the conversation

You can post now and register later. If you have an account, sign in now to post with your account.

Guest
Reply to this topic...

×   Pasted as rich text.   Paste as plain text instead

  Only 75 emoji are allowed.

×   Your link has been automatically embedded.   Display as a link instead

×   Your previous content has been restored.   Clear editor

×   You cannot paste images directly. Upload or insert images from URL.

×
×
  • Create New...