+
Skip to content

Add dstack confidential AI framework to Model Deployment #132

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
+1 −0
Open

Add dstack confidential AI framework to Model Deployment #132

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -337,6 +337,7 @@
1. ML Model Rollout To Production. [Part 1](https://www.superwise.ai/resources-old/safely-rolling-out-ml-models-to-production) | [Part 2](https://www.superwise.ai/blog/part-ii-safely-rolling-out-models-to-production)
1. [Deploying Python ML Models with Flask, Docker and Kubernetes](https://alexioannides.com/2019/01/10/deploying-python-ml-models-with-flask-docker-and-kubernetes/)
1. [Deploying Python ML Models with Bodywork](https://alexioannides.com/2020/12/01/deploying-ml-models-with-bodywork/)
1. [dstack: Open-source confidential AI framework for ML deployment with data privacy](https://github.com/Dstack-TEE/dstack)
1. [Framework for a successful Continuous Training Strategy. When should the model be retrained? What data should be used? What should be retrained? A data-driven approach](https://towardsdatascience.com/framework-for-a-successful-continuous-training-strategy-8c83d17bb9dc)
1. [Efficient Machine Learning Inference. The benefits of multi-model serving where latency matters](https://www.oreilly.com/content/efficient-machine-learning-inference/)
1. [Deploying Hugging Face ML Models in the Cloud with Infrastructure as Code](https://www.pulumi.com/blog/mlops-the-ai-challenge-is-cloud-not-code/)
Expand Down
点击 这是indexloc提供的php浏览器服务，不要输入任何密码和下载