XGBoost deployment made easy
Rather obscure way to serve XGBoost models, but still works.
Link: https://towardsdatascience.com/xgboost-deployment-made-easy-6e11f4b3f817
#production #xgboost #deployment
Rather obscure way to serve XGBoost models, but still works.
Link: https://towardsdatascience.com/xgboost-deployment-made-easy-6e11f4b3f817
#production #xgboost #deployment
Medium
Convert your XGBoost model into if-else format
In this article, I’ll show the reader how to convert an XGBoost model to a .py file with some tricks with regular expressions, so that the…
Hi, our friends @mike0sv and @agusch1n just open-sourced MLEM - a tool that helps you deploy your ML models as part of the DVC ecosystem
It’s a Python library + Command line tool.
TLDR:
📦 MLEM can package an ML model into a Docker image or a Python package, and deploy it to Heroku (we made them promise to add SageMaker, K8s and Seldon-core soon :parrot:).
⚙️ MLEM saves all model metadata to a human-readable text file: Python environment, model methods, model input & output data schema and more.
💅 MLEM helps you turn your Git repository into a Model Registry with features like ML model lifecycle management.
Read more in release blogpost: https://dvc.org/blog/MLEM-release
Also, check out the project: https://github.com/iterative/mlem
And the website: https://mlem.ai
Guys are happy to hear your feedback, discuss how this could be helpful for you, how MLEM compares to MLflow, etc.
Ask in the comments!
#mlops #opensource #deployment #dvc
It’s a Python library + Command line tool.
TLDR:
📦 MLEM can package an ML model into a Docker image or a Python package, and deploy it to Heroku (we made them promise to add SageMaker, K8s and Seldon-core soon :parrot:).
⚙️ MLEM saves all model metadata to a human-readable text file: Python environment, model methods, model input & output data schema and more.
💅 MLEM helps you turn your Git repository into a Model Registry with features like ML model lifecycle management.
Read more in release blogpost: https://dvc.org/blog/MLEM-release
Also, check out the project: https://github.com/iterative/mlem
And the website: https://mlem.ai
Guys are happy to hear your feedback, discuss how this could be helpful for you, how MLEM compares to MLflow, etc.
Ask in the comments!
#mlops #opensource #deployment #dvc