FastAPI和Docker部署大模型 - 2025 Deploy ML Model in Production with FastAPI and Docker共计100条视频,包括:1 - Course Introduction、2 - Install Requirementstxt、4 - What is Machine Learning Pipeline等,UP主更多精彩视频,请关注UP账号。
This post shows you how to easily deploy and run serverless ML inference by exposing your ML model as an endpoint using FastAPI, Docker, Lambda, andAmazon API Gateway. We also show you how to automate the deployment using theAWS Cloud Development Kit(AWS...
Streamline ML Operations with FastAPI:Master the art of serving machine learning models using FastAPI, one of the fastest-growing web frameworks. Learn to build robust RESTful APIs that facilitate quick and efficient model inference, ensuring your ML solutions are both accessible and scalable. ...
shanesoh/deploy-ml-fastapi-redis-docker Serve a production-ready and scalable Keras-based deep learning model image classification using FastAPI, Redis and… github.com Building the web server I chose to use thetiangolo/uvicorn-gunicorn-fastapifor the web server. This Docker image provides a neat ...
Welcome to MLOps! In this lab I have demonstrated how to deploy a webserver that hosts a predictive model trained on the wine dataset using FastApiand Docker. - MLOPS-Deploy-a-ML-model-with-fastAPI-and-Docker/FastAPI_Docker/no-batch/Untitled.ipynb at mai
git clone https://github.com/owainow/ml-on-aca.git To start the demo we require a requirements .txt file outlining the packages required for this walk through. The packages are: - FastAPI - Numpy - Uvicorn - Image - TensorFlow The requirements.txt file can be found in the...
Welcome to MLOps! In this lab I have demonstrated how to deploy a webserver that hosts a predictive model trained on the wine dataset using FastApiand Docker. - MLOPS-Deploy-a-ML-model-with-fastAPI-and-Docker/FastAPI_Docker/no-batch/requirements.txt at m
Hi, I am hoping to run my python code that uses large AI models over large videos. Please can someone best advise me on the best Azure technology to use. Currently I hosted my FastAPI python application on a web app (CPU), and now I am facing chall...
Use az ml model create --file model.yaml to register the model to your workspace. Define the endpoint To define an endpoint, you need to specify: Endpoint name: The name of the endpoint. It must be unique in the Azure region. For more information on the naming rules, see endpoint limit...
Expose a port for your application to communicate with other services. You can use Flask or FastAPI to create a RESTful API for your model inference. Build your container image using docker build or Podman build commands. For example: