5 - Type of ML Model Deployment 08:22 6 - ML Infrastructure and Integration Tools 07:26 7 - Benefits of ML Pipeline 02:39 8 - Challenges in ML Model Deployment 06:44 9 - Data and Model Management 05:25 10 - AB Testing for ML Model 04:35 11 - ML Model Bias and Securit...
Welcome to Production-Grade ML Model Deployment with FastAPI, AWS, Docker, and NGINX! Unlock the power of seamless ML model deployment with our comprehensive course,Production-Grade ML Model Deployment with FastAPI, AWS, Docker, and NGINX.This course is designed for data scientists, machine learnin...
Docker is a platform designed to help developers build, share, and run container applications. We handle the tedious setup, so you can focus on the code.
We’ve discussed why ML models need to be deployed to production and how to do so using Docker and Flask. Without deployment, trained models cannot be used for inference for real-time data. To deploy any service to production, two key factors are important, i.e., scalability and portabilit...
Faster and more secure AI/ML development Faster time to code For more than a decade, developers have relied on Docker to accelerate the setup and deployment of their development environments. Modern AI/ML applications are complex, and Docker saves developers time to accelerate innovation. ...
model/由PyTorch模型参数和任何预处理模块joblib组成 notebook/包含这个项目的示例PyTorch模型 你可以在这个Github repo中找到这里提到的所有文件: https://github.com/ming0070913/example-ml-project 三、准备推理 在部署机器学习模型之前,我们需要保存训练模型以及任何预处理模块(与训练数据集相匹配,例如scikit-learn的On...
将容器化的 ML 模型评分服务部署到 Kubernetes 要在Kubernetes 上启动我们的测试模型评分服务,我们将首先在 Kubernetes Pod 中部署容器化服务,它的推出由部署管理,而部署又会创建一个 ReplicaSet,这是通过下面的代码实现的: kubectl create deployment test-ml-score-api --image=alexioannides/test-ml-score-api...
将机器学习(ML)模型部署到生产环境中的一个常见模式是将这些模型作为RESTful API微服务公开,这些微服务从Docker容器中托管,例如使用 SciKit Learn 或 Keras 包训练的 ML 模型,这些模型可以提供对新数据的预测。然后,可以将它们部署到云环境中,以处理维护连续可用性所需的所有事情,例如容错、自动缩放、负载平衡和滚动服...
KFServing是基于Knative的自定义资源,用于部署和管理ML模型。 KFServing提供了以下功能: Deploy your model using out-of-the-box model servers (no need to write your own flask app) Auto-scaling based on load, even for models served on GPUs
将容器化的 ML 模型评分服务部署到 Kubernetes 要在Kubernetes 上启动我们的测试模型评分服务,我们将首先在 Kubernetes Pod 中部署容器化服务,它的推出由部署管理,而部署又会创建一个 ReplicaSet,这是通过下面的代码实现的: kubectl create deployment test-ml-score-api --image=alexioannides/test-ml-score-api:...