Welcome to this project on Deploy Image Classification Pre-trained Keras model using Flask. In this project, we will have a comprehensive understanding of how to deploy a deep learning model as a web application using the Flask framework.
Developing a machine learning or deep learning model is very important to solve problems using AI. On the other hand, it is equally important to have a knowledge of how to deploy those amazing problem-solving models into such an interface which enables the users to make use of these solutions. Even many apps we use today, like YouTube, Amazon/Flipkart shopping, FaceApps …
Welcome to the project on Hosting an Image Classification App on Heroku. In this project, we will get a basic understanding of how to deploy a web app on Heroku, a Platform as a Service.
Heroku is a cloud platform for the deployment and management purposes of web applications. It could be considered as one of the best solutions for hosting web-apps very quickly, thus allowing the developer to concentrate more on development.
Welcome to this project on Deploying Multi-Container Docker App on AWS. In this app, we will learn how to build a Deploy Multi-Container Application using Flask, Redis, and PostgreSQL.
We will use NGINX-uWSGI along with Flask as the web service, and connect it with the PostgreSQL and Redis container services. Then, we will understand how to automate the process of deploying the web app to Docker Hub, using GitHub and Travis CI. Finally, we will understand how to automate deployments on AWS Elastic Beanstalk using GitHub and Travis.
Github link: https://github.com/cloudxlab/user-wishlist-app
Welcome to the project on How to build low-latency deep-learning-based flask app. In this project, we will refactor the entire codebase of the project [ How to Deploy an Image Classification Model using Flask][1]. That monolithic code will be refactored to form two microservices - the flask service and model service. The model service acts as a server that renders pretrained Tensorflow model as a deep learning API, and keeps listening for any incoming requests. The flask service requests the model service, and displays the response from the model server. This way, we write cleaner code and promote service isolation.
Further …
Welcome to the project on the Deploy Flask app with AWS RDS and ElastiCache Redis. In this project, we will learn how to use Amazon RDS and Amazon ElastiCache, how to connect them to AWS Elastic Beanstalk and deploy a project based on these three technologies.
It is highly recommended to go through the playlist Deploy Multi-Container Docker App on AWS, before going through this project, for a better understanding of this project series.
Github link: https://github.com/cloudxlab/user-wishlist-app/tree/awscache-rds-eb
In this project, we will understand how to deploy a multi-container application on Minikube and GKE.
We will learn about Kubernetes Deployments, Kubernetes Services, Kubernetes Ingress, Kubernetes Secrets and Kubernetes Persistent Volume Claim. By the end of this project, we will have a fair understanding of the basic workflow of Kubernetes project deployment. We will also be able to appreciate the use of MiniKube before deploying an application onto production, like onto Google Kubernetes Engine. Further, we will also see how to monitor the Kubernetes cluster and scale pods.
It is very highly recommended to complete the previous projects for …
Welcome to this project on Deploy Image Classification Pre-trained Keras model using Flask. In this project, we will have a comprehensive understanding of how to deploy a deep learning model as a web application using the Flask framework.
Developing a machine learning or deep learning model is very important to solve problems using AI. On the other hand, it is equally important to have a knowledge of how to deploy those amazing problem-solving models into such an interface that enables the users to make use of these solutions. Even many apps we use today, like YouTube, Amazon/Flipkart shopping, FaceApps …