Welcome to this project on Deploying App with Docker, Travis CI & AWS Elastic Beanstalk. In this project, we will understand about Docker, Travis, and some services of AWS.
We will first make a simple static website, then dockerize the app. Then we will push it to GitHub and enable Travis to track changes in that repository. Further, we will understand the app deployment on the AWS Elastic Beanstalk using S3 and IAM. We will also host the app on a public domain bought from Google Domains, and configure it with the help of Amazon Route 53.
Github link: [https://github …
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 this project on Image Stitching using OpenCV. In this project, we will use OpenCV with Python and Matplotlib in order to merge two images and form a panorama.
As you know, the Google photos app has stunning automatic features like video making, panorama stitching, collage making, and many more. In this exercise, we will understand how to make a panorama stitching using OpenCV with Python.
Skills you will develop:
In this project, we will learn how to build a real-time analytics dashboard using Apache Spark Streaming, Kafka, Node.js, Socket.IO, and Highcharts.
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 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
Welcome to this project on Testing App Locally on MiniKube. In this project, we will understand what is Kubernetes and what is Minikube.
As part of the hands-on, we will learn to set up Minikube with VirtualBox in Windows 10 Home system. We will learn various concepts of Kubernetes like pods, deployments, services, and ingress, and have a look at how we could create them in various ways using different commands. We will also deploy the single container static web application - which we have dockerized as part of the Docker, Travis, and AWS project series - and access it using Kubernetes …
In any machine learning project, 90% of work is about data extraction, cleaning, preprocessing. This is a very challenging part of the machine learning projects. This skill is must have for any machine learning engineer.
Solve these problems to become very efficient at solving data preprocessing, cleaning, transforming, or extracting using Pandas, Python, and Numpy.
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 …
Vulture is a Python library that is used to remove dead code from a Python program. So let us see how to use Vulture to remove dead code from a sample program in Python.
Welcome to the project on Yolov4 with OpenCV for Object Detection. In this project, we will learn how to use a YOLOv4 network pretrained on the MSCOCO dataset for object detection.
Object detection has applications in various fields, from home automation to self-driving computers. YOLOv4 is one of the recent state-of-art object detection models. This project provides an overview of how to use a YOLOv4 pretrained model.