Deploying Machine Learning Model inside a Docker container.
Absar Qureshi
Bachelor of Engineering(Computer Science) || Master of Business Administration(Financial Management)
*Docker is an open platform for developing, shipping, and running applications. Docker enables you to separate your applications from your infrastructure so you can deliver software quickly. With Docker, you can manage your infrastructure in the same ways you manage your applications. By taking advantage of Docker’s methodologies for shipping, testing, and deploying code quickly, you can significantly reduce the delay between writing code and running it in production.
*The whole concept of machine learning is figuring out ways in which we can teach a computer to perform a task without needing to provide explicit instructions. Another way to think about it is that we're trying to "program" intuition in a computer. You and I can look at an email and easily discern whether or not it's spam, but how do you get a computer to do such a task? You could construct a huge convoluted logic infrastructure of "if.. then.." statements to sort out the spam emails, but it would be a pain to construct and probably wouldn't work too well. Instead, the machine learning approach is to equip the computer with skills to learn on its own and feed it a bunch of examples. Machine learning is exploding as a field right now as people are realizing a multitude of tasks that we can teach computers to perform by feeding it large datasets.
TASK DESCRIPTION
- Pull the Docker image of CentOS image from DockerHub and create a new container.
- Install the Python software on the top of docker container.
- Inside the docker container you need to copy/create machine learning model and train it.
STEP 1- START DOCKER SERVICES
systemctl start docker
STEP 2- PULL CentOs IMAGE FROM DOCKERHUB AND ENTER DOCKER CONTAINER
docker pull centos:latest docker run -it --name os1 centos:latest docker start os1 docker attach os1
STEP3- INSTALL PYTHON3 SOFTWARE INSIDE DOCKER CONTAINER
yum install python3
STEP 4- INSTALL SKLEARN AND JOBLIB PACKAGES
pip3 install joblib pip3 install sklearn
STEP 5- INSTALL GIT PACKAGE.
yum install git
STEP6- CLONE GITHUB REPO.
git clone https://absarqureshi/salary_detection_using_machine_learning.git
STEP 7- RUN THE PYTHON FILE FOR SALARY PREDICTION
Thanks for reading!