(NLP) Machine Learning Model building and Deployment

A to Z (NLP) Machine Learning Model building and Deployment

Python, Docker, Flask, GitLab, Jenkins tools and technology used for deploy model in your Local server. A complete Guide

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Last Updated: 2022-07-25 09:07:47 English Course Type: Self Learning

Developing the NLP Model for Sentiment analysis and Machine learning deployment on local server using flask and docker.
Select the most efficient Machine Learning Model,Tune the hyper-parameters and selecting the best model using cross-validation technique
A quick discussion from the basic in nutshell about DevOps tools like docker, Git and GitLab, Jenkins etc.
A better understanding about software development and automation in real scenario and concept of end-to-end Integration.

Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it.

Most of the problems nowadays as I have made a machine-learning model but what next.

How it is available to the end-user, the answer is through API, but how it works?

How you can understand where the Docker stands and how to monitor the build we created.

This course has been designed to keep these areas under consideration. The combination of industry-standard build pipeline with some of the most common and important tools.

This course has been designed into Following sections:

1) Configure and a quick walkthrough of each of the tools and technologies we used in this course.

2) Building our NLP Machine Learning model and tune the hyperparameters.

3) Creating flask API and running the WebAPI in our Browser.

4) Creating the Docker file, build our image and running our ML Model in Docker container.

5) Configure GitLab and push your code in GitLab.

6) Configure Jenkins and write Jenkins's file and run end-to-end Integration.

This course is perfect for you to have a taste of industry-standard Data Science and deploying in the local server. Hope you enjoy the course as I enjoyed making it.

Basic programming in any language
Some exposure to Python (but not mandatory)

Beginner Machine Learning Enthusiast want to deploy their model.
Beginner python developer curious about data science.
Any one wants to learn Devops and role of DevOps in Data Science.

Section 1 : Configuration and Installation 5 Lectures 00:31:38
Lecture 1 :
Environment - Virtual Box Configuration and Installation Preview
Lecture 2 :
Environment - Docker Installation
Lecture 3 :
Environment Setup - Installation of jenkins.
Lecture 4 :
Environment Setup - GitLab Installation
Lecture 5 :
Introduction to Flask
Section 2 : Natural Language processing Programming 6 Lectures 01:01:30
Lecture 1 :
Sentiment Analysis Python
Lecture 2 :
Sentiment Analysis Python
Lecture 3 :
Regex to remove username
Lecture 4 :
Punctutaion and body length Features
Lecture 5 :
Vectorizers and Model Selection
Lecture 6 :
HyperParameter tuning and model selection
Section 3 : Programming Python Flask NLP Model 4 Lectures 00:33:02
Lecture 1 :
Programming Python Flask Web API
Lecture 2 :
Understanding Templates and WebPages
Lecture 3 :
Importing webpages and main function
Lecture 4 :
Running our flask API
Section 4 : Introduction to docker commands and Dockerfile 3 Lectures 00:34:40
Lecture 1 :
Understating the docker in Nutshell
Lecture 2 :
Writing Dockerfile
Lecture 3 :
GitHub Clone and docker build
Section 5 : CI -CD Pipeline and jenkins configuration 3 Lectures 00:36:28
Lecture 1 :
Push code to gitlab and make Jenkinsfreestyle project
Lecture 2 :
Making Jenkinsfile and creating jenkins pipeline
Lecture 3 :
Configuring CI-CD Pipeline with GitLab webhook and jenkins

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