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

4.40 (157 reviews)
Udemy
platform
English
language
Data Science
category
instructor
13,975
students
5 hours
content
Aug 2023
last update
$54.99
regular price

What you will learn

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.

Description

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.

Content

Installation and Configuration

Environment - Virtual Box Configuration and Installation
Environment - Docker Installation
Environment Setup - Installation of jenkins.
Environment Setup - GitLab Installation
Introduction to Flask

Part 1 Natural Language processing Programming

Sentiment Analysis introduction and data set.
Programming Python Flask Web API
Sentiment Analysis Cleaning of data.
Regex to remove username
Punctutaion and body length Features
Vectorizers and Model Selection
HyperParameter tuning and model selection
Some basic NLP Quiz for your Exercise.

Part 2 Programming Python Flask NLP Model

Understanding Templates and WebPages
Importing webpages and main function
Running our flask API
The quiz about API and flask application.

Part 3 Introduction to docker commands and Dockerfile

Understating the docker in Nutshell
Writing Dockerfile
GitHub Clone and docker build
Docker Quiz.

CI -CD Pipeline and jenkins configuration

Push code to GitLab and make Jenkins freestyle project
Making Jenkinsfile and creating Jenkins pipeline
Configuring CI-CD Pipeline with GitLab webhook and Jenkins
Quiz on CI-CD Pipeline

Course Completion

Congratulation for your completion

Screenshots

A to Z (NLP) Machine Learning Model building and Deployment. - Screenshot_01A to Z (NLP) Machine Learning Model building and Deployment. - Screenshot_02A to Z (NLP) Machine Learning Model building and Deployment. - Screenshot_03A to Z (NLP) Machine Learning Model building and Deployment. - Screenshot_04

Reviews

Alan
August 26, 2021
Multiple benefits thus far include creating a Linux environment on top of your machine’s operating system.
Anthony
November 13, 2020
The intent of the content was intriguing however; it was hard to follow because it seemed to lack structure. The audio was sporadic. If you have basic experience with these tools, you will get through it and find value. Overall the quality of the training delivered and the structure need some work. In the end if you want to understand how to take your ML model to deployment. That mission could be accomplished. Patience was required to sit through every section.
Nellie
August 10, 2020
This is an ambitious course: to teach you various deployment pieces in 4 hours, including an hour of building a data science model. As a result, your underlying knowledge of machine learning, virtualisation, docker, Python etc. should already be at least upper-intermediate. Forwarned is forarmed. (the instructor should really change the "pre-requisite" section. It is not just "you have to know a bit of Python"). The delivery is haphazard in places, you have to already have intuition to know what is going on (again, to have the prior knowledge/experience). Giving 4.5 stars still, because I needed to know the deployment with containers/Jenkins and I did get it.
Aishwarya
May 18, 2020
Better structuring of the course is required. Also the teacher is not able to articulate when he tries to explain. But I got to learn about Jenkins and how to integrate it with our flask api so it was ok. Wouldn't have paid for this course
Erjan
February 5, 2020
nice, but typos make the overall impression weak.. sorry , the content is pretty good and the course is short - only 3.5h. fix typos!
Sandeep
January 13, 2020
Firstly, I thank the instructor Mohammed Rizwan for providing this good beginner course for free. The instructor had nicely explained that is easy to understand. A very good course to begin the ML models (NLP) deployment. Just one suggestion- It will be very much helpful if this course contains 2 or 3 more models deployed with different scenarios.
PALAKOLANU
December 29, 2019
Great experience. End to End development and deployment concepts are explained. Thank you very much. Expecting another course on Django deployment.
Ameya
December 15, 2019
i have completed this course few days back , and it helped me a lot in my undergoing projects , the way real time problems are simplified is totally awesome , i suggest everyone to must undergo this course . Thanks instructor for great knowledge.

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2598110
udemy ID
10/9/2019
course created date
12/14/2019
course indexed date
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