NLP: Natural Language Processing ML Model Deployment at AWS
4.7 (32 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,125 students enrolled

NLP: Natural Language Processing ML Model Deployment at AWS

Build & Deploy BERT, DistilBERT, FastText NLP Models in Production with Flask, uWSGI, and NGINX at AWS EC2.
Hot & New
4.7 (32 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,125 students enrolled
Last updated 7/2020
English
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 7.5 hours on-demand video
  • 1 article
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • Complete End to End NLP Application
  • How to work with BERT in Google Colab
  • How to use BERT for Text Classification
  • Deploy Production Ready ML Model
  • Fine Tune and Deploy ML Model with Flask
  • Deploy ML Model in Production at AWS
  • Deploy ML Model at Ubuntu and Windows Server
  • DistilBERT vs BERT
  • Optimize your NLP Code
  • You will learn how to develop and deploy FastText model on AWS
  • Learn Multi-Label and Multi-Class classification in NLP
Course content
Expand all 66 lectures 07:33:14
+ BERT | Sentiment Prediction | Multi Class Prediction Problem
12 lectures 01:02:36
Download Working Files
00:35
What is ktrain
05:00
Going Deep Inside ktrain Package
04:35
Notebook Setup
02:19
Installing ktrain
04:22
Loading Dataset
04:44
Train-Test Split and Preprocess with BERT
08:17
BERT Model Training
10:23
Testing Fine Tuned BERT Model
04:42
+ DistilBERT | Faster and Cheaper BERT model from Hugging Face
12 lectures 01:16:39
What is DistilBERT?
09:11
Notebook Setup
04:52
Data Preparation
08:14
DistilBERT Model Training
07:49
Model Evaluation
03:21
Download Fine Tuned DistilBERT Model
01:21
Flask App Preparation
01:40
Run Your First Flask Application
07:25
Predict Sentiment at Your Local Machine
05:11
Build Predict API
09:36
+ Deploy Your DistilBERT ML Model at AWS EC2 Windows Machine with Flask
9 lectures 01:08:39
Create AWS Account
06:50
Create Free Windows EC2 Instance
05:45
Connect EC2 Instance from Windows 10
07:23
Install Python on EC2 Windows 10
03:02
Install TensorFlow 2 and KTRAIN
10:36
Run Your First Flask Application on AWS EC2
07:44
Transfer DistilBERT Model to EC2 Flask Server
03:57
Deploy ML Model on EC2 Server
11:44
+ Deploy Your DistilBERT ML Model at AWS Ubuntu (Linux) Machine with Flask
9 lectures 01:01:56
Install Git Bash and Commander Terminal on Local Computer
07:13
Create AWS Account
06:50
Launch Ubuntu Machine on EC2
04:31
Install PIP3 on AWS Ubuntu
05:04
Update and Upgrade Your Ubuntu Packages
02:28
Install TensorFlow 2 and KTRAIN
11:09
Create Extra RAM from SSD by Memory Swapping
10:19
Deploy DistilBERT ML Model on EC2 Ubuntu Machine
08:32
+ Deploy Robust and Secure Production Server with NGINX, uWSGI, and Flask
9 lectures 01:07:40
NGINX Introduction
04:45
Virtual Environment Setup
06:10
Setting Up Flask Server
05:54
Setting Up uWSGI Server
08:12
Installing TensorFlow 2 and KTRAIN
05:58
Start API Services at System Startup
06:49
Configuring NGINX with uWSGI, and Flask Server
10:05
Congrats! You Have Deployed ML Model in Production
15:28
+ Multi-Label Classification | Deploy Facebook's FastText NLP Model in Production
15 lectures 01:55:43
What is Multi-Label Classification?
07:46
FastText Research Paper Review
14:22
Notebook Setup
06:39
Data Preparation
12:02
FastText Model Training
06:14
FastText Model Evaluation and Saving at Google Drive
04:26
Creating Fresh Ubuntu Machine
08:14
Setting Python3 and PIP3 Alias
05:54
Creating 4GB Extra RAM by Memory Swapping
03:33
Making Your Server Ready
06:13
Preparing Prediction APIs
12:33
Testing Prediction API at Local Machine
06:08
Testing Prediction API at AWS Ubuntu Machine
08:13
Configuring uWSGI Server
06:15
Deploy FastText Model in Production with NGINX, uWSGI, and Flask
07:11
Requirements
  • Introductory knowledge of NLP
  • Comfortable in Python, Keras, and TensorFlow 2
  • Basic Elementary Mathematics
Description

Are you ready to kickstart your  Advanced NLP course? Are you ready to deploy your machine learning models in production at AWS? You will learn each and every steps on how to build and deploy your ML model on a robust and secure server at AWS.

Prior knowledge of python and Data Science is assumed. If you are AN absolute beginner in Data Science, please do not take this course. This course is made for medium or advanced level of Data Scientist.


What is BERT?

BERT is a method of pre-training language representations, meaning that we train a general-purpose "language understanding" model on a large text corpus (like Wikipedia), and then use that model for downstream NLP tasks that we care about (like question answering). BERT outperforms previous methods because it is the first unsupervised, deeply bidirectional system for pre-training NLP.

Unsupervised means that BERT was trained using only a plain text corpus, which is important because an enormous amount of plain text data is publicly available on the web in many languages.


Why is BERT so revolutionary?

Not only is it a framework that has been pre-trained with the biggest data set ever used, but it is also remarkably easy to adapt to different NLP applications, by adding additional output layers. This allows users to create sophisticated and precise models to carry out a wide variety of NLP tasks.


Here is what you will learn in this course

  • Notebook Setup and What is BERT.

  • Data Preprocessing.

  • BERT Model Building and Training.

  • BERT Model Evaluation and Saving.

  • DistilBERT Model Fine Tuning and Deployment

  • Deploy Your ML Model at AWS with Flask Server

  • Deploy Your Model at Both Windows and Ubuntu Machine

  • And so much more!


All these things will be done on Google Colab which means it doesn't matter what processor and computer you have. It is super easy to use and plus point is that you have Free GPU to use in your notebook.

Who this course is for:
  • AI Students eager to learn advanced techniques of text processing
  • Data Science enthusiastic to build end-to-end NLP Application
  • Anyone wants to strengthen NLP skills
  • Anyone want to deploy ML Model in Production
  • Data Scientists who want to learn Production Ready ML Model Deployment