Udemy - Deployment of Machine Learning Models in Production | Python

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Deployment of Machine Learning Models in Production Python [TutsNode.com] - Deployment of Machine Learning Models in Production Python 03 DistilBERT _ Faster and Cheaper BERT model from Hugging Face
  • 030 DistilBERT-App.zip (235.2 MB)
  • 041 Deploy DistilBERT Model at Your Local Machine.en.srt (20.1 KB)
  • 037 Flask App Preparation.en.srt (2.1 KB)
  • 040 Build Predict API.en.srt (13.6 KB)
  • 032 Data Preparation.en.srt (12.7 KB)
  • 030 What is DistilBERT_.en.srt (12.5 KB)
  • 033 DistilBERT Model Training.en.srt (11.6 KB)
  • 038 Run Your First Flask Application.en.srt (11.0 KB)
  • 030 Sentiment-Classification-using-DistilBERT.zip (10.5 KB)
  • 039 Predict Sentiment at Your Local Machine.en.srt (7.2 KB)
  • 031 Notebook Setup.en.srt (7.1 KB)
  • 034 Save Model at Google Drive.en.srt (7.0 KB)
  • 036 Download Fine Tuned DistilBERT Model.en.srt (2.0 KB)
  • 035 Model Evaluation.en.srt (4.6 KB)
  • 030 What is DistilBERT_.mp4 (74.1 MB)
  • 041 Deploy DistilBERT Model at Your Local Machine.mp4 (69.5 MB)
  • 040 Build Predict API.mp4 (56.2 MB)
  • 032 Data Preparation.mp4 (54.6 MB)
  • 033 DistilBERT Model Training.mp4 (41.6 MB)
  • 038 Run Your First Flask Application.mp4 (32.4 MB)
  • 031 Notebook Setup.mp4 (24.4 MB)
  • 034 Save Model at Google Drive.mp4 (22.8 MB)
  • 039 Predict Sentiment at Your Local Machine.mp4 (21.9 MB)
  • 035 Model Evaluation.mp4 (14.9 MB)
  • 037 Flask App Preparation.mp4 (6.2 MB)
  • 036 Download Fine Tuned DistilBERT Model.mp4 (4.9 MB)
01 BERT _ Sentiment Prediction _ Multi Class Prediction Problem
  • 003 Sentiment-Classification-using-BERT.zip (326.9 KB)
  • 003 DO NOT SKIP IT _ Download Working Files.html (1.8 KB)
  • 012 BERT Model Training.en.srt (15.1 KB)
  • 008 Must Read.html (1.7 KB)
  • 011 Train-Test Split and Preprocess with BERT.en.srt (11.9 KB)
  • 014 Saving and Loading Fine Tuned Model.en.srt (10.5 KB)
  • 004 What is BERT.en.srt (8.5 KB)
  • 013 Testing Fine Tuned BERT Model.en.srt (7.0 KB)
  • 006 Going Deep Inside ktrain Package.en.srt (6.9 KB)
  • 009 Installing ktrain.en.srt (6.8 KB)
  • 005 What is ktrain.en.srt (6.8 KB)
  • 010 Loading Dataset.en.srt (6.5 KB)
  • 001 Welcome.en.srt (6.2 KB)
  • 002 Introduction.en.srt (6.0 KB)
  • 007 Notebook Setup.en.srt (3.2 KB)
  • 012 BERT Model Training.mp4 (56.8 MB)
  • 011 Train-Test Split and Preprocess with BERT.mp4 (51.4 MB)
  • 004 What is BERT.mp4 (45.3 MB)
  • 001 Welcome.mp4 (42.6 MB)
  • 002 Introduction.mp4 (35.8 MB)
  • 005 What is ktrain.mp4 (32.8 MB)
  • 006 Going Deep Inside ktrain Package.mp4 (31.3 MB)
  • 009 Installing ktrain.mp4 (29.9 MB)
  • 014 Saving and Loading Fine Tuned Model.mp4 (25.5 MB)
  • 013 Testing Fine Tuned BERT Model.mp4 (21.0 MB)
  • 010 Loading Dataset.mp4 (20.2 MB)
  • 007 Notebook Setup.mp4 (7.2 MB)
07 Multi-Label Classification _ Deploy Facebook's FastText NLP Model in Production
  • 069 NGINX-uWSGI-and-Flask-Installation-Guide-Jupyter-Notebook.zip (95.4 KB)
  • 070 FastText Research Paper Review.en.srt (20.5 KB)
  • 079 Preparing Prediction APIs.en.srt (20.0 KB)
  • 072 Data Preparation.en.srt (17.3 KB)
  • 070 FastText Research Paper Review.mp4 (160.1 MB)
  • 081 Testing Prediction API at AWS Ubuntu Machine.en.srt (13.5 KB)
  • 075 Creating Fresh Ubuntu Machine.en.srt (13.0 KB)
  • 083 Deploy FastText Model in Production with NGINX, uWSGI, and Flask.en.srt (11.6 KB)
  • 069 What is Multi-Label Classification_.en.srt (11.6 KB)
  • 071 Notebook Setup.en.srt (9.9 KB)
  • 076 Setting Python3 and PIP3 Alias.en.srt (9.8 KB)
  • 073 FastText Model Training.en.srt (9.8 KB)
  • 078 Making Your Server Ready.en.srt (9.7 KB)
  • 080 Testing Prediction API at Local Machine.en.srt (9.6 KB)
  • 082 Configuring uWSGI Server.en.srt (9.6 KB)
  • 074 FastText Model Evaluation and Saving at Google Drive.en.srt (7.1 KB)
  • 077 Creating 4GB Extra RAM by Memory Swapping.en.srt (5.6 KB)
  • 069 FastText-Multi-Label-Text-Classification.zip (4.5 KB)
  • 079 Preparing Prediction APIs.mp4 (80.8 MB)
  • 081 Testing Prediction API at AWS Ubuntu Machine.mp4 (77.5 MB)
  • 078 Making Your Server Ready.mp4 (76.5 MB)
  • 072 Data Preparation.mp4 (67.4 MB)
  • 075 Creating Fresh Ubuntu Machine.mp4 (59.3 MB)
  • 083 Deploy FastText Model in Production with NGINX, uWSGI, and Flask.mp4 (58.6 MB)
  • 082 Configuring uWSGI Server.mp4 (58.3 MB)
  • 076 Setting Python3 and PIP3 Alias.mp4 (49.3 MB)
  • 071 Notebook Setup.mp4 (45.8 MB)
  • 080 Testing Prediction API at Local Machine.mp4 (40.2 MB)
  • 073 FastText Model Training.mp4 (38.6 MB)
  • 077 Creating 4GB Extra RAM by Memory Swapping.mp4 (37.0 MB)
  • 069 What is Multi-Label Classification_.mp4 (32.7 MB)
  • 074 FastText Model Evaluation and Saving at Google Drive.mp4 (19.9 MB)
  • 069 FastText-App.zip (18.5 MB)
06 Deploy Robust and Secure Production Server with NGINX, uWSGI, and Flask
  • 060 NGINX-uWSGI-and-Flask-Installation-Guide-Jupyter-Notebook.zip (86.6 KB)
  • 068 Congrats! You Have Deployed ML Model in Production.en.srt (24.5 KB)
  • 067 Configuring NGINX with uWSGI, and Flask Server.en.srt (13.5 KB)
  • 063 Setting Up uWSGI Server.en.srt (12.5 KB)
  • 066 Start API Services at System Startup.en.srt (10.0 KB)
  • 061 Virtual Environment Setup.en.srt (9.2 KB)
  • 062 Setting Up Flask Server.en.srt (9.1 KB)
  • 064 Installing TensorFlow 2 and KTRAIN.en.srt (8.9 KB)
  • 060 NGINX Introduction.en.srt (6.7 KB)
  • 065 Configuring uWSGI Server.en.srt (6.0 KB)
  • 063 Setting Up uWSGI Server.mp4 (101.7 MB)
  • 067 Configuring NGINX with uWSGI, and Flask Server.mp4 (91.8 MB)
  • 068 Congrats! You Have Deployed ML Model in Production.mp4 (84.9 MB)
  • 066 Start API Services at System Startup.mp4 (58.1 MB)
  • 061 Virtual Environment Setup.mp4 (57.7 MB)
  • 064 Installing TensorFlow 2 and KTRAIN.mp4 (56.1 MB)
  • 062 Setting Up Flask Server.mp4 (50.7 MB)

Description


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

Requirements

Introductory knowledge of NLP
Comfortable in Python, Keras, and TensorFlow 2
Basic Elementary Mathematics

Last Updated 1/2021



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Udemy - Deployment of Machine Learning Models in Production | Python

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4.1 GB
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Udemy - Deployment of Machine Learning Models in Production | Python

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