Udemy - Deployment of Machine Learning Models in Production | Pyt...

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 4.1 GB
  • Uploaded By tutsnode
  • Downloads 437
  • Last checked 3 days ago
  • Date uploaded 5 years ago
  • Seeders 10
  • Leechers 5

Infohash : F2BF4C45530F1331A1BAA6FA7C699E08A23D9EBA




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

Files:

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)
  • 060 NGINX Introduction.mp4 (36.6 MB)
  • 065 Configuring uWSGI Server.mp4 (32.9 MB)
04 Deploy Your DistilBERT ML Model at AWS EC2 Windows Machine with Flask
  • 050 Make Your ML Model Accessible to the World.en.srt (17.7 KB)
  • 049 Deploy ML Model on EC2 Server.en.srt (17.7 KB)
  • 046 Install TensorFlow 2 and KTRAIN.en.srt (14.7 KB)
  • 047 Run Your First Flask Application on AWS EC2.en.srt (10.5 KB)
  • 042 Create AWS Account.en.srt (9.4 KB)
  • 044 Connect EC2 Instance from Windows 10.en.srt (9.3 KB)
  • 043 Create Free Windows EC2 Instance.en.srt (7.9 KB)
  • 048 Transfer DistilBERT Model to EC2 Flask Server.en.srt (6.0 KB)
  • 045 Install Python on EC2 Windows 10.en.srt (4.3 KB)
  • 049 Deploy ML Model on EC2 Server.mp4 (71.0 MB)
  • 050 Make Your ML Model Accessible to the World.mp4 (66.8 MB)
  • 046 Install TensorFlow 2 and KTRAIN.mp4 (66.6 MB)
  • 044 Connect EC2 Instance from Windows 10.mp4 (52.5 MB)
  • 043 Create Free Windows EC2 Instance.mp4 (47.7 MB)
  • 042 Create AWS Account.mp4 (36.6 MB)
  • 047 Run Your First Flask Application on AWS EC2.mp4 (29.1 MB)
  • 048 Transfer DistilBERT Model to EC2 Flask Server.mp4 (24.4 MB)
  • 045 Install Python on EC2 Windows 10.mp4 (15.8 MB)
05 Deploy Your DistilBERT ML Model at AWS Ubuntu (Linux) Machine with Flask
  • 057 Install TensorFlow 2 and KTRAIN.en.srt (16.5 KB)
  • 058 Create Extra RAM from SSD by Memory Swapping.en.srt (13.7 KB)
  • 059 Deploy DistilBERT ML Model on EC2 Ubuntu Machine.en.srt (13.7 KB)
  • 051 Install Git Bash and Commander Terminal on Local Computer.en.srt (10.7 KB)
  • 052 Create AWS Account.en.srt (9.4 KB)
  • 054 Connect AWS Ubuntu (Linux) from Windows Computer.en.srt (9.1 KB)
  • 055 Install PIP3 on AWS Ubuntu.en.srt (7.6 KB)
  • 053 Launch Ubuntu Machine on EC2.en.srt (6.2 KB)
  • 056 Update and Upgrade Your Ubuntu Packages.en.srt (3.5 KB)
  • 057 Install TensorFlow 2 and KTRAIN.mp4 (93.6 MB)
  • 058 Create Extra RAM from SSD by Memory Swapping.mp4 (83.7 MB)
  • 055 Install PIP3 on AWS Ubuntu.mp4 (44.6 MB)
  • 059 Deploy DistilBERT ML Model on EC2 Ubuntu Machine.mp4 (44.2 MB)
  • 051 Install Git Bash and Commander Terminal on Local Computer.mp4 (40.9 MB)
  • 052 Create AWS Account.mp4 (36.6 MB)
  • 054 Connect AWS Ubuntu (Linux) from Windows Computer.mp4 (32.5 MB)
  • 053 Launch Ubuntu Machine on EC2.mp4 (31.4 MB)
  • 056 Update and Upgrade Your Ubuntu Packages.mp4 (19.9 MB)
02 Fine Tuning BERT for Disaster Tweets Classification
  • 019 Number of Characters Distribution in Tweets.en.srt (14.6 KB)
  • 016 BERT Intro - Disaster Tweets Dataset Understanding.en.srt (14.2 KB)
  • 027 Word Embeddings and Classification with Deep Learning Part 2.en.srt (14.1 KB)
  • 015 Resources Folder.html (0.9 KB)
  • 029 BERT Model Evaluation.en.srt (13.1 KB)
  • 026 Word Embeddings and Classification with Deep Learning Part 1.en.srt (11.3 KB)
  • 025 Classification with Word2Vec and SVM.en.srt (11.1 KB)
  • 028 BERT Model Building and Training.en.srt (10.9 KB)
  • 024 Classification with TFIDF and SVM.en.srt (9.8 KB)
  • 021 Most and Least Common Words.en.srt (8.7 KB)
  • 018 Target Class Distribution.en.srt (8.6 KB)
  • 020 Number of Words, Average Words Length, and Stop words Distribution in Tweets.en.srt (8.4 KB)
  • 022 One-Shot Data Cleaning.en.srt (6.2 KB)
  • 023 Disaster Words Visualization with Word Cloud.en.srt (5.9 KB)
  • 017 Download Dataset.en.srt (5.5 KB)
  • 016 BERT Intro - Disaster Tweets Dataset Understanding.mp4 (109.8 MB)
  • 019 Number of Characters Distribution in Tweets.mp4 (83.5 MB)
  • 027 Word Embeddings and Classification with Deep Learning Part 2.mp4 (73.6 MB)
  • 029 BERT Model Evaluation.mp4 (58.4 MB)
  • 028 BERT Model Building and Training.mp4 (55.1 MB)
  • 025 Classification with Word2Vec and SVM.mp4 (52.9 MB)
  • 026 Word Embeddings and Classification with Deep Learning Part 1.mp4 (52.9 MB)
  • 024 Classification with TFIDF and SVM.mp4 (44.2 MB)
  • 021 Most and Least Common Words.mp4 (43.4 MB)
  • 023 Disaster Words Visualization with Word Cloud.mp4 (42.2 MB)
  • 020 Number of Words, Average Words Length, and Stop words Distribution in Tweets.mp4 (41.0 MB)
  • 022 One-Shot Data Cleaning.mp4 (32.0 MB)
  • 018 Target Class Distribution.mp4 (31.5 MB)
  • 017 Download Dataset.mp4 (29.7 MB)
  • 015 Fine-Tuning-BERT-for-Disaster-Tweets-Classification.zip (2.5 MB)
  • TutsNode.com.txt (0.1 KB)
    • .pad
    • 0 (0.1 KB)
    • 1 (385.5 KB)
    • 2 (203.9 KB)
    • 3 (262.3 KB)
    • 4 (425.4 KB)
    • 5 (224.5 KB)
    • 6 (98.7 KB)
    • 7 (287.2 KB)
    • 8 (483.7 KB)
    • 9 (244.5 KB)
    • 10 (562.3 KB)
    • 11 (524.5 KB)
    • 12 (969.5 KB)
    • 13 (438.7 KB)
    • 14 (1.9 KB)
    • 15 (544.1 KB)
    • 16 (594.7 KB)
    • 17 (197.7 KB)
    • 18 (436.3 KB)
    • 19 (717.0 KB)
    • 20 (384.8 KB)
    • 21 (581.3 KB)
    • 22 (743.9 KB)
    • 23 (880.0 KB)
    • 24 (311.1 KB)
    • 25 (166.5 KB)
    • 26 (838.5 KB)
    • 27 (944.4 KB)
    • 28 (875.2 KB)
    • 29 (402.6 KB)
    • 30 (108.0 KB)
    • 31 (130.7 KB)
    • 32 (525.2 KB)
    • 33 (585.2 KB)
    • 34 (269.4 KB)
    • 35 (700.7 KB)
    • 36 (332.1 KB)
    • 37 (248.1 KB)
    • 38 (738.3 KB)
    • 39 (397.9 KB)
    • 40 (819.9 KB)
    • 41 (835.9 KB)
    • 42 (628.8 KB)
    • 43 (414.7 KB)
    • 44 (863.5 KB)
    • 45 (413.5 KB)
    • 46 (0.0 KB)
    • 47 (83.2 KB)
    • 48 (802.8 KB)
    • 49 (387.5 KB)
    • 50 (993.2 KB)
    • 51 (382.2 KB)
    • 52 (387.7 KB)
    • 53 (390.2 KB)
    • 54 (253.5 KB)
    • 55 (145.2 KB)
    • 56 (167.7 KB)
    • 57 (265.8 KB)
    • 58 (462.7 KB)
    • 59 (633.6 KB)
    • 60 (993.3 KB)
    • 61 (536.5 KB)
    • 62 (625.6 KB)
    • 63 (700.1 KB)
    • 64 (57.0 KB)
    • 65 (278.8 KB)
    • 66 (889.3 KB)
    • 67 (552.9 KB)
    • 68 (571.1 KB)
    • 69 (639.2 KB)
    • 70 (246.9 KB)
    • 71 (125.8 KB)
    • 72 (980.1 KB)
    • 73 (792.1 KB)
    • 74 (73.3 KB)
    • 75 (131.4 KB)
    • 76 (475.0 KB)
    • 77 (223.7 KB)
    • 78 (95.4 KB)
    • 79 (868.3 KB)
    • 80 (776.8 KB)
    • 81 (110.0 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)

There are currently no comments. Feel free to leave one :)

Code:

  • udp://inferno.demonoid.pw:3391/announce
  • udp://tracker.openbittorrent.com:80/announce
  • udp://tracker.opentrackr.org:1337/announce
  • udp://torrent.gresille.org:80/announce
  • udp://glotorrents.pw:6969/announce
  • udp://tracker.leechers-paradise.org:6969/announce
  • udp://tracker.pirateparty.gr:6969/announce
  • udp://tracker.coppersurfer.tk:6969/announce
  • udp://ipv4.tracker.harry.lu:80/announce
  • udp://9.rarbg.to:2710/announce
  • udp://shadowshq.yi.org:6969/announce
  • udp://tracker.zer0day.to:1337/announce
REVERSE-PROXY 🔄 RP (success) | 1816ms 📄 torrent 🕐 17 Jan 2026, 06:58:03 pm IST ⏰ 11 Feb 2026, 06:58:03 pm IST ✅ Valid for 24d 23h 🔄 Wait 10m