Deployment of Machine Learning Models in Production | Python

Deployment of Machine Learning Models in Production | Python

Deploy ML Model with BERT, DistilBERT, FastText NLP Models in Production with Flask, uWSGI, and NGINX at AWS EC2

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

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

Course content

7 sections • 83 lectures • 9h 37m total length
  • BERT | Sentiment Prediction | Multi Class Prediction Problem
  • Fine Tuning BERT for Disaster Tweets Classification
  • DistilBERT | Faster and Cheaper BERT model from Hugging Face
  • Deploy Your DistilBERT ML Model at AWS EC2 Windows Machine with Flask
  • Deploy Your DistilBERT ML Model at AWS Ubuntu (Linux) Machine with Flask
  • Deploy Robust and Secure Production Server with NGINX, uWSGI, and Flask
  • Multi-Label Classification | Deploy Facebook’s FastText NLP Model in Production
Created by: Laxmi Kant Principal Data Scientist at mBreath and KGPTalkie
Last updated 1/2021
English
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Highest Rated
Rating: 4.7 out of 5
(124 ratings)
2,990 students

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