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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
Laxmi Kant
3,860 students enrolled
English [Auto]
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

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.

You should have an introductory knowledge of Python, Machine Learning, and Natural Language Processing before enrolling in this course otherwise please do not enroll in this course. This is an advanced NLP course.

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.

You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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10 hours on-demand video
5 articles
Full lifetime access
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Certificate of Completion
Deployment of Machine Learning Models in Production | Python
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