Deployment of Machine Learning Models in Production | Python
Updated on
Course overview
- Provider
- Udemy
- Course type
- Paid course
- Level
- All Levels
- Duration
- 10 hours
- Lessons
- 85 lessons
- Certificate
- Available on completion
- Course author
- Laxmi Kant
-
- 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
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.
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.
Similar courses
-
30 hours
-
488 lessonsCertificate
-
21 hours
-
218 lessonsCertificate
