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
- You will learn how to deploy machine learning models on AWS EC2 using NGINX as a web server, FLASK as a web framework, and uwsgi as a bridge between the two.
- You will learn how to use fasttext for natural language processing tasks in production, and integrate it with TensorFlow for more advanced machine learning
- You will learn how to use ktrain, a library built on top of TensorFlow, to easily train and deploy models in a production environment.
- You will gain hands-on experience in setting up and configuring an end-to-end machine learning production pipeline using the aforementioned technologies.
- You will learn how to optimize and fine-tune machine learning models for production use, and how to handle scaling and performance issues.
- 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
- You will learn how to develop and deploy FastText model on AWS
- Learn Multi-Label and Multi-Class classification in NLP
