Deployment of ML Models - Complete Tutorial Series

Learn to deploy machine learning models across multiple cloud platforms and frameworks through this comprehensive tutorial series. Master the fundamentals of deployment environments by comparing on-premises, Infrastructure as a Service (IaaS), and Platform as a Service (PaaS) cloud platforms. Build practical deployment skills using Flask to deploy ML models on Heroku, including specialized applications like NLP models and movie recommendation systems. Explore enterprise-level deployment strategies on major cloud platforms including AWS EC2 instances, Google Cloud Platform, and Microsoft Azure. Develop modern web applications using Streamlit for interactive ML model interfaces and create robust APIs with FastAPI for scalable model serving. Gain hands-on experience with PyWebIO integration and learn best practices for deploying machine learning models as production-ready APIs across different cloud environments.

Syllabus

Tutorial 1- Deployment of Models- Premises VS IAAS vs PAAS cloud Platforms
Tutorial 2- Deployment of ML models in Heroku using FLASK
Tutorial 3- Deployment of NLP Model in Heroku Cloud
Tutorial 4- Deployment Of ML Models In AWS EC2 Instance
Tutorial 5- Deployment Of Movie Recommendation Model By My Subscriber In Heroku
Tutorial 6 :Deployment of Machine Learning Models in Google Cloud Platform
Tutorial 7: Deploying Machine Learning Models In Azure Cloud
Deploy Streamlit WebApp ML Models In Heroku(PAAS)- Data Science
Introduction To FastAPI- How To Create API's Using FastAPI- Data Science
How To Deploy Machine Learning Models Using FastAPI-Deployment Of ML Models As API’s
How To Deploy FastAPI Machine Learning Models In Heroku
Deployment Of ML Models Using PyWebIO And Flask