What will you in Deployment of Machine Learning Models Course
Learn various deployment strategies for machine learning models.
Understand how to use Flask, FastAPI, Streamlit, and Docker for deploying ML models.
Master real-world deployment workflows: REST APIs, web apps, and containerization.
Automate model serving and expose predictions via production-ready endpoints.
Build and deploy end-to-end machine learning applications.
Program Overview
Module 1: Introduction to Model Deployment
⏳ 30 minutes
Why deployment is essential in ML lifecycle.
Overview of deployment strategies: batch, online, and real-time.
Module 2: Creating REST APIs with Flask
⏳ 45 minutes
Converting ML models into RESTful APIs.
Building backend services using Flask.
Module 3: Deploying with FastAPI
⏳ 60 minutes
Advantages of FastAPI over Flask for ML.
Creating scalable and high-performance ML APIs.
Module 4: Building ML Web Apps with Streamlit
⏳ 60 minutes
Interactive frontends for ML models using Streamlit.
Deploying Streamlit apps locally and on the cloud.
Module 5: Model Deployment with Docker
⏳ 60 minutes
Dockerizing ML projects for consistent environments.
Running and managing containers for deployment.
Module 6: Deployment on Cloud Platforms
⏳ 45 minutes
Overview of deployment on Heroku, AWS, and other platforms.
Pushing models to production environments.
Module 7: End-to-End Project Deployment
⏳ 75 minutes
Full ML app deployment from training to production.
Code structure, version control, and CI/CD tips.
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Job Outlook
High Demand: ML deployment skills are essential for production-ready AI.
Career Advancement: Key for ML engineers, data scientists, and full-stack developers.
Salary Potential: $95K–$150K+ for professionals with deployment expertise.
Freelance Opportunities: Model API development, app integration, and DevOps for ML startups.
Specification: Deployment of Machine Learning Models
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