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Master Machine Learning Model Deployment with Python
Updated on Sep, 2026
Programming, Data Science, Machine Learning
Duration - 7.5 hours
Training 5 or more people ?
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This comprehensive course will teach you everything you need to know to deploy machine learning models in production, including Flask, Streamlit, AWS Lambda, and SageMaker. This includes using both server and serverless frameworks in Python.
This course will help you learn how to use models that are scalable, reliable, and secure. You will also learn how to choose the right deployment framework for your needs and how to securely and reliably configure and deploy your models.
Learn to use Docker to containerize your machine-learning models and configure and deploy machine-learning models. The curriculum of this course covers a wide range of topics, including:
Model deployment fundamentals
Server-based model deployment with Flask
Serverless model deployment with AWS Lambda
Model deployment on AWS SageMaker
Containerization with Docker
Learn the Basics of Flask
Learn to Deploy a Machine Learning Model using Flask.
Learn the Basics of Steamlit.
Learn to Deploy a Machine Learning Model using Streamlit.
Learn Serverless and AWS Lambda.
Learn to Deploy Machine Learning Model using Serverless and Lambda.
Learn the Basics of Docker.
Learn to Deploy Machine Learning Model using Docker Container.
Learn To Deploy model on AWS Sagemaker.
Learn about Model Training and End Point Hosting on Sagemaker.
This course is designed for intermediate Python developers with basic knowledge of machine learning.
Check out the detailed breakdown of what’s inside the course
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