Low Code Machine Learning and Deployment in Python - Hands On MLOps with MLflow PyCaret REST API Docker

This course will help anyone, at any level, to build a machine learning model and create a docker container in Python that can be deployed anywhere. Even if you are a complete beginner, you will have success. But if you have already built machine learning models countless times, you can still learn from this course, because your speed will increase if you want to create a baseline model very quickly. This course helps you implement machine learning prototyping as quickly as possible.

What you’ll learn

• Importance of MLOps, and also discuss the benefits of PyCaret and MLflow
• Develop machine learning models up to 10 times faster than usual and more reliably with PyCaret
• How to save the results and artifacts of machine learning model training experiments very simply, and how to view them later on a web user interface
• Deploy machine learning models up to 10 times faster and more reliably, create a REST API, Docker image with a few lines of code, test our created web service

This course will help anyone, at any level, to build a machine learning model and create a docker container in Python that can be deployed anywhere. Even if you are a complete beginner, you will have success. But if you have already built machine learning models countless times, you can still learn from this course, because your speed will increase if you want to create a baseline model very quickly. This course helps you implement machine learning prototyping as quickly as possible.

Who this course is for:

• Curious anybody about Machine Learning and/or MLOps
• Beginner/medior/senior Machine learning engineer
• Beginner/medior/senior Data scientist/Data Analyst
• Beginner/medior/senior Python developer
• Beginner/medior/senior DevOps engineer
• Beginner/medior/senior MLOps engineer
• Beginner/medior/senior Manager who want to see a productive way of machine learning development and deployment