1: ML and MLOps 10X faster! Hands-on MLOps MLflow PyCaret
1: ML and MLOps 10X faster! Hands-on MLOps MLflow PyCaret
How to build, track, deploy a machine learning model as fast as possible | MLOps coding: PyCaret and MLflow
Udemy Coupon Code
What you'll learn
- Importance of MLOps, and also discuss the benefits of PyCaret and MLflow
- Develop machine learning models in Python 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
Description
Udemy Discount Coupon
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.
Learn how to preprocess data much faster than usual in Python
Learn how to train even more than 10 different machine learning models together and compare them in Python
Learn how to optimize your machine learning models with help of different optimization packages from PyCaret with one line of code
Learn how to track your machine learning model building experiments.
Save the results, artifacts (models, environment settings, etc.) of each experiment.
Requirements
- Very basic Python experience
Gerzson David Boros is the owner and CEO of Data Science Europe and a chief machine learning engineer
