Low Code Machine Learning and Deployment in Python - Hands On MLOps with MLflow PyCaret REST API Docker
Machine Learning model development, optimization, experiment tracking and deployment in Python
Machine Learning,Artificial Intelligence,Python,Data Science,Data Science and AI ML,
Lectures -15
Resources -14
Duration -1 hours
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Course Description
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
Goals
What will you learn in this course:
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 the 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 and artifacts (models, environment settings, etc.) of each experiment.
Learn how to deploy your machine learning model with one line of code. You will be able to create REST API and Docker containers for your machine-learning model. So your machine-learning model will be able to communicate with any programming language. So your model will get the inference (never seen data) and provide the predictions for them. And your application can be installed anywhere (cloud or on-premise).
Prerequisites
What are the prerequisites for this course?
- Very basic Python experience
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
15 Lectures
- Intro video 01:34 01:34
- About the Course 00:54 00:54
- About the Instructor 01:40 01:40
- Introduction to MLOps 05:57 05:57
- Introduction to PyCaret 03:06 03:06
- Introduction to MLflow 02:16 02:16
- About the Dataset 01:47 01:47
- Data Preprocessing with PyCaret 12:39 12:39
- PyCaret Setup Function Cheat Sheet and Documentation 05:54 05:54
- Train and Evaluate Your Machine Learning Model 03:12 03:12
- Optimize Your Machine Learning Model 07:52 07:52
- Track and Trace your Machine Learning Model with MLflow 05:26 05:26
- Deploy Your ML Model, Create a REST API and Test That 09:26 09:26
- Create a Docker Container for Your REST API 05:54 05:54
- Congratulations 00:15 00:15
Instructor Details
Gerzson David Boros
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