Low Code Machine Learning and Deployment in Python: Hands On Training
Machine Learning model development, optimization, experiment tracking and deployment in Python
Updated on Feb, 2025
Language - English
Development ,Data Science,Python
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
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
- 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
Gerzson David Boros is the owner and CEO of Data Science Europe and a chief machine learning engineer who has been involved in data science and AI for more than 10 years.
He holds an MSc, and is an instructor and with the International Institute of Information Technology Bangalore via Upgrad. He holds courses and live sessions for MLOps in a Postgraduate Program in Machine Learning and Artificial Intelligence. In the past he worked as a Technical Reviewer at Packt. He regularly author’s articles on the subject of machine learning, and MLOps.
Gerzson is also currently working on the latest developments in AI, such as its use in a cancer diagnosis project. In the last 5 years, he and his team have produced business proposals for 100 different executives and successfully worked on more than 30 different projects on the topic of data science and artificial intelligence.
His motto is “Social responsibility is achievable with the help of data.”
He is passionate about sharing his knowledge with others.
Gerzson, is a proud father and husband. In his spare time he likes to travel and is a professional drummer who plays with his wife in various folk and world music bands.
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