ML and MLOps 10X faster! Hands-on MLOps MLflow PyCaret 2023

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Gerzson Boros

1:06:18

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1 - Introduction

1 - About the course.mp4

00:54

1 - github-link-for-the-code.zip

1 - text.zip

2 - About the instructor.mp4

01:40

2 - text.zip

2 - MLOps Pycaret MLflow

3 - Introduction to MLOps.mp4

05:57

3 - reading.zip

3 - text.zip

4 - Introduction to PyCaret.mp4

03:06

4 - text.zip

5 - Introduction to MLflow.mp4

02:16

5 - text.zip

3 - Machine Learning development much faster than usual with PyCaret

6 - About the dataset.mp4

01:47

7 - Data preprocessing with PyCaret.mp4

12:39

8 - PyCaret setup function cheat sheet and documentation.mp4

05:54

8 - PyCaret-Functions-setup.pdf

9 - Machine Learning model train and evaluate with PyCaret.mp4

03:12

10 - Machine learning model optimize with PyCaret.mp4

07:52

4 - Machine Learning model tracking

11 - Tracking with MLflow.mp4

05:26

5 - Deploy machine learning model

12 - Create a REST API and test that in multiple ways.mp4

09:26

12 - rest-api-mistakes.zip

12 - rest-api-reading.zip

13 - Create Docker container for machine learning model.mp4

05:54

13 - docker-mistakes.zip

13 - docker-reading.zip

6 - Congratulations

14 - Congratulations.mp4

00:15

Description

How to build, track, deploy a machine learning model as fast as possible | MLOps coding: PyCaret and MLflow

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

Who is this for?

Curious anybody about Machine Learning and/or MLOps with some ML knowledge

Medior/senior Machine learning engineer

Medior/senior Data scientist/Data Analyst

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

More details

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.

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.
Learn how to deploy your machine learning model with one line of code. You will be able to create RESTÂ APIÂ and Docker container for your machine learning model. So your machine learning model will be able to communicate with any programming languages. 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).
Who this course is for:
Curious anybody about Machine Learning and/or MLOps with some ML knowledge
Medior/senior Machine learning engineer
Medior/senior Data scientist/Data Analyst
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

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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 on Udemy 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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