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Ml And Mlops 10X Faster! Hands-On Mlops Mlflow Pycaret 2023
Published 3/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 853.29 MB | Duration: 1h 6m
How to build, track, deploy, register a machine learning model as fast as possible | MLOps coding: PyCaret and MLflow
Published 3/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 853.29 MB | Duration: 1h 6m
How to build, track, deploy, register 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 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
Requirements
Very basic Python experience
Description
This course will help anyone, at any level, to build a machine learning model and create a docker container 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 usualLearn how to train even more than 10 different machine learning models together and compare themLearn how to optimize your machine learning models with help of different optimization packages from PyCaret with one line of codeLearn 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 REST API 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).
Overview
Section 1: Introduction
Lecture 1 About the course
Lecture 2 About the instructor
Section 2: MLOps, Pycaret, MLflow
Lecture 3 Introduction to MLOps
Lecture 4 Introduction to PyCaret
Lecture 5 Introduction to MLflow
Section 3: Machine Learning development much faster than usual with PyCaret
Lecture 6 About the dataset
Lecture 7 Data preprocessing with PyCaret
Lecture 8 PyCaret setup function cheat sheet and documentation
Lecture 9 Machine Learning model train and evaluate with PyCaret
Lecture 10 Machine learning model optimize with PyCaret
Section 4: Machine Learning model tracking
Lecture 11 Tracking with MLflow
Section 5: Deploy machine learning model
Lecture 12 Create a REST API and test that in multiple ways
Lecture 13 Create Docker container for machine learning model
Section 6: Congratulations
Lecture 14 Congratulations
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
Download link
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