Published 3/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: 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 s faster than usual and more reliably with PyCaret

How to save the results and artifacts of machine learning model training expents very simply, and how to view them later on a web user interface

Deploy machine learning models up to 10 s 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 bner, you will have success. But if you have already built machine learning models countless s, 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 expents. Save the results, artifacts (models, environment settings, etc.) of each expent.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,Bner/medior/senior Machine learning eeer,Bner/medior/senior Data scientist/Data Analyst,Bner/medior/senior Python developer,Bner/medior/senior DevOps eeer,Bner/medior/senior MLOps eeer,Bner/medior/senior Manager who want to see a productive way of machine learning development and deployment

HomePage: