Mlops, Machine Learning Operations For Beginners

Machine Learning models from experimentation to production

What you'll learn
Understand the lifecycle of a Machine Learning model
Gain the best practices for putting Machine Learning models in production
Leverage the power of MLOps to productionalise Machine Learning models at scale
Get some insights on how to choose your perfect MLOps stack
Requirements
Basic understanding of Machine Learning concepts (preferred)
Previous Experience in Software Development (preferred)
Some Experience in DevOps (not mandatory)
Description
This course is about Machine Learning Operations.Machine Learning and Artificial Intelligence have became a hot topic in recent years. Numerous techniques and algorithms were developed and proved their efficiencies in addressing business issues and bringing value to companies. Take fraud detection, recommendation systems or autonomous vehicles, etc. as examples.However, most of the developed machine learning models do not go to production! Among others, this is due to one major reason: Machine Learning models are not classical software. The existing frameworks and methodologies that work for classical software proved to be inadequate with Machine Learning models. Hence, new paradigms and concepts should be brought to handle the specificities of Machine Learning Algorithms.This course is addressed to Data professionals (Data Scientists, Data Engineers, Machine Learning Engineers and Software Engineers) as well as to everyone who want to understand the lifecycle of a Machine Learning model from experimentation to production. In this course, wa re going to see the best practices and recommended ways to put machine learning models into production. This will allow us also to see how we can leverage the power of MLOps to deploy Machine Learning at scale. Finally, as deploying models is about tooling, we are going to have a look on how to choose its perfect stack when adopting Machine Learning Operations best practices.Wish you a nice journey!

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Lecture 2 Course audience and prerequisites

Lecture 3 Take the most of this course

Section 2: MLOps Concepts

Lecture 4 MLOps: ML and Ops

Lecture 5 MLOps in the eyes of the giants

Lecture 6 From MLOps to DevOps and Vice-versa

Lecture 7 Traditional Vs. Machine Learning programming - Part 1

Lecture 8 The Machine Learning Lifecycle - Part 1

Lecture 9 The Machine Learning Lifeycyle - Part 2

Section 3: MLOps actors

Lecture 10 Subject Matter Expert

Lecture 11 Data Scientist

Lecture 12 Data Engineer

Lecture 13 Software Engineer

Lecture 14 DevOps Engineer

Lecture 15 Machine Learning Engineer

Section 4: MLOps tools

Lecture 16 MLOps tools

Everyone,Data Scientists,Machine Learning Engineers,Software Engineers,DevOps Engineers