Nuts and bolts of MLFlow

Nuts and bolts of MLFlow, available at $49.99, has an average rating of 3.3, with 32 lectures, based on 38 reviews, and has 211 subscribers.

You will learn about Understand in great details how MLFlow works Build an End-to-End MLOps pipeline from experimentation to predictions Build your MLOps stack on AWS with MLFlow using EC2, S3 and RDS Be able to track experiments in MLFlow Understand how Machine Learning models are logged in mlflow Make use of MLFlow model Registry Serve your models in MLFlow to make prediction This course is ideal for individuals who are Anyone or Data Sciencits who wants to learn how to track their experiments or Machine Learning Engineers who want to build their MLOps stacks It is particularly useful for Anyone or Data Sciencits who wants to learn how to track their experiments or Machine Learning Engineers who want to build their MLOps stacks.

Enroll now: Nuts and bolts of MLFlow

Summary

Title: Nuts and bolts of MLFlow

Price: $49.99

Average Rating: 3.3

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Understand in great details how MLFlow works
Build an End-to-End MLOps pipeline from experimentation to predictions
Build your MLOps stack on AWS with MLFlow using EC2, S3 and RDS
Be able to track experiments in MLFlow
Understand how Machine Learning models are logged in mlflow
Make use of MLFlow model Registry
Serve your models in MLFlow to make prediction

Who Should Attend

Anyone
Data Sciencits who wants to learn how to track their experiments
Machine Learning Engineers who want to build their MLOps stacks

Target Audiences

Anyone
Data Sciencits who wants to learn how to track their experiments
Machine Learning Engineers who want to build their MLOps stacks

This course is about MLOps.

When choosing your MLOps stack, MLFlow is probably the most populat solution for tracking experiments, registering and serving models.

This course will give you a deep dive on how MLFlow works and how you can build your own MLOps stack with mlflow using Amazon Web Services (AWS).

We will start the course by giving an overall overvew of what mlflow is and why it is necessary for Machine Learning and Data Science. Next we will explore in detail the most important component of MLFlow which is mlflow tracking where we will have a look at how tracking works and how you what can be tracked.

Next, we will move to MLflow model registry where we will cover how to register a model in a mlflow and how to manage its lifecycle. We will also learn how to retrieve a model from the registry in order to make predictions.

The next topic is MLFlow models. Here, we will explore how models work as well as the different types (flavours) of a saved model. We will also, serve some of the models in order to make predictions.

The last section is optional and will cover how to build, step by step, an MLOps architecture based on MLFlow using Amazon Web Services such as Amazon EC2, Amazon S3 and Amazon RDS.
This course will not focus on data science and machine learning, so do not except to learn the details of Machine Learning models. We will take a simple clustering model as an example that will illustrate any Machine Learning Model.

Good luck.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: Take the Most of this Course

Chapter 2: Tracking

Lecture 1: Introduction – What is MLFlow?

Lecture 2: Prepare your working environment – Part 1

Lecture 3: Prepare your working environment – Part 2

Lecture 4: Prepare your working environment – Part 3

Lecture 5: Discovering MLFlow

Lecture 6: First MLFlow program

Lecture 7: First MLFlow run

Lecture 8: Experiments

Lecture 9: Logging

Lecture 10: Assignement

Lecture 11: Solution

Lecture 12: Explore the User Interface

Lecture 13: Architecture Focus 1

Lecture 14: Architecture Focus 2

Lecture 15: Introducing SQLite

Lecture 16: Architecture Focus 3

Lecture 17: MLFlow with Local Tracking Server

Lecture 18: Architecture Focus 4

Chapter 3: Model Registry

Lecture 1: MLFlow auto-logging

Lecture 2: Model Registry

Lecture 3: Consume Models from Model Registry

Chapter 4: Models

Lecture 1: MLFlow Models

Lecture 2: Serve MLFlow Models

Chapter 5: Build MLFlow Infrastructure on AWS

Lecture 1: Architetcure Overview

Lecture 2: Create S3 artifact store

Lecture 3: Create RDS backend

Lecture 4: Create EC2 server – Part 1

Lecture 5: Create EC2 server – Part 2

Lecture 6: End-to-End test

Chapter 6: BONUS

Lecture 1: Bonus

Instructors

Dat Art
Data centric trainings and courses

Rating Distribution

1 stars: 4 votes
2 stars: 2 votes
3 stars: 10 votes
4 stars: 7 votes
5 stars: 15 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu