Nuts and bolts of MLFlow

Learn MLFlow and build your MLOps stack on AWS

Ratings: 3.30 / 5.00




Description

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.

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