Introduction to MLflow
Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.
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Course Description
Managing the end-to-end lifecycle of a Machine Learning application can be a daunting task for data scientists, engineers, and developers. Machine Learning applications are complex and have a proven track record of being difficult to track, hard to reproduce, and problematic to deploy.
In this course, you will learn what MLflow is and how it attempts to simplify the difficulties of the Machine Learning lifecycle such as tracking, reproducibility, and deployment. After learning MLflow, you will have a better understanding of how to overcome the complexities of building Machine Learning applications and how to navigate different stages of the Machine Learning lifecycle.
Throughout the course, you will deep dive into the four major components that make up the MLflow platform. You will explore how to track models, metrics, and parameters with MLflow Tracking, package reproducible ML code using MLflow Projects, create and deploy models using MLflow Models, and store and version control models using Model Registry.
As you progress through the course, you will also learn best practices of using MLflow for versioning models, how to evaluate models, add customizations to models, and how to build automation into training runs. This course will prepare you for success in managing the lifecycle of your next Machine Learning application.
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Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Machine Learning Engineer
Go To TrackMachine Learning in Production in Python
Go To Track- 1
Introduction to MLflow
FreeIn this Chapter, you will be introduced to MLflow and how it aims to assist with some difficulties of the Machine Learning lifecycle. You will be introduced to the four main concepts that make up MLflow with a main focus on MLflow Tracking. You will learn to create experiments and runs as well as how to track metrics, parameters, and artifacts. Finally, you will search MLflow programmatically to find experiment runs that fit certain criteria.
- 2
MLflow Models
FreeIn this Chapter, you will be introduced to MLflow Models. The MLflow Models component of MLflow plays an essential role in the Model Evaluation and Model Engineering steps of the Machine Learning lifecycle. You will learn how MLflow Models standardizes the packaging of ML models as well as how to save, log and load them. You will learn how to create custom MLflow Models to provide more flexibility to your use cases as well as how to evaluate model performance. You will utilize the powerful concept of “Flavors” and finally use the MLflow command line tool for model deployment.
Introduction to MLflow Models50 xpPackage a machine learning model100 xpStorage Format50 xpWhat's in an MLmodel file?50 xpModel API50 xpSaving and loading a model100 xpLogging and loading a model100 xpCustom models50 xpCreating a custom Python Class100 xpCustom scikit-learn model100 xpScikit-learn flavor and evaluation100 xpModel serving50 xpServing a model50 xpScore from a served model50 xp - 3
Mlflow Model Registry
This Chapter introduces the concept of MLflow called the Model Registry. You will be introduced to how the Model Registry is used to manage the lifecycle of ML models. You will learn how to create and search for models in the Model Registry. You then learn how to register models to the Model Registry and learn how to transition models between predefined stages. Finally, you will also learn how to deploy models from the Model Registry.
Introduction to MLflow Model Registry50 xpCreate a Model100 xpSearching Models100 xpRegistering Models50 xpRegistering existing models100 xpRegistering new models100 xpModel stages50 xpModel stages in Model Registry50 xpTransitioning model stages100 xpModel deployment50 xpServing models from the Model Registry50 xpLoading models from the Model Registry100 xp - 4
MLflow Projects
In this chapter, you'll gain valuable knowledge on how to streamline your data science code for reusability and reproducibility using MLflow Projects. The chapter begins by introducing the concept of MLflow Projects and walking you through creating an MLproject file. From there, you'll learn how to run MLflow Projects through both the command-line and the MLflow Projects module while also discovering the power of using parameters for added flexibility in your code. Finally, you will learn how to manage steps of the machine learning lifecycle by creating a multi-step workflow using MLflow Projects.
Introduction to MLflow Projects50 xpMLproject file layout50 xpCreating an MLproject100 xpRunning MLflow Projects50 xpMLflow run command50 xpMLflow projects module100 xpSpecifying parameters50 xpAdding parameters to MLproject100 xpAdding parameters to project run100 xpWorkflows50 xpCreating an MLproject for the ML Lifecycle: Model Engineering100 xpCreating an MLproject for the ML Lifecycle: Model Evaluation100 xpCreating a multi-step workflow: Model Engineering100 xpCreating a multi-step workflow: Model Evaluation100 xpWrap-up50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Machine Learning Engineer
Go To TrackMachine Learning in Production in Python
Go To Trackcollaborators
Weston BasslerSenior MLOps Engineer
Former DevOps turned MLOps. I currently work at Emburse where I enjoy building and scaling ML projects. I have a passion for bridging the gap between Machine Learning and Operations, automating ML workflows and pushing technology boundaries.
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