Description
Introduction to MLflow, MLflow training course published by Datacamp Academy. 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, repeatable, and problematic to deploy. In this lesson, you’ll learn what MLflow is and how it tries to simplify machine learning lifecycle problems like tracking, repeatability, and scalability. After learning MLflow, you will have a better understanding of how to overcome the complexities of building machine learning applications and how to go through the different stages of the machine learning lifecycle.
You will learn how to trace models, metrics, and parameters with MLflow tracing, package repeatable ML code using MLflow projects, create and deploy models using MLflow models, and version control and save models using model registration. will check As you progress through this course, you’ll learn best practices for using MLflow to script models, how to evaluate models, add customization to models, and how to automate training execution. This course prepares you for success in managing the next machine learning application lifecycle. Next, we will discuss the four main parts that make up the MLflow platform.
What you will learn
- MLflow models
- The concept of model registration in MLflow
- Valuable knowledge on how to simplify data science code for reusability and repeatability
Introduction to MLflow course specifications
- Publisher: Datacamp
- teacher : Weston Bassler
- English language
- Education level: all levels
- Number of courses: 4
- Training duration: 4 hours and 0 minutes
Introduction to MLflow chapters
Course prerequisites
- Supervised learning with scikit-learn
- MLOps Concepts
Pictures
Sample video
Installation guide
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English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
97 MB
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