Descriptions
Introduction to MLflow, Administration The EndThe entire lifecycle of a machine learning application can be a daunting task for data scientists, engineers, and developers. Machine learning applications are complex and proven to be 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 Learn With MLflow, you will gain a better understanding of how to overcome the complexity of building machine learning applications and navigate through the different stages of the machine learning lifecycle. During the course, you will dive deep into the four main components that make up the MLflow platform.
You will explore how to track models, metrics, and parameters with MLflow Tracking, package reproducible ML code with MLflow Projects, build and deploy models with MLflow Models, and store and version models with Model Registry. During the course, you will also learn best practices for using MLflow to version models, how to evaluate models, add customizations to models, and how to incorporate automation into training runs. This course will prepare you to successfully manage the lifecycle of your next machine learning application.
What you will learn
- MLflow Models
- concept of MLflow is called Model Registry.
- valuable Knowledge of how to optimize your data science code for reusability and reproducibility
MLflow Introduction Specifications
- Publisher : Data Camp
- Teacher: Weston Bassler
- Language: English
- Level: All levels
- Number of courses: 4
- Duration: 4 hours and 0 minutes
Contents of the introduction to MLflow
Requirements
- Supervised learning with scikit-learn
- MLOps concepts
Pictures
Sample clip
installation Guide
Extract the files and watch them with your favorite player
Subtitles: English
Quality: 720p
Download links
Password file(s): free download software
File size
97MB