MLOps with MLflow: Getting Started
Mlflow 2.3.2
| Beginner
- 13 videos | 1h 27m 3s
- Includes Assessment
- Earns a Badge
MLflow plays a crucial role in systemizing the machine learning (ML) workflow by providing a unified platform that seamlessly integrates different stages of the ML life cycle. In the course, you will delve into the theoretical aspects of the end-to-end machine learning workflow, covering data preprocessing and visualization. You will learn the importance of data cleaning and feature engineering to prepare datasets for model training. You will explore the MLflow platform that streamlines experiment tracking, model versioning, and deployment management, aiding in better collaboration and model reproducibility. Next, you will explore MLflow's core components, understanding their significance in data science and model deployment. You'll dive into the Model Registry that enables organized model versioning and explore MLflow Tracking as a powerful tool for logging and visualizing experiment metrics and model performance. Finally, you'll focus on practical aspects, including setting up MLflow in a virtual environment, understanding the user interface, and integrating MLflow capabilities into Jupyter notebooks.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Provide an overview of mlflow
Outline the use of the machine learning (ml) workflow
Recognize how model deployment works in mlflow
Outline mlflow concepts and components
Recognize the features offered by mlflow
Outline how mlflow signatures work -
Provide an overview of mlflow tracking
Explore the mlflow install documentation
Install mlflow in a virtual environment
View the mlflow user interface (ui) and directory structure
Set up an mlflow virtual environment for jupyter
Summarize the key concepts covered in this course
IN THIS COURSE
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1m 49sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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7m 16sAfter completing this video, you will be able to provide an overview of Mlflow. FREE ACCESS
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3. The Machine Learning Workflow6m 24sUpon completion of this video, you will be able to outline the use of the machine learning (ML) workflow. FREE ACCESS
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4. Understanding Model Deployment9m 12sAfter completing this video, you will be able to recognize how model deployment works in Mlflow. FREE ACCESS
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5. MLflow Concepts and Components4m 5sUpon completion of this video, you will be able to outline MLflow concepts and components. FREE ACCESS
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6. The Features of MLflow9m 54sAfter completing this video, you will be able to recognize the features offered by Mlflow. FREE ACCESS
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7. Model Signature8m 7sUpon completion of this video, you will be able to outline how MLflow signatures work. FREE ACCESS
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8. MLflow Tracking7m 4sAfter completing this video, you will be able to provide an overview of MLflow Tracking. FREE ACCESS
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9. Installing MLflow6m 20sLearn how to explore the MLflow install documentation. FREE ACCESS
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10. Installing MLflow in a Virtual Environment8m 16sFind out how to install MLflow in a virtual environment. FREE ACCESS
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11. Viewing the MLflow User Interface (UI) and Directory Structure4m 55sIn this video, discover how to view the MLflow User Interface (UI) and directory structure. FREE ACCESS
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12. Setting up an MLflow Virtual Environment for Jupyter9m 58sDiscover how to set up an MLflow virtual environment for Jupyter. FREE ACCESS
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13. Course Summary3m 42sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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