MLOps with MLflow: Registering & Deploying ML Models
Mlflow 2.3.2
| Intermediate
- 15 videos | 1h 57m 10s
- Includes Assessment
- Earns a Badge
The MLflow Model Registry enables easy registration and deployment of machine learning (ML) models for future use, either locally or in the cloud. It streamlines model management, facilitating collaboration among team members during model development and deployment. In this course, you will create classification models using the regular ML workflow. You'll see that visualizing and cleaning data, running experiments, and analyzing model performance using SHapley Additive exPlanations (SHAP) will provide valuable insights for decision-making. You'll also discover how programmatic comparison will aid in selecting the best-performing model. Next, you'll explore the powerful MLflow Models feature, enabling efficient model versioning and management. You'll learn how to modify registered model versions, work with different versions of the same model, and serve models to Representational State Transfer (REST) endpoints. Finally, you'll explore integrating MLflow with Azure Machine Learning, leveraging the cloud's power for model development.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Visualize and clean data
Create an experiment from the mlflow user interface (ui)
Run a classification model and view the metrics
Analyze model insights using shapley additive explanations (shap)
Run multiple classification models
Compare models programmatically
Register an mlflow model -
Modify registered model versions and read models into python
Register another model and view the registered model
Serve models to a rest endpoint and access that endpoint
Create an azure ml workspace and register a model to azure
Deploy a model to azure and view this model
Predict using a model's representational state transfer (rest) endpoint
Summarize the key concepts covered in this course
IN THIS COURSE
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1m 35sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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10m 15sFind out how to visualize and clean data. FREE ACCESS
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3. Creating an Experiment from the MLflow UI9m 15sIn this video, find out how to create an experiment from the MLflow user interface (UI). FREE ACCESS
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4. Running a Classification Model and Viewing its Metrics8m 10sLearn how to run a classification model and view the metrics. FREE ACCESS
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5. Analyzing Model Insights Using SHAP10m 37sDuring this video, discover how to analyze model insights using SHapley Additive exPlanations (SHAP). FREE ACCESS
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6. Running Multiple Classification Models7m 32sIn this video, you will learn how to run multiple classification models. FREE ACCESS
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7. Comparing Models Programmatically10m 10sDiscover how to compare models programmatically. FREE ACCESS
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8. Registering an MLflow Model8m 2sFind out how to register an MLflow model. FREE ACCESS
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9. Modifying Registered Model Versions8m 22sIn this video, find out how to modify registered model versions and read models into Python. FREE ACCESS
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10. Registering Another Model and Viewing the Registered Model6m 48sLearn how to register another model and view the registered model. FREE ACCESS
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11. Serving Models to a Local REST Endpoint6m 1sDuring this video, discover how to serve models to a REST endpoint and access that endpoint. FREE ACCESS
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12. Creating an Azure Machine Learning (Azure ML) Account10m 58sIn this video, you will learn how to create an Azure ML workspace and register a model to Azure. FREE ACCESS
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13. Registering a Model on Azure7m 58sDiscover how to deploy a model to Azure and view this model. FREE ACCESS
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14. Accessing Models through Azure REST Endpoints8m 37sLearn how to predict using a model's Representational State Transfer (REST) endpoint . FREE ACCESS
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15. Course Summary2m 50sIn 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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