Operationalize machine learning models (MLOps)
At a glance
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Role
Follow an end-to-end MLOps workflow across two stages. First, design a training solution, experiment with models, track training with MLflow, and tune hyperparameters. Then, build pipelines, plan an MLOps solution, automate model training with GitHub Actions, and deploy and monitor a model.
Prerequisites
- Programming experience with Python or R
- Experience developing and training machine learning models
- Familiarity with basic Azure Machine Learning concepts
Achievement Code
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Modules in this learning path
Choose this module if you want to design a machine learning training solution and select Azure Machine Learning services and compute.
Choose this module if you want to compare models by using automated machine learning, MLflow, and the Responsible AI dashboard.
Choose this module if you want to convert notebooks to training scripts, run command jobs, and track models with MLflow.
Choose this module if you want to optimize model training by tuning hyperparameters with sweep jobs in Azure Machine Learning.
Choose this module if you want to automate multistep machine learning workflows with reusable components and Azure Machine Learning pipelines.
Choose this module if you want to design an MLOps architecture for monitoring and retraining production models.
Choose this module if you want to automate and validate model training with GitHub Actions and Azure Machine Learning pipelines.
Choose this module if you want to register, deploy, monitor, and roll back models by using GitHub Actions and protected environments.