Designing and Implementing Data Science Solutions on Microsoft Azure

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Nächst Sessioun

Ziler

1: Design a machine learning solution

  • Design a data ingestion strategy for machine learning projects
  • Design a machine learning model training solution
  • Design a model deployment solution

2: Explore the Azure Machine Learning workspace
Explore Azure Machine Learning workspace resources and assets

  • Explore developer tools for workspace interaction

3: Make data available in Azure Machine Learning

  • Make data available in Azure Machine Learning

4: Work with compute in Azure Machine Learning

  • Work with compute targets in Azure Machine Learning
  • Work with environments in Azure Machine Learning

5: Use no-code machine learning with the Azure Machine Learning Designer

  • Explore data with the Azure Machine Learning Designer
  • Train and compare models with the Azure Machine Learning Designer

6: Automate machine learning model selection with Azure Machine Learning

  • Explore Automate Machine Learning
  • Find the best classification model with Automated Machine Learning

7: Use notebooks for experimentation in Azure Machine Learning

  • Track model training in Jupyter notebooks with MLflow

8: Train models with scripts in Azur Machine Learning

  • Run a training script as a command job in Azure Machine Learning
  • Track model training with MLflow in jobs

9: Optimize model training in Azure Machine Learning

  • Run pipelines in Azure Machine Learning
  • Perform hyperparameter tuning with Azure Machine Learning

10: Manage and review models in Azure Machine Learning

  • Register an MLflow model in Azure Machine Learning
  • Manage and compare models in Azure Machine Learning

11: Deploy and consume models with Azure Machine Learning

  • Deploy a model to a managed online endpoint
  • Deploy a model to a batch endpoint

12: Design a machine learning operations (MLOps) solution

  • Design a machine learning operations (MLOps) solution

Inhalt

1: Design a machine learning solution
  • Design a data ingestion strategy for machine learning projects
  • Design a machine learning model training solution
  • Design a model deployment solution
2: Explore the Azure Machine Learning workspace
  • Explore Azure Machine Learning workspace resources and assets
  • Explore developer tools for workspace interaction
3: Make data available in Azure Machine Learning
  • Make data available in Azure Machine Learning
4: Work with compute in Azure Machine Learning
  • Work with compute targets in Azure Machine Learning
  • Work with environments in Azure Machine Learning
5: Use no-code machine learning with the Azure Machine Learning Designer
  • Explore data with the Azure Machine Learning Designer
  • Train and compare models with the Azure Machine Learning Designer
6: Automate machine learning model selection with Azure Machine Learning
  • Explore Automate Machine Learning
  • Find the best classification model with Automated Machine Learning
7: Use notebooks for experimentation in Azure Machine Learning
  • Track model training in Jupyter notebooks with MLflow
8: Train models with scripts in Azur Machine Learning
  • Run a training script as a command job in Azure Machine Learning
  • Track model training with MLflow in jobs
9: Optimize model training in Azure Machine Learning
  • Run pipelines in Azure Machine Learning
  • Perform hyperparameter tuning with Azure Machine Learning
10: Manage and review models in Azure Machine Learning
  • Register an MLflow model in Azure Machine Learning
  • Manage and compare models in Azure Machine Learning
11: Deploy and consume models with Azure Machine Learning
  • Deploy a model to a managed online endpoint
  • Deploy a model to a batch endpoint
12: Design a machine learning operations (MLOps) solution
  • Design a machine learning operations (MLOps) solution

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Fiona Marin

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