Designing and Implementing Data Science Solutions on Microsoft Azure (Live Virtual Class)

Blended learning

À qui s'adresse la formation?

Administrateurs systèmes et réseaux, Web Designer, Business Intelligence professionals, Consultants, Consultants IT, Data Scientist, Ingénieurs Systèmes, IT Professionals

Durée

4,00 jour(s)

Langues(s) de prestation

EN FR

Prochaine session

Objectifs

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

Contenu

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

Certificat, diplôme

Une attestation de participation sera transmise aux participants

Contact pour cette formation

Fiona Marin

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