DP-100 Designing and Implementing a Data Science solution on Azure

Designing and Implementing a Data Science solution on Azure -koulutuksessa käydään läpi uudet tavat tehdä Machine Learning -malleja Azuressa ilman koodausta Machine Learning Designerilla, sekä Notebookeilla.

Lisäksi koulutuksessa käydään läpi koko ML Pipeline: Datan käsittely, laskentaympäristön pystyttäminen, algoritimien automatisoitu valinta, hyperparametrien virittäminen, tuotantoon siirto, monitorointi jne. Siis käytännössä koko ketju alusta loppuun modernin koneoppimisen toteuttamiseen Azure-ympäristössä.

Tavoite

Opi kuinka teet modernia koneoppimista Azuressa.

Koulutuksen sisältö

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

Explore the Azure Machine Learning Workspace

• Explore Azure Machine Learning workspace resources and assets
• Explore developer tools for workspace interaction

Make data available in Azure Machine Learning

• Create Datastores
• Create Data Assets

Work with compute in Azure Machine Learning

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

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

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

Use notebooks for experimentation in Azure Machine Learning

• Track model training in notebooks with MLflow

Train models with scripts in Azure Machine Learning

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

Optimize model training in Azure Machine Learning

• Run pipelines in Azure Machine Learning

Manage and review models in Azure Machine Learning

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

Deploy and consume models with Azure Machine Learning

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

Design a machine learning operations (MLOps) solution

• Understand MLOps
• Design an MLOps solution