Designing and Implementing a Data Science Solution on Azure

kurz

Základní info

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

Audience Profile

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Course Syllabus

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
Design a machine learning operations solution

Explore and configure 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
Work with compute targets in Azure Machine Learning
Work with environments in Azure Machine Learning

Experiment with Azure Machine Learning

Find the best classification model with Automated Machine Learning
Track model training in Jupyter notebooks with MLflow

Optimize model training with 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
Run pipelines in Azure Machine Learning

Manage and review models in Azure Machine Learning

Register an MLflow model in Azure Machine Learning
Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

Deploy and consume models with Azure Machine Learning

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

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