In the series of Azure Machine Learning posts:
- Dec 01: What is Azure Machine Learning?
- Dec 02: Creating Azure Machine Learning Workspace
- Dec 03: Understanding Azure Machine Learning Studio
- Dec 04: Getting data to Azure Machine Learning workspace
- Dec 05: Creating compute and cluster instances in Azure Machine Learning
- Dec 06: Environments in Azure Machine Learning
- Dec 07: Introduction to Azure CLI and Python SDK
- Dec 08: Python SDK namespaces for workspace, experiments and models
- Dec 09: Python SDK namespaces for environment, and pipelines
- Dec 10: Connecting to client using Python SDK namespaces
- Dec 11: Creating Pipelines with Python SDK
- Dec 12: Creating jobs
- Dec 13: Automated ML
- Dec 14: Registering the models
- Dec 15: Getting to know MLflow
- Dec 16: MLflow in action with xgboost
- Dec 17: Building responsible AI dashboard with Python SDK
- Dec 18: Statistical analysis, plotting graphs and feature engineering
- Dec 19: Statistical analysis and ML comparison of prediction models
- Dec 20: Handling kernels, python packages, YAML files in notebooks and keeping structure and good practices
- Dec 21: Using Azure Machine Learning terminal
- Dec 22: Batch endpoints for batch scoring
- Dec 23: Working with R
If you want to immerse in further reading and additional knowledge, here are some links. Here are just couple.
Microsoft Learn website:
- General information: https://learn.microsoft.com/en-us/azure/machine-learning/
- Python SDK v2: https://learn.microsoft.com/en-us/azure/machine-learning/concept-v2
- Python API: https://learn.microsoft.com/en-us/python/api/?view=azure-ml-py
- Auto ML https://learn.microsoft.com/en-us/samples/azure/azureml-examples/azure-machine-learning-automl-examples/
Github:
- Official Azure Github: https://github.com/azure/azureml-examples
- Code samples: https://github.com/Azure-Samples
- Quick start templates: https://github.com/Azure/azure-quickstart-templates
- ResponsibleAI toolbox: https://github.com/microsoft/responsible-ai-toolbox
- and many more.
There are great GitHub repositories with great end-to-end solutions.
Books:
- Master Azure Machine Learning (2nd ed.): https://www.amazon.com/Mastering-Azure-Machine-Learning-end/dp/1803232412
- Automated Machine Learning with Microsoft Azure: Build highly accurate and scalable end-to-end AI solutions with Azure AutoML: https://www.amazon.com/Automated-Machine-Learning-Microsoft-Azure/dp/1800565313
- Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions; https://www.amazon.com/Practical-Automated-Machine-Learning-Azure/dp/149205559X
Blogposts:
- PragmaticWorks: https://blog.pragmaticworks.com/topic/azure-machine-learning
- Bea Stollnitz: https://bea.stollnitz.com/blog/
- this series 🙂
Courses:
- By Pluralsight: https://www.pluralsight.com/search?q=Azure%20Machine%20Learning
- Pluralsight on DP100: https://www.pluralsight.com/paths/microsoft-exam-dp-100-designing-and-implementing-a-data-science-solution-on-azure
Certifications:
- DP 100
- AI 102, AI 900
Compete set of code, documents, notebooks, and all of the materials will be available at the Github repository: https://github.com/tomaztk/Azure-Machine-Learning
The series ends with this blogpost 🙂 Wish you all a merry Christmas and a happy new year 2023.
TomazTsql 
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