Designing effective data science solutions requires more than just modelling—it demands strategic planning and robust pipelines. This course provides participants with the skills to create, deploy, and manage end-to-end machine learning workflows on Azure. Through labs and structured modules, they will master data preparation, experimentation, and operationalisation.
Learning Outcomes:
Design data ingestion and preparation workflows
Build and validate machine learning models
Operationalise models using Azure ML pipelines
Implement responsible AI practices in deployment
Key Topics:
Azure Machine Learning workspace and SDK
Model training, tuning, and evaluation
Data pipelines and automated ML
Model versioning and deployment endpoints
Monitoring and retraining strategies
Exam Details
This course is designed to build participants’ understanding of key concepts and domains covered in the DP-100: Designing and Implementing a Data Science Solution on Azure certification.
While the course provides technical training aligned with certification objectives, the certification exam is not bundled and must be registered separately.
To maximise success, participants are strongly encouraged to complement the course with additional self-study, revision of course materials, and dedicated practice before attempting the exam.
1. Design a data ingestion strategy for machine learning projects
Learn how to design a data ingestion solution for training data used in machine learning projects.
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2. Design a machine learning model training solution
Learn how to design a model training solution for machine learning projects.
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3. Design a model deployment solution
Learn how to design a model deployment solution and how the requirements of the deployed model can affect the way you train a model.
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4. Explore Azure Machine Learning workspace resources and assets
As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets.
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5. Explore developer tools for workspace interaction
Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).
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6. Make data available in Azure Machine Learning
Learn about how to connect to data from the Azure Machine Learning workspace. You'll be introduced to datastores and data assets.
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7. Work with compute targets in Azure Machine Learning
Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.
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8. Work with environments in Azure Machine Learning
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.
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9. Find the best classification model with Automated Machine Learning
Learn how to find the best classification model with automated machine learning (AutoML). You'll use the Python SDK (v2) to configure and run an AutoML job.
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10. Track model training in Jupyter notebooks with MLflow
Learn how to use MLflow for model tracking when experimenting in notebooks.
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11. Run a training script as a command job in Azure Machine Learning
Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.
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12. Track model training with MLflow in jobs
Learn how to track model training with MLflow in jobs when running scripts.
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13. Run pipelines in Azure Machine Learning
Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.
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14. Perform hyperparameter tuning with Azure Machine Learning
Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.
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15. Deploy a model to a managed online endpoint
Learn how to deploy models to a managed online endpoint for real-time inferencing.
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16. Deploy a model to a batch endpoint
Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you'll trigger a batch scoring job.
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