Designing and Implementing a Data Science Solution on Azure (DP-100T01-A)
Learnfast is a Microsoft Silver Learning Partner. This is an authorised Microsoft Official Course (MOC), a preparation course for the DP-100: Designing and Implementing a Data Science Solution on Azure exam.
Pricing & Payment Options
Duration
Course Prerequisites
Delivery Methods
Module 1: Getting Started with Azure Machine Learning
In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
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Introduction to Azure Machine Learning
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Working with Azure Machine Learning
Module 2: No-Code Machine Learning
This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.
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Automated Machine Learning
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Azure Machine Learning Designer
Module 3: Running Experiments and Training Models
In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
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Introduction to Experiments
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Training and Registering Models
Module 4: Working with Data
Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage data stores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
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Working with Datastores
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Working with Datasets
Module 5: Working with Compute
One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
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Working with Environments
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Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.
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Introduction to Pipelines
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Publishing and Running Pipelines
Module 7: Deploying and Consuming Models
Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
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Real-time Inferencing
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Batch Inferencing
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Continuous Integration and Delivery
Module 8: Training Optimal Models
By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
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Hyperparameter Tuning
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Automated Machine Learning
Module 9: Responsible Machine Learning
Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.
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Differential Privacy
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Model Interpretability
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Fairness
Module 10: Monitoring Models
After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
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Monitoring Models with Application Insights
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Monitoring Data Drift