DP-100: DESIGNING AND IMPLEMENTING A DATA SCIENCE SOLUTION ON AZURE Training in College Station
We offer private customized training for groups of 3 or more attendees.
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Course Description |
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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 in Microsoft Azure.
Course Length: 3 Days
Course Tuition: $1690 (US) |
Prerequisites |
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Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques. Specifically: Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first. |
Course Outline |
DescriptionModule 1: Getting Started with Azure Machine LearningIn 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. Lessons
Lab : Create an Azure Machine Learning WorkspaceAfter completing this module, you will be able to
Module 2: Visual Tools for Machine LearningThis 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. Lessons
Lab : Use Automated Machine LearningLab : Use Azure Machine Learning DesignerAfter completing this module, you will be able to
Module 3: Running Experiments and Training ModelsIn this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models. Lessons
Lab : Train ModelsLab : Run ExperimentsAfter completing this module, you will be able to
Module 4: Working with DataData is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments. Lessons
Lab : Work with DataAfter completing this module, you will be able to
Module 5: Working with ComputeOne 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. Lessons
Lab : Work with ComputeAfter completing this module, you will be able to
Module 6: Orchestrating Operations with PipelinesNow 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. Lessons
Lab : Create a PipelineAfter completing this module, you will be able to
Module 7: Deploying and Consuming ModelsModels 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. Lessons
Lab : Create a Real-time Inferencing ServiceLab : Create a Batch Inferencing ServiceAfter completing this module, you will be able to
Module 8: Training Optimal ModelsBy 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. Lessons
Lab : Use Automated Machine Learning from the SDKLab : Tune HyperparametersAfter completing this module, you will be able to
Module 9: Responsible Machine LearningData 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. Lessons
Lab : Explore Differential provacyLab : Interpret ModelsLab : Detect and Mitigate UnfairnessAfter completing this module, you will be able to
Module 10: Monitoring ModelsAfter 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. Lessons
Lab : Monitor Data DriftLab : Monitor a Model with Application InsightsAfter completing this module, you will be able to
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