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On this accelerated Designing and Implementing a Data Science Solution on Azure course, you will 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. This course teaches you how to create end-to-end solutions in Microsoft Azure where you will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning.
You will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. This certification is an opportunity to prove knowledge and expertise 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.
By the end of this program, you will be ready to take the DP-100: Designing and Implementing a Data Science Solution on Azure.
At the end of this course, you’ll achieve your Designing and Implementing a Data Science Solution on Azure certification.
Through Firebrand’s Lecture | Lab | Review methodology you’ll certify at twice the speed of traditional training and get access to courseware, learn from certified instructors, and train in a distraction-free environment.
40% faster
Distraction-free environment
This course is ideal 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.
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.
After completing this module, you will be able to:
This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.
After completing this module, you will be able to;
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.
After completing this module, you will be able to:
Data 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.
After completing this module, you will be able to:
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.
After completing this module, you will be able to:
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.
After completing this module, you will be able to:
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.
After completing this module, you will be able to:
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.
After completing this module, you will be able to:
Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behaviour. This module describes how you can interpret models to explain how feature importance determines their predictions.
After completing this module, you will be able to:
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.
After completing this module, you will be able to:
At the end of this accelerated course, you’ll sit the following exam at the Firebrand Training centre, covered by your Certification Guarantee:
Before attending this accelerated course, you should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Microsoft Azure. You should also have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.
Before attending this course, you should have:
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Ende |
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27.11.2023 (Montag) |
28.11.2023 (Dienstag) |
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1.4.2024 (Montag) |
2.4.2024 (Dienstag) |
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13.5.2024 (Montag) |
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16.9.2024 (Montag) |
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