Coursedot’s portfolio of the full Microsoft Azure courses features the DP-100: Designing and Implementing a Data Science Solution on Azure. We can also offer you over 20 certified instructors globally to deliver the training needed for gaining this certification.
Available in: Portugal, United Kingdom, Spain, Czech Republic, rest of EU, United Kingdom, USA, India, Canada, Russia (+many more on request).
Available languages: English, Spanish, Hindi, Russian (+many more on request).
Available trainers, certified to deliver the course: 8 in Europe, 6 in Asia, 5 in North America, 2 in South America . Total: 20+ globally
Delivery methods: Online or instructor-led (virtual and in-class)
Official course: Yes
Certification exam: Offered separately.
Coursedot can help organize and deliver this course along with a suitable, certified IT instructor. Get in touch with us via firstname.lastname@example.org or write to us via the form for more details and an offer.
More information about the course
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 is designed 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.
The course features 10 modules in total. Some of them are:
Module 1: Introduction to 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.
Module 2: No-Code Machine Learning with Designer
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.
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.
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 datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
Note: Training providers can offer a different schedule or description.
More information about this course.
About the exam
The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.
More information about this exam.
Coursedot can help enterprise clients organize and deliver this course along with a suitable, certified IT instructor. Get in touch with us via email@example.com or write to us via the form for more details and an offer.
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