Designing and Implementing a Data Science Solution on Azure

Course Description
In this course you will learn how to apply data science and machine learning to implement and run machine learning workloads on Azure. Also, planning and creating a suitable working environment for data science workloads on Azure. You will run data experiments and train predictive models. In addition, you will manage, optimize, and deploy machine learning models into production. You will get knowledge and experience in data science and using Azure Machine Learning, Deep Learning, Big Data. After finishing this course and passing your exam you will become a Data Scientist.

Watch this video presentation from the pilot phase of the program:

• 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.
• 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.
• Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.
• experiments that encapsulate data processing and model training code, and use them to train machine learning models.
• how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
• how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
• how to define and run pipelines in the module.
• how to deploy models for real-time inferencing, and for batch inferencing.
• 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.
• some considerations and techniques for applying responsible machine learning principles.

Who is this course for?
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.

Prerequisites:
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.

• 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.

• Knowledge of Microsoft Azure and ability to navigate the Azure portal (or course Azure fundamentals AZ900)
• Knowledge of either C# or Python
• Familiarity with JSON and REST programming semantics