New Design and implementation of data science solutions in a Microsoft Azure environment

The course aims to demonstrate how to deploy cloud-scale machine learning solutions using Azure Machine Learning. This course will teach you how to leverage your knowledge of Python and machine learning to manage data ingestion and preparation, model building and deployment, and monitoring of machine learning solutions using Azure Machine Learning and MLflow.

This course is designed for data scientists who already have some knowledge of Python and machine learning frameworks such as Scikit-Learn, PyTorch and Tensorflow, and who want to build and deploy machine learning solutions in the cloud.

Job role: Data Scientist

• Designing a data loading strategy for machine learning projects
• Designing a training solution for a machine learning model
• Designing a model deployment solution
• Exploring the resources and tools of the Azure Machine Learning workspace
• Exploring developer tools for workspace interaction
• Making data available in Azure Machine Learning
• Using computational goals in Azure Machine Learning
• Using environments in Azure Machine Learning
• Finding the best classification model with automated machine learning
• Tracking model training in Jupyter notebooks using MLflow
• Running a training script as a command task in Azure Machine Learning
• Tracking model training in tasks with MLflow
• Running processes in Azure Machine Learning
• Fine-tune hyperparameters using Azure Machine Learning
• Deploying the model to a supervised online endpoint
• Deploying a model on a batch endpoint

The successful Azure Data Scientists will start this role with a core understanding of cloud computing concepts, as well as experience in general data science and machine learning tools and techniques.

Specifically:

• Building cloud resources in Microsoft Azure.
• Using Python to discover and visualize data.
• Teaching and validating machine learning models using general frameworks such as Scikit-Learn, PyTorch and TensorFlow.