Designing and Implementing Data Science with Azure in Karachi, Lahore, Islamabad, Pakistan, Karachi

Designing and Implementing Data Science with Azure

The Azure Data Scientist applies their knowledge of data science and machine learning to implementing and running 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.

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. Data Scientist is a trending job title right now in the IT market. More and more organizations are seeking experienced candidates for big and small organizations. This implies planning and creating a suitable working environment for data science workloads on Azure, running data experiments, and training predictive ML models. A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.

Skills measured

The content of this exam was updated on May 22, 2020. Please download the exam skills outline below to see what changed.
Set up an Azure Machine Learning workspace
Run experiments and train models
Optimize and manage models
Deploy and consume models

Course Outline

Create an Azure Machine Learning workspace

create an Azure Machine Learning workspace (Microsoft Documentation: Azure Machine Learning workspaces)
configure workspace settings (Microsoft Documentation: Settings: az security workspace)
manage a workspace by using Azure Machine Learning studio (Microsoft Documentation: Azure Machine Learning Studio (classic) workspace)

Manage data in an Azure Machine Learning workspace

select Azure storage resources
register and maintain datastores (Microsoft Documentation: Connect to storage services on Azure)
create and manage datasets (Microsoft Documentation: Create Azure Machine Learning datasets)

Manage compute for experiments in Azure Machine Learning

determine the appropriate compute specifications for a training workload (Microsoft Documentation: training runs)
create compute targets for experiments and training (Microsoft Documentation: Configuring training runs)
configure Attached Compute resources including Azure Databricks (Microsoft Documentation: Set up compute targets for model training and deployment)
monitor compute utilization(Microsoft Documentation: Monitor Azure Machine Learning)

Implement security and access control in Azure Machine Learning

determine access requirements and map requirements to built-in roles (Microsoft Documentation: Azure built-in roles, Manage access to an Azure Machine Learning workspace)
create custom roles (Microsoft Documentation: Azure custom roles)
manage role membership
manage credentials by using Azure Key Vault (Microsoft Documentation: Store credential in Azure Key Vault)

Set up an Azure Machine Learning development environment

create compute instances (Microsoft Documentation: Overview of Azure Machine Learning compute instance)
share compute instances (Microsoft Documentation: Create and manage an Azure Machine Learning compute instance)
access Azure Machine Learning workspaces from other development environments (Microsoft Documentation: Set up a Python development environment for Azure Machine Learning)

Set up an Azure Databricks workspace

create an Azure Databricks workspace (Microsoft Documentation: Run a Spark job on Azure Databricks Workspace using the Azure portal)
create an Azure Databricks cluster (Microsoft Documentation: Clusters, Create a cluster)
create and run notebooks in Azure Databricks (Microsoft Documentation: Manage notebooks, Run a Spark job on Azure Databricks Workspace using the Azure portal)
link and Azure Databricks workspace to an Azure Machine Learning workspace

Who this course is for:

The students or professionals who have successfully completed the training of Home / Microsoft / Designing and Implementing a Data Science Solution on Azure (DP-100T01) Designing and Implementing a Data Science Solution on Azure.
The students or professionals who have a fundamental knowledge of Microsoft Azure.
The 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.

Job Interview Preparation (Soft Skills Questions & Answers)