DP-100 – Designing and Implementing a Data Science Solution on Azure

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 with Azure Machine Learning and MLflow.

What you’ll learn

Know how to implement solutions for the Microsoft Azure ML platform
Pass the Microsoft DP-100 Microsoft Azure Machine Learning test the first time
Achieve the Azure Data Scientist Associate badge
Be up-to-date on the latest updates to this ever-changing platform

Course Content:

Module1: Design a data ingestion strategy for machine learning Projects

Introduction
Identify your data source and format
Choose how to serve data to machine learning workflows5 min
Design a data ingestion solution7 min
Exercise: Design a data ingestion strategy

Module2:Design a machine learning model training solution

Introduction
Identify machine learning tasks
Choose a service to train a machine learning mode
Decide between compute options
Exercise: Design a model training strategy

Module3: Design a model deployment solution

Introduction
Understand how model will be consumed
Decide on real-time or batch deploymen
Exercise – Design a deployment solution

Module4:Explore Azure Machine Learning workspace resources and assets

Introduction
Create an Azure Machine Learning workspace
Identify Azure Machine Learning resource
Identify Azure Machine Learning assets
Train models in the workspace
Exercise – Explore the workspace

Module5:Explore Developer tools for workspace interaction

Introduction
Explore the studio
Explore the Python SDK
Explore the CLI
Exercise-Explore the developer tools
Knowledge check

Module6:Make data available in Azure Machine Learning

Introduction
Understand URIs
Create a datastore
Create a data asset
Exercise – Make data available

Module7:Work with compute targets in Azure Machine Learning

Introduction
Create and use a compute instance
Create and use a compute instance
Create and use a compute cluster
Exercise – Work with compute resources

Module8:Find the best classification model with Automated Machine Learning

Introduction
Preprocess data and configure featurization
Run an Automated Machine Learning experiment
Evaluate and compare models
Exercise – Find the best classification model

Who This Course is 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.

Free Exam Practice Test:

Designing and Implementing a Data Science Solution on Azure Exam Dumps

International Student Fee (online class) 500 USD

Job Interview Preparation (Soft Skills Questions & Answers)