- Manage Azure resources for machine learning (25-30%)
- Run experiments and train models (20-25%)
- Deploy and operationalize machine learning solutions (35-40%)
- Implement responsible machine learning (5-10%)
Manage Azure machine learning resources, conduct experiments and model training, and deploy and operationalize ethical machine learning solutions.
How to organise and set up a working environment on Azure for data science workloads, as well as how to conduct data experiments and train predictive models.
You’ll learn how to design and manage enterprise-ready machine learning solutions using the Azure Machine Learning Python SDK.
You’ll learn how to perform data science workloads with Apache Spark and powerful clusters running on the Azure Databricks platform.
This study guide is intended to assist you in preparing for the Microsoft DP-100 Designing and Implementing a Data Science Solution on Azure test. These courses and hands-on labs will help you learn how to use Azure’s machine learning solutions even if you don’t plan to take the exam.
The Microsoft Certified: Azure Data Scientist Associate certification is awarded to candidates who pass the DP-100 exam.
- Manage Azure machine learning resources.
- Experiment and develop models.
- Machine learning systems should be deployed and operationalized.
- Machine learning should be used responsibly.
- Who is the target audience?
- Those interested in working as Azure data scientists
- Prerequisites for those preparing for the Microsoft DP-100 exam
- Basic knowledge of Microsoft Azure
