• Tricia Palanca

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

Updated: Sep 6, 2020

Audience Profile

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.


Before attending this course, students must have:

• Fundamental knowledge of Microsoft Azure

• Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.

• Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

Course Outline

Module 1: Introduction to Azure Machine Learning


• Getting Started with Azure Machine Learning

• Azure Machine Learning Tools

Module 2: No-Code Machine Learning with Designer


• Training Models with Designer

• Publishing Models with Designer

Module 3: Running Experiments and Training Models


• Introduction to Experiments

• Training and Registering Models

Module 4: Working with Data


• Working with Datastores

• Working with Datasets

Module 5: Compute Contexts


• Working with Environments

• Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines


• Introduction to Pipelines

• Publishing and Running Pipelines

Module 7: Deploying and Consuming Models


• Real-time Inferencing

• Batch Inferencing

Module 8: Training Optimal Models


• Hyperparameter Tuning

• Automated Machine Learning

Module 9: Interpreting Models


• Introduction to Model Interpretation

• using Model Explainers

Module 10: Monitoring Models


• Monitoring Models with Application Insights

• Monitoring Data Drift

Qualitia specializes in Microsoft Azure, Data Science, Machine Learning and AI. We can help you in your preparation for Exam DP-100 | Designing and Implementing a Data Science Solution on Azure.

Book now!

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