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.
Prerequisites
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
Lessons
• Getting Started with Azure Machine Learning
• Azure Machine Learning Tools
Module 2: No-Code Machine Learning with Designer
Lessons
• Training Models with Designer
• Publishing Models with Designer
Module 3: Running Experiments and Training Models
Lessons
• Introduction to Experiments
• Training and Registering Models
Module 4: Working with Data
Lessons
• Working with Datastores
• Working with Datasets
Module 5: Compute Contexts
Lessons
• Working with Environments
• Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
Lessons
• Introduction to Pipelines
• Publishing and Running Pipelines
Module 7: Deploying and Consuming Models
Lessons
• Real-time Inferencing
• Batch Inferencing
Module 8: Training Optimal Models
Lessons
• Hyperparameter Tuning
• Automated Machine Learning
Module 9: Interpreting Models
Lessons
• Introduction to Model Interpretation
• using Model Explainers
Module 10: Monitoring Models
Lessons
• 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.
