Microsoft Azure Data Scientist Associate (DP-100) Cert Prep
1h 43mIntermediate2025-06-18
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Course details
In this comprehensive course, MLOps expert Noah Gift helps you prepare for the Microsoft Azure Data Scientist Associate (DP-100) Certification exam. Learn how to design and prepare a machine learning solution as you create an Azure Machine Learning workspace. Explore data and run experiments through developing code with a compute instance, training a model with Python SDK, and much more. Step through the full process of training and deploying models. Plus, dive into techniques you can use to optimize language models for AI applications.
Skills covered
Cloud DevelopmentMachine LearningCloud AdministrationAzureCert PrepArtificial Intelligence (AI)Cloud ComputingMicrosoft
Concepts
0. Introduction
- 01 - Overview
- 02 - Prerequisite technology
Domain 1 - Design and Prepare a Machine Learning Solution
- 03 - Determine the appropriate compute specifications for a training workload
- 04 - Create an Azure Machine Learning workspace
- 05 - Manage a workspace by using developer tools for workspace interaction
- 06 - Create and manage data assets
- 07 - Create compute targets for experiments and training
- 08 - Monitor compute utilization
Domain 2 - Explore Data and Train Models
- 09 - Load and transform data
- 10 - Analyze data by using Azure Data Explorer
- 11 - Demo - Azure Data Explorer
- 12 - Consume data assets from the designer
- 13 - Use automated machine learning for tabular data
- 14 - Develop code by using a compute instance
- 15 - Consume data in a notebook
- 16 - Train a model by using Python SDK
- 17 - Techniques for dealing with hyperparameter optimization
Domain 3 - Prepare a Model for Deployment
- 18 - Configure compute for a job run
- 19 - Consume data from a data asset in a job
- 20 - Run a script as a job by using Azure Machine Learning
- 21 - Use MLflow to log metrics from a job run
- 22 - Describe MLflow model output
- 23 - Identify an appropriate framework to package a model
- 24 - Describe MLflow model workflow in Databricks
Domain 4 - Deploy and Retrain a Model
- 25 - Configure compute for a batch deployment
- 26 - Deploy a model to a batch endpoint
- 27 - Test a real-time deployed service
- 28 - Apply machine learning operations (MLOps) practices
- 29 - Trigger an Azure Machine Learning pipeline, including from Azure DevOps or GitHub
- 30 - Conclusion