DP-100: Designing and Implementing a Data Science Solution on Azure Course Outline
Course Overview
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.
*** NOTE: This class also available in On-Demand, eLearning Format ***
Too busy to attend this class during the work day? Consider the Microsoft On-Demand, self-study version of this course. Watch expert lectures and demonstrations, work with hands-on labs, and study at your own pace. For details on this economical alternative format see
Microsoft On-Demand: Designing and Implementing a Data Science Solution on Azure (DP-100)
Audience profile
This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.
Prerequisites
Before attending this course, students must have:
• Azure Fundamentals Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
• Python programming ability and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.
Interested in Getting Certification? Check out our Exam Pak option!
Prepare for Microsoft Certification by getting our Exam Pak -- a $500 value you can get for free! Optional Exam Pak includes:
• 24/7 Online Support
Need assistance while you are learning? Chat with our 24/7 online support specialists. And, with your permission, the expert can even take over your computer to provide assistance. (90-day access.)
• Microsoft Exam Reference Guide
When you are ready for certification, begin your preparation with the Exam Reference Guide from Microsoft Press. We provide you with a copy of this book that focuses on the critical skills and knowledge measured on the Microsoft Certification exam.
• Practice Exam Software
You may study at your own pace with this web-based practice exam. Exam-like questions are designed to help you prepare for your certification exam by validating your knowledge and reinforcing key concepts.
• Exam Voucher with Exam-Pass Guarantee
Prepare for your exam using the practice software. Once you have achieved an 85% or above score, contact us and we will provide you with an exam voucher. Didn't pass the first time? Not a problem--you will get a second voucher with our Exam-Pass Guarantee - For details visit
Exam Pass Guarantee
For full details on this promotional offer visit
Free Microsoft Exam Pak
**Call our office to order the Exam Pak when registering for this class. Exam Paks are only available with this promotion when you register for your class. This promotion is valid on new registrations only and cannot be combined with other offers.**
Course Outline
1 - Design a data ingestion strategy for machine learning projects
Identify your data source and format
Choose how to serve data to machine learning workflows
Design a data ingestion solution
2 - Design a machine learning model training solution
Identify machine learning tasks
Choose a service to train a machine learning model
Decide between compute options
3 - Design a model deployment solution
Understand how model will be consumed
Decide on real-time or batch deployment
4 - Design a machine learning operations solution
Explore an MLOps architecture
Design for monitoring
Design for retraining
5 - Explore Azure Machine Learning workspace resources and assets
Create an Azure Machine Learning workspace
Identify Azure Machine Learning resources
Identify Azure Machine Learning assets
Train models in the workspace
6 - Explore developer tools for workspace interaction
Explore the studio
Explore the Python SDK
Explore the CLI
7 - Make data available in Azure Machine Learning
Understand URIs
Create a datastore
Create a data asset
8 - Work with compute targets in Azure Machine Learning
Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster
9 - Work with environments in Azure Machine Learning
Understand environments
Explore and use curated environments
Create and use custom environments
10 - Find the best classification model with Automated Machine Learning
Preprocess data and configure featurization
Run an Automated Machine Learning experiment
Evaluate and compare models
11 - Track model training in Jupyter notebooks with MLflow
Configure MLflow for model tracking in notebooks
Train and track models in notebooks
12 - Run a training script as a command job in Azure Machine Learning
Convert a notebook to a script
Run a script as a command job
Use parameters in a command job
13 - Track model training with MLflow in jobs
Track metrics with MLflow
View metrics and evaluate models
14 - Perform hyperparameter tuning with Azure Machine Learning
Define a search space
Configure a sampling method
Configure early termination
Use a sweep job for hyperparameter tuning
15 - Run pipelines in Azure Machine Learning
Create components
Create a pipeline
Run a pipeline job
16 - Register an MLflow model in Azure Machine Learning
Log models with MLflow
Understand the MLflow model format
Register an MLflow model
17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
Understand Responsible AI
Create the Responsible AI dashboard
Evaluate the Responsible AI dashboard
18 - Deploy a model to a managed online endpoint
Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints
19 - Deploy a model to a batch endpoint
Understand and create batch endpoints
Deploy your MLflow model to a batch endpoint
Deploy a custom model to a batch endpoint
Invoke and troubleshoot batch endpoints
View outline in Word
ADP100
Facebook
LinkedIn