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Course Description
Designing and Implementing a Data Science Solution on Azure (DP-100) Certification training is a popular Microsoft certification training. This DP-100 training course is ideal for professionals who are data scientists with proper understanding of programming languages such as python and machine learning frameworks such as Tensorflow, pytorch, and more. Checkout dates below to find the suitable schedule for you to enrol and get certified as an Azure Data Scientist Associate.
Objectives
Participants who take part in Designing and Implementing a Data Solution on Azure DP-100 training will learn about:
- Using Azure services to develop machine learning solutions
- Performing data science activities on Azure
- Understanding of automate machine learning with Azure machine learning
- Managing and Monitoring machine learning models with Azure machine learning
Audience
Job roles that can take up Designing and Implementing a Data Solution on Azure DP-100 training include, but are not limited to:
- Data Scientists
- Machine Learning professionals
- Professionals who create data solutions for Microsoft Azure
- Anybody who wants to understand Implementing an Azure Data Solution
- Professionals who want to clear Designing and Im
- plementing a Data Solution on Azure DP-100 examination
Prerequisites
- Candidates who wish to take up the Designing and Implementing a Data Solution on Azure DP-100 certification exam should have a fundamental knowledge of Microsoft Azure.
- A participant should also be able to write in programming languages such as Python to work with data using various libraries.
- Basic understanding of data science that include preparing data and train machine learning models using machine learning libraries.
Content
Module 1: Introduction to Azure Machine Learning
- Getting Started with Azure Machine Learning
- Azure Machine Learning Tools
- Lab : Creating an Azure Machine Learning Workspace
- Lab : Working with Azure Machine Learning Tools
Module 2: No-Code Machine Learning with Designer
- Training Models with Designer
- Publishing Models with Designer
- Lab : Creating a Training Pipeline with the Azure ML Designer
- Lab : Deploying a Service with the Azure ML Designer
Module 3: Running Experiments and Training Models
- Introduction to Experiments
- Training and Registering Models
- Lab : Running Experiments
- Lab : Training and Registering Models
Module 4: Working with Data
- Working with Datastores
- Working with Datasets
- Lab : Working with Datastores
- Lab : Working with Datasets
- Create and consume datastores
- Create and consume datasets
Module 5: Compute Contexts
- Working with Environments
- Working with Compute Targets
- Lab : Working with Environments
- Lab : Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
- Introduction to Pipelines
- Publishing and Running Pipelines
- Lab : Creating a Pipeline
- Lab : Publishing a Pipeline
Module 7: Deploying and Consuming Models
- Real-time Inferencing
- Batch Inferencing
- Lab : Creating a Real-time Inferencing Service
- Lab : Creating a Batch Inferencing Service
- After completing this module, you will be able to
- Publish a model as a real-time inference service
- Publish a model as a batch inference service
Module 8: Training Optimal Models
- Hyperparameter Tuning
- Automated Machine Learning
- Lab : Tuning Hyperparameters
- Lab : Using Automated Machine Learning
Module 9: Interpreting Models
- Introduction to Model Interpretation
- using Model Explainers
- Lab : Reviewing Automated Machine Learning Explanations
- Lab : Interpreting Models
Module 10: Monitoring Models
- Monitoring Models with Application Insights
- Monitoring Data Drift
- Lab : Monitoring a Model with Application Insights
- Lab : Monitoring Data Drift
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