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DP-100T01: Designing and Implementing a Data Science Solution on Azure Training


What DP-100T01: Designing and Implementing a Data Science Solution on Azure training is all about?

The DP-100T01Data Science Solution on Azure Training helps students learn how to operate machine learning solutions in the cloud by leveraging Azure Machine Learning. In this training, instructors prepare students to utilize their skills and knowledge in machine learning and Python to manage data preparation and ingesting, machine learning solution monitoring, and model deployment and training in Azure.

This training is based on the DP-100T01-A training variant.

  • START DATE
  • CLASS TIMINGS
  • DELIVERY MODE
  • PRICE
  • ACTION
  • 08/31/2020
  • 9:00 am - 5:00 pm EST
  • $ 1725
  • 11/02/2020
  • 9:00 am - 5:00 pm EST
  • $ 1725
  • 12/02/2020
  • 9:00 am - 5:00 pm EST
  • $ 1725
What are the course objectives for DP-100T01: Designing and Implementing a Data Science Solution on Azure training?
  • Doing Data Science on Azure
  • Doing Data Science with Azure Machine Learning service
  • Automating Machine Learning with Azure Machine Learning service
  • Managing and Monitoring Machine Learning Models with the Azure Machine Learning service
Who should attend DP-100T01: Designing and Implementing a Data Science Solution on Azure training?

Data scientists who know Python and existing understanding of machine learning frameworks such as TensorFlow, PyTorch, and Scikit-Learn who want to create and manage machine learning solutions in the cloud are the primary audiences for this training.

What is the course outline for DP-100T01: Designing and Implementing a Data Science Solution on Azure training?
  • 1. Introduction to Azure Machine Learning
  • a). Getting Started with Azure Machine Learning
  • b). Azure Machine Learning Tools
  • c). Lab : Creating an Azure Machine Learning Workspace
  • d). Lab : Working with Azure Machine Learning Tools
  • 2. No-Code Machine Learning with Designer
  • a). Training Models with Designer
  • b). Publishing Models with Designer
  • c). Lab : Creating a Training Pipeline with the Azure ML Designer
  • d). Lab : Deploying a Service with the Azure ML Designer
  • 3. Running Experiments and Training Models
  • a). Introduction to Experiments
  • b). Training and Registering Models
  • c). Lab : Running Experiments
  • d). Lab : Training and Registering Models
  • 4. Working with Data
  • a). Working with Datastores
  • b). Working with Datasets
  • c). Lab : Working with Datastores
  • d). Lab : Working with Datasets
  • 5. Compute Contexts
  • a). Working with Environments
  • b). Working with Compute Targets
  • c). Lab : Working with Environments
  • d). Lab : Working with Compute Targets
  • 6. Orchestrating Operations with Pipelines
  • a). Introduction to Pipelines
  • b). Publishing and Running Pipelines
  • c). Lab : Creating a Pipeline
  • d). Lab : Publishing a Pipeline
  • 7. Deploying and Consuming Models
  • a). Real-time Inferencing
  • b). Batch Inferencing
  • c). Lab : Creating a Real-time Inferencing Service
  • d). Lab : Creating a Batch Inferencing Service
  • 8. Training Optimal Models
  • a). Hyperparameter Tuning
  • b). Automated Machine Learning
  • c). Lab : Tuning Hyperparameters
  • d). Lab : Using Automated Machine Learning
  • 9. Interpreting Models
  • a). Introduction to Model Interpretation
  • b). using Model Explainers
  • c). Lab : Reviewing Automated Machine Learning Explanations
  • d). Lab : Interpreting Models
  • 10. Monitoring Models
  • a). Monitoring Models with Application Insights
  • b). Monitoring Data Drift
  • c). Lab : Monitoring a Model with Application Insights
  • d). Lab : Monitoring Data Drift