VTEC Training
Course Code: 19094
DP-100T01 Designing and Implementing a Data Science Solution on Azure
VTEC Training
Portland,
ME
Class Dates:
1/1/0001
Length:
4 Days
Cost:
$2380.00
Class Time:
Technology:
Database
Delivery:
Virtual Instructor-Led Training
Overview
- Course Overview
- The Azure Data Scientist applies their knowledge of data science and machine learning to implementing and running machine learning workloads on Microsoft Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.
- Audience
- 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
- Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.
Specifically:
Creating cloud resources in Microsoft Azure.
Using Python to explore and visualize data.
Training and validating machine learning models using common frameworks like Scikit-Learn,
PyTorch, and TensorFlow.
Working with containers
Course Details
- Module 1: Getting Started with Azure Machine Learning
- Introduction to Azure Machine Learning
- Working with Azure Machine Learning
- Lab : Create an Azure Machine Learning Workspace
- Module 2: Visual Tools for Machine Learning
- Automated Machine Learning
- Azure Machine Learning Designer
- Lab : Use Automated Machine Learning
- Lab : Use Azure Machine Learning Designer
- Module 3: Running Experiments and Training Models
- Introduction to Experiments
- Training and Registering Models
- Lab : Run Experiments
- Lab : Train Models
- Module 4: Working with Data
- Working with Datastores
- Working with Datasets
- Lab : Work with Data
- Module 5: Working with Compute
- Working with Environments
- Working with Compute Targets
- Lab : Work with Compute
- Module 6: Orchestrating Operations with Pipelines
- Introduction to Pipelines
- Publishing and Running Pipelines
- Lab : Create a Pipeline
- Module 7: Deploying and Consuming Models
- Real-time Inferencing
- Batch Inferencing
- Continuous Integration and Delivery
- Lab : Create a Real-time Inferencing Service
- Lab : Create a Batch Inferencing Service
- Module 8: Training Optimal Models
- Hyperparameter Tuning
- Automated Machine Learning
- Lab : Tune Hyperparameters
- Lab : Use Automated Machine Learning from the SDK
- Module 9: Responsible Machine Learning
- Differential Privacy
- Model Interpretability
- Fairness
- Lab : Explore Differential provacy
- Lab : Interpret Models
- Lab : Detect and Mitigate Unfairness
- Module 10: Monitoring Models
- Monitoring Models with Application Insights
- Monitoring Data Drift
- Lab : Monitor a Model with Application Insights
- Lab : Monitor Data Drift