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

DP-100T01: Designing and Implementing a Data Science Solution on Azure Training teaches the techniques to work with machine learning solutions at the cloud scale with the help of Azure Machine Learning. This technical course enhances and supports one’s current knowledge of machine learning and Python to manage data preparations and ingestions, model training, and launch machine learning solution inspection in Microsoft Azure. Our enterprise training program is best for organizations and companies. It teaches automation, management, and monitoring of machine learning using Azure Machine Learning service. This training program is ideal for data scientists with experience in machine learning frameworks and Python, who want to develop and work with machine learning solutions in the cloud. It is also very useful for individuals preparing for the Microsoft Certified: Azure Data Scientist Associate certification exam.

This training is designed based on the objectives of the course variant DP-100T01-A

• 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

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.

• 1. Introduction to Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

• Getting Started with Azure Machine Learning
• Azure Machine Learning Tools

Lab: Creating an Azure Machine Learning Workspace

Lab: Working with Azure Machine Learning Tools

After completing this module, you will be able to

• Provision an Azure Machine Learning workspace
• Use tools and code to work with Azure Machine Learning
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2. No-Code Machine Learning with Designer
This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.
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
After completing this module, you will be able to
Use designer to train a machine learning model
Deploy a Designer pipeline as a service
3. Running Experiments and Training Models
In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
Introduction to Experiments
Training and Registering Models
Lab: Running Experiments
Lab: Training and Registering Models
After completing this module, you will be able to
Run code-based experiments in an Azure Machine Learning workspace
Train and register machine learning models
4. Working with Data
Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
Working with Datastores
Working with Datasets
Lab: Working with Datastores
Lab: Working with Datasets
After completing this module, you will be able to
Create and consume datastores
Create and consume datasets
5. Compute Contexts
One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
Working with Environments
Working with Compute Targets
Lab: Working with Environments
Lab: Working with Compute Targets
After completing this module, you will be able to
Create and use environments
Create and use compute targets
6. Orchestrating Operations with Pipelines
Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.
Introduction to Pipelines
Publishing and Running Pipelines
Lab: Creating a Pipeline
Lab: Publishing a Pipeline
After completing this module, you will be able to
Create pipelines to automate machine learning workflows
Publish and run pipeline services
7. Deploying and Consuming Models
Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
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
8. Training Optimal Models
By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
Hyperparameter Tuning
Automated Machine Learning
Lab: Tuning Hyperparameters
Lab: Using Automated Machine Learning
After completing this module, you will be able to
Optimize hyperparameters for model training
Use automated machine learning to find the optimal model for your data
9. Interpreting Models
Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.
Introduction to Model Interpretation
using Model Explainers
Lab: Reviewing Automated Machine Learning Explanations
Lab: Interpreting Models
After completing this module, you will be able to
Generate model explanations with automated machine learning
Use explainers to interpret machine learning models
10. Monitoring Models
After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
Monitoring Models with Application Insights
Monitoring Data Drift
Lab: Monitoring a Model with Application Insights
Lab: Monitoring Data Drift
After completing this module, you will be able to
Use Application Insights to monitor a published model
Monitor data drift