Designing and Implementing a Data Science Solution on Azure
e-Attendance
Please follow the steps to complete your e-attendance
- Click this link
- Get your Student ID
- Class ID:
- User Guide
Prerequisite
- Course Pre-requisite
- Better if you have good knowledge on Azure Services (AZ-900)
- This course assume that you have some understanding on Machine Learning Process
- Get your e-books
- Go to Skillpipe
- Create an account
- Redeem your License Code (Click for Code) and get your e-copy of the DP-100 Student Book
- Signup with Azure Portal
- Go to Azure Pass
- Click Start and Sign-in with Microsoft Account. Details guide is here.
- Enter your Promo Code (Click for Code)
Course Outline
Module 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.
Lessons
- 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
Module 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.
Lessons
- 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
Module 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.
Lessons
- 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
Module 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.
Lessons
- 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
Module 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.
Lessons
- 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
Module 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.
Lessons
- 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
Module 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.
Lessons
- 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
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.
Lessons
- 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
Module 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.
Lessons
- 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
Module 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.
Lessons
- 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
Azure Services
- Explore Azure Services here
- Deep dive through Azure Fundamentals aka.ms/HN/learnaz
Azure ML Learning Path
Azure Machine Learning
A set of services for training, testing and deploying your own Machine Learning models.
Machine Learning Services
What is it?
- Simplify and accelerate the building, training, and deployment of your machine learning models. Use automated machine learning to identify suitable algorithms and tune hyperparameters faster. Improve productivity and reduce costs with autoscaling compute and DevOps for machine learning. Seamlessly deploy to the cloud and the edge with one click. Access all these capabilities from your favorite Python environment using the latest open-source frameworks, such as PyTorch, TensorFlow, and scikit-learn.
Who is it for?
- Data Scientists, Machine Learning experts (code-first, Python-focused)
Learning Resources
- 📃 Landing page
- 📺 AI with Azure Machine Learning services: Simplifying the data science process – BRK2304
- 💡 Bootcamp Materials
Machine Learning Studio
What is it?
- A fully-managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions. Machine Learning Studio is a powerfully simple browser-based, visual drag-and-drop authoring environment where no coding is necessary. Go from idea to deployment in a matter of clicks.
Who is it for?
- Data Scientists, Machine Learning experts, Developers (Low/No-Code)
Learning Resources
- 📃 Landing page
- 📺 Azure Machine Learning Demo |70-774 Perform Cloud Data Science with Azure Machine Learning Tutorial
Azure Databricks
What is it?
- Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
- Set up your Spark environment in minutes and autoscale quickly and easily. Data scientists, data engineers, and business analysts can collaborate on shared projects in an interactive workspace. Apply your existing skills with support for Python, Scala, R, and SQL, as well as deep learning frameworks and libraries like TensorFlow, Pytorch, and Scikit-learn. Native integration with Azure Active Directory (Azure AD) and other Azure services enables you to build your modern data warehouse and machine learning and real-time analytics solutions.
Who is it for?
- Apache Spark users, Data Scientists, Machine Learning experts
Learning Resources
- 📃 Landing Page
- 📃 Quickstart Guide
- 📺 Microsoft Azure Databricks – Azure Power Lunch
- 📺 Real-time analytics with Azure Databricks and Azure Event Hubs – BRK3203
Taxonomy
A taxonomy of the workspace is illustrated in the following diagram:
Presentation Files
Lab Files
You can find all Lab Files and Instructions here.
Download as ZIP.
Synopsis of Lab:
Module 1: Introduction to Azure Machine Learning
- Lab 1A: Creating an Azure Machine Learning Workspace
- Lab 1B: Working with Azure Machine Learning Tools
Module 2: “No-code” Machine Learning with Designer
- Lab 2A: Creating a Training Pipeline with the Azure ML Designer
- Lab 2B: Deploying a Service with the Azure ML Designer
Module 3: Running Experiments and Training Models
Module 4: Working with Data
Module 5: Compute Contexts
Module 6: Orchestrating Operations with Pipelines
Module 7: Deploying and Consuming Models
Module 8: Training Optimal Models
Module 9: Interpreting Models
Module 10: Monitoring Models
Important: Remember to stop any virtual machines used in these labs when you no longer need them – this will minimize the Azure credit incurred for these services. When you have completed all of the labs, consider deleting the resource group you created if you don’t plan to experiment with it any further.
Exam Preparation
Ignite session: preparation for Microsoft Azure Data Scientist (DP-100)
-
- Duration: 45 Minutes.
- Speaker: Glenn Morris.
- Slide Download: DP-100 | Designing and Implementing a Data Science Solution on Azure Training.
Extra Resources
Dataset
Mindmap
- Click here
Checklist
Book
Documentation
AML Cheat Sheet
Data Concept
- To know more about Data Concept you can click [this] link.
ML Performance Metrics:
Hyperparameter Tuning
- Solving different Optimization Problem
Interpretable Machine Learning
- Interpreting Models
- You can use this notebook file where you can use your local model to explanation.
- For more clarity this video can help you.
Azure Machine Learning Notebooks
- Different Notebooks are available for Different Services
Summary
- Summary – Azure Machine Learning Service
- There is a web series from Facundo Santiago [Part 1], [Part 2] and [Part 3]