This blog post covers Hands-On Labs that you must perform in order to learn Machine Learning, Data Science & clear the Azure Data Scientist Associate (DP-100) Certification.
This post helps you with your self-paced learning as well as your team learning. There are 17 Hands-On Labs in this course.
- Register For Azure Free Trial Account
- Explore the Azure Machine Learning Workspace
- Explore developer tools for Workspace interaction
- Make Data available in Azure Machine Learning
- Work with Compute resources in Azure Machine Learning
- Work with environments in Azure Machine Learning
- Train a model with the Azure Machine Learning Designer
- Find the best classification model with Automated Machine Learning
- Track model training in notebooks with MLflow
- Run a training script as a command job in Azure Machine Learning
- Use MLflow to track training jobs
- Perform Hyperparameter Tuning with a Sweep job
- Run pipelines in Azure Machine Learning
- Create and explore the Responsible AI dashboard
- Log and register models with MLflow
- Deploy a model to a batch endpoint
- Deploy a model to a managed online endpoint
Here’s a quick sneak-peak of how to start learning Data Science on Azure & to clear Azure Data Scientist Associate (DP-100) by doing Hands-on.
Check our blog to know in more detail about the Azure Data Scientist Associate (DP-100) Certification
Activity Guides:
1.) Register For Azure Free Trial Account
The first thing required to perform the labs of DP-200 Implementing An Azure Data Scientist Exam is to get a Trial Account of Microsoft Azure. (You get 200 USD FREE Credit from Microsoft to practice)
Microsoft Azure is one of the top choices for any organization due to its freedom to build, manage, and deploy applications. In this activity guide, we will look at how to register for the Microsoft Azure FREE Trial Account.
You can Check out our blog to know more about how to create a Free Azure account.
2) Explore the Azure Machine Learning Workspace
The workspace is the first resource to create for Azure Machine Learning, it provides a centralized place to work with all the assets like data, compute, model training code logged metrics, and trained models you create when you use Azure Machine Learning. The workspace keeps the history of all the training runs to pick the best model out of it.
3) Explore developer tools for Workspace interaction
A developer tasked with integrating their application with an AML workspace must explore tools for provisioning resources, submitting and monitoring jobs, managing data and models, automating workflows, and extending functionality.
Check out: Overview of Azure Machine Learning Service
4) Make Data available in Azure Machine Learning
As a data scientist working at a company that is new to machine learning, you have been tasked with making your data available in Azure Machine Learning (AML). This involves creating and configuring datastores within your AML workspace, which will enable you to store and manage your data in a secure and scalable manner.
5) Work with Compute resources in Azure Machine Learning
In this exercise, you’ll learn how to use cloud compute in Azure Machine Learning to run experiments and production code at scale.
6) Work with environments in Azure Machine Learning
To run notebooks and scripts, you must ensure that the required packages are installed. Environments allow you to specify the runtimes and Python packages that must be used by your compute to run your code.
In this exercise, you will learn about environments and how to use them when training machine learning models with Azure Machine Learning compute.
Read more: MLOps is based on DevOps principles and practices that increase the efficiency of workflows and improves the quality and consistency of the machine learning solutions.
7) Train a model with the Azure Machine Learning Designer
Azure Machine Learning Designer provides a drag and drop interface with which you can define a workflow. You can create a workflow to train a model, testing and comparing multiple algorithms with ease.
In this exercise, you’ll use the Designer to quickly train and compare two classification algorithms
8) Find the best classification model with Automated Machine Learning
AutoML allows you to try multiple preprocessing transformations and algorithms with your data to find the best machine learning model.
In this exercise, you’ll use automated machine learning to determine the optimal algorithm and preprocessing steps for a model by performing multiple training runs in parallel.
9) Track model training in notebooks with MLflow
To track your work and keep an overview of the models you train and how they perform, you can use MLflow tracking.
In this exercise, you’ll MLflow within a notebook running on a compute instance to log model training.
10) Run a training script as a command job in Azure Machine Learning
A notebook is ideal for experimentation and development. Once you’ve developed a machine learning model and it’s ready for production, you’ll want to train it with a script. You can run a script as a command job.
In this exercise, you’ll test a script and then run it as a command job.
Read more about Datastores and datasets in our blog at Working With Azure Datastores and Datasets.
11) Use MLflow to track training jobs
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. MLflow Tracking is a component that logs and tracks your training job metrics, parameters and model artifacts.
In this exercise, you’ll use MLflow to track model training run as a command job.
12) Perform Hyperparameter Tuning with a Sweep job
Hyperparameters are variables that affect how a model is trained, but which can’t be derived from the training data. Choosing the optimal hyperparameter values for model training can be difficult, and usually involved a great deal of trial and error.
In this exercise, you’ll use Azure Machine Learning to tune hyperparameters by performing multiple training trials in parallel.
13) Run pipelines in Azure Machine Learning
You can use the Python SDK to perform all of the tasks required to create and operate a machine learning solution in Azure. Rather than perform these tasks individually, you can use pipelines to orchestrate the steps required to prepare data, run training scripts, and other tasks.
In this exercise, you’ll run multiple scripts as a pipeline job.
14) Create and explore the Responsible AI dashboard
After you train your model, you’ll want to evaluate your model to explore whether it’s performing as expected. Next to performance metrics, there are other factors you can take into consideration. The responsible AI dashboard in Azure Machine Learning allows you to analyze the data and the model’s predictions to identify any bias or unfairness.
In this exercise, you’ll prepare your data and create a responsible AI dashboard in Azure Machine Learning.
15) Log and register models with MLflow
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. When you log models with MLflow, you can easily move the model across platforms and workloads.
In this exercise, you’ll use MLflow to log machine learning models.
16) Deploy a model to a batch endpoint
In many scenarios, inferencing is performed as a batch process that uses a predictive model to score a large number of cases. To implement this kind of inferencing solution in Azure Machine Learning, you can create a batch endpoint.
In this exercise, you’ll deploy an MLflow model to a batch endpoint, and test it on sample data by submitting a job.
17) Deploy a model to a managed online endpoint
To consume a model in an application, and get real-time predictions, you’ll want to deploy the model to a managed online endpoint. An MLflow model is easily deployed since you won’t need to define the environment or create the scoring script.
In this exercise, you’ll deploy an MLflow model to a managed online endpoint, and test it on sample data.
Related/References:
- [DP-100] Microsoft Certified Azure Data Scientist Associate: Everything you must know
- Exam DP-100: Designing and Implementing a Data Science Solution on Azure
- AI-900: Azure AI Fundamentals: Everything You Need To Know
- Microsoft Azure AI Fundamentals [AI-900]: Step By Step Activity Guides (Hands-On Labs)
- DP 100 Exam | Microsoft Certified Azure Data Scientist Associate
- [DP-100] Designing and Implementing a Data Science Solution on Azure
- Microsoft Azure Data Scientist DP-100 FAQ
Next Task For You
In our Azure Data Scientist training program, we will cover 17 Hands-On Labs. If you want to begin your journey towards becoming a Microsoft Certified: Azure Data Scientist Associate check out our FREE CLASS.
Piyush says
Completion of this course will guarantee passing of the certification examination?
Jayesh Pandey says
Hi Piyush,
To pass the Microsoft Azure Exam DP-100 certification exams all you need is to:
1. Go through the Module wise lesson videos.
2. Perform the hands-on labs.
3. Give all quizzes to check your preparation level.
4. The assistance of the subject matter experts to clarify your technical as well as non-technical doubts.
This is all we provide in our azure exam DP-100 certification training courses.
Note: that we include the links for whitepapers and documentation in our training videos and question explanations, and we highly recommend you to go through those links and dive deep to understand the concepts better.
For more information on the course please drop us an email at contact@k21academy.com and the team will help you.
Thanks and Regards
Jayesh Pandey
Team K21 Academy