A compute target is a designated compute resource or environment where you run your training script or host your service deployment. This location might be your local machine or a cloud-based computing resource.
This post will cover some quick tips including FAQs on the topics that we covered in the Day 3 live session which will help you to clear Certification [DP-100] & get a better-paid job.
In the previous week sessions, in Day 2 session we got an overview of Running Experiments, Training Models and Working with Data. And in this week’s Day 3 Live Session of the AI/ML & Azure Data Scientist Certification [DP-100] training program, we covered the concepts of Working with Compute. We also covered hands-on Lab 11 out of our 15+ extensive labs.
So, here are some of the Q & As asked during the Live session from Module 5: Working with Compute of Microsoft Azure Data Scientist [DP-100]
DP-100 FAQ’s: Working with Compute
When you run a script as an Azure Machine Learning experiment, you would like to define the execution context for the experiment run. The execution context is formed up of:
- The Python environment for the script, which must include all Python packages utilized in the script.
- The computed target on which the script is going to be run. this might be the local workstation from which the experiment run is initiated or a remote compute target like a training cluster that’s provisioned on-demand.
Source: Microsoft
Q1: What are Azure Machine Learning Environments?
Ans: Azure Machine Learning environments are an encapsulation of the environment where the machine learning training happens. They specify the Python packages, environment variables, and software settings around your training and scoring scripts.
Q2: What are curated environments in Azure ML?
Ans: Azure Machine Learning provides curated environments, which are predefined environments that provide good starting points for building your own environments. Curated environments are backed by cached Docker images, providing a reduced run preparation cost.
Q3: What is training Compute Targets?
Ans: Azure Machine Learning has varying support across different compute targets. A typical model development lifecycle starts with development or experimentation on a little amount of data. At this stage, use an area environment like your local computer or a cloud-based VM. As you proportion your training on larger datasets or perform distributed training, use Azure Machine Learning compute to make a single- or multi-node cluster that auto-scales whenever you submit a run. you’ll also attach your own compute resource, although support for various scenarios might vary.
You can use any of the subsequent resources for a training compute target for many jobs. Not all resources are often used for automated machine learning, machine learning pipelines, or designer
Source: Microsoft
Q4: What is an Azure Machine Learning compute instance?
Ans: An Azure Machine Learning compute instance may be a managed cloud-based workstation for data scientists.
Compute instances make it easy to urge started with Azure Machine Learning development also as provide management and enterprise readiness capabilities for IT administrators.
Use a compute instance as your fully configured and managed development environment within the cloud for machine learning. they will even be used as a compute target for training and inferencing for development and testing purposes.
Q5: Why use a compute instance?
Ans: A compute instance is a fully managed cloud-based workstation optimized for your machine learning development environment. It provides the following benefits:
- Productivity: You can build and deploy models using integrated notebooks and the following tools in Azure Machine Learning studio:- Jupyter, JupyterLab, VS Code, RStudio
- Fully Integrated: Compute instance is fully integrated with Azure Machine Learning workspace and studio. You can share notebooks and data with other data scientists in the workspace.
- Managed & secure: Reduce your security footprint and add compliance with enterprise security requirements. Compute instances provide robust management policies and secure networking configurations.
- Preconfigured for ML: Save time on setup tasks with pre-configured and up-to-date ML packages, deep learning frameworks, GPU drivers.
- Fully customizable: Broad support for Azure VM types including GPUs and persisted low-level customization such as installing packages and drivers makes advanced scenarios a breeze.
Q6: What is the Gini index?
Ans: Gini index is another way to identify which variables influence the data most. A Gini index of zero expresses perfect equality, where all values are the same (for example, where everyone has the same income). A Gini index of one expresses maximal inequality among values
Q7: What is pyarrow ?
Ans: This library provides a Python API for the functionality provided by the Arrow C++ libraries, along with tools for Arrow integration and interoperability with pandas, NumPy, and other software in the Python ecosystem
Q8: What has preferred: Azure ML Studio or SDK?
Ans: The Azure Machine Learning SDK for Python provides both stable and experimental features in the same SDK. This is the payoff for your coding. But You can use Azure ML studio according to your requirements.
Source: Microsoft
Q9: Does a Data Scientist need to know the concept of Docker & Container?
Ans: A containerization is an approach to software development in which an application or service, its dependencies, and its configuration are packaged together as a container image. Docker is an open-source project for automating the deployment of applications as portable, self-sufficient containers that can run on the cloud or on-premises.
Q10: Do we have to train the model first and then register or register the model first and then train?
Ans: We have to train the model first and then register it. When you register a model, we upload the model to the cloud (in your workspace’s default storage account) and then mount it to the same compute where your web service is running.
Feedback Received…
From our DP-100 day 3 session, we received some good feedback from our trainees who had attended the session, so here is a sneak peek of it.
To know more about DP-100 certification and whether it is the right certification for you, read our blog on [DP-100] Microsoft Certified Azure Data Scientist Associate: Everything you must know
Quiz Time (Sample Exam Questions)
With my AI/ML & Azure Data Science training program, we cover 150+ sample exam questions to help you prepare for the certification DP-100.
Check out one of the questions and see if you can crack this…
A. Provision a managed Azure Compute Instance.
B. Attach a remote VM as a compute.
C. Provision Azure ML Compute Cluster
D. Make use of Azure Kubernetes Service
Comment with your answer & we will tell you if you are correct or not!!
Related/References
- [DP-100] Microsoft Certified Azure Data Scientist Associate: Everything you must know
- [DP-100] Design & Implement a Data Science Solution on Azure Question & Answers/Day 1 Live Session Review
- [DP-100] Design & Implement a Data Science Solution on Azure QnA Day 2 Live Session Review
- Microsoft Certified Azure Data Scientist Associate | DP 100 | Step By Step Activity Guides (Hands-On Labs)
- [AI-900] Microsoft Certified Azure AI Fundamentals Course: Everything you must know
- Azure Machine Learning Service Workflow: Overview for Beginners
- Azure ML Model
- Azure Free Account: Steps to Register for Free Trial Account
Next Task For You
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