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Certification BootCamp for Exam DP-100: Design & Implement Data Science Solution on Azure

DP-100

Duration: 3 Days

Description

The Microsoft DP-100 BootCamp course from CloudThat is designed to train candidates who plan to take up the Microsoft DP-100 certification exam. Taking this course and passing the Microsoft DP-100 exam will meet all the requirements needed to become a Microsoft Certified Azure Data Scientist Associate.

This is newly launched course by Microsoft. During the training candidates will use machine learning techniques to train, evaluate and deploy models to build AI solutions that satisfy business objectives. Candidates will also use an applications that involve natural language processing, speech, computer vision and predictive analytics.

After this training, candidates can apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders.

Course Outline

  • Module 1: Introduction to Azure Machine Learning
  • Module 2: No-Code Machine Learning with Designer
  • Module 3: Running Experiments and Training Models
  • Module 4: Working with Data...Read more

Objectives

  • To understand and build AI solutions on Azure
  • To learn about various Azure Machine Learning services usage & integration
  • To understand the profound impacts Machine Learning is making in smart business decisions

Who Should Attend

Candidates serving as part of a multi-disciplinary team that incorporates ethical, privacy, and governance considerations into the solution.

Prerequisites

  • Candidates typically have background in mathematics, statistics and computer science
  • Basic knowledge of Cloud platform: Azure
  • Basic understanding of Machine Learning
  • IT industry work experience or those pursuing a degree in the IT field
  • Strong learning acumen

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

Hands-On: Creating an Azure Machine Learning Workspace
Hands-On: Working with Azure Machine Learning Tools

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

Hands-On: Creating a Training Pipeline with the Azure ML Designer
Hands-On: Deploying a Service with the Azure ML Designer

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

Hands-On: Running Experiments
Hands-On: Training and Registering 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

Hands-On: Working with Datastores
Hands-On: Working with 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

Hands-On: Working with Environments
Hands-On: Working with 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

Hands-On: Creating a Pipeline
Hands-On: Publishing a Pipeline

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

Hands-On: Creating a Real-time Inferencing Service
Hands-On: Creating a Batch Inferencing 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

Hands-On: Tuning Hyperparameters
Hands-On: Using Automated Machine Learning

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

Hands-On: Reviewing Automated Machine Learning Explanations
Hands-On: Interpreting 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

Hands-On: Monitoring a Model with Application Insights
Hands-On: Monitoring Data Drift

About the Trainer

A Certified Microsoft Azure Trainer

Other Details

Questions?

For latest batch dates, fees, location, technical queries and general inquiries, contact our sales team at: +91 8880002200 or email at sales@cloudthat.in

FAQs

Who should take up the Microsoft DP-100 certification exam?

CloudThat’s Azure training course for the preparation of DP-100 certification exam can be taken up by: Candidates serving as part of a multi-disciplinary team that incorporates ethical, privacy, and governance considerations into the solution.

What is the time duration for Azure DP-100 exam?

Candidates appearing for the DP-100 exam will get 180 minutes to complete the Microsoft Azure Data Scientist Associate certification exam.

How many questions are there in Microsoft Azure DP-100 certification exam?

Candidates need to answer between 45-60 questions. However, the number of questions may change as and when changes in technology and job roles occur.

What is the cost of the Microsoft Azure Data Scientist Associate certification exam in India?

The cost of the Microsoft Azure Fundamentals certification exam is ₹4800 in Bangalore and other Indian cities.

The exam cost is mostly priced according to the currency values in specific regions and countries. However, the exam prices are subject to change and may also vary depending on the additional taxes that may apply in some countries and regions.

What is the passing score to clear Azure DP-100 exam?

Candidates need to score 700 (on a scale of 1-1000) approx. to pass the Microsoft AZ-900 exam.

What is the DP-100 exam retake policy?

  • Candidates must wait for 24 hours to retake the DP-100 exam on failing in his/her first attempt. Also, candidates can log in to their certification dashboard to reschedule the exam.
  • The candidate must wait for at least 14 days to retake the exam for the third time on failing in their second attempt. The waiting period is the same while retaking the exam for the fourth and fifth times.
  • In a year, a candidate is allowed a maximum of 5 retakes. (One year is calculated from the day of the fifth unsuccessful exam attempt.)

What will you learn from CloudThat’s DP-100 Microsoft Azure training course?

After completing CloudThat’s highly interactive DP-100 exam training in Bangalore or other cities, you will be able to:

  • Set up an Azure Machine Learning workspace
  • Run experiments and train models
  • Optimize and manage models
  • Deploy and consume models

Which is the best Azure training provider in Bangalore?

CloudThat, a Microsoft Gold Partner and the winner of Microsoft Learning Partner 2020 of the Year Finalist award, is undoubtedly the best Azure training provider in Bangalore and other cities in India. CloudThat has trained over 350K+ IT professionals from fortune 500 companies in Cloud since 2012. From personalized mentoring to self-paced learning modules, we work towards providing a growth-oriented learning experience to nurture their future.
• CloudThat’s training modules are equipped with 50%-60% hands-on lab sessions.
• Highly interactive virtual and classroom teaching.
• Qualified instructor-led training and mentoring sessions.
• Practice lab and projects aligned to Azure learning modules.
• Integrated teaching assistance and support.

How to prepare for the Microsoft DP-100 exam?

To prepare for the DP-100 certification and pass the exam successfully, candidates need to enroll in CloudThat’s DP-100 certification training course in Bangalore. Our subject matter experts and certified Azure trainers offer training through online or instructor-led classes and provide relevant study material necessary to support your exam preparation. At CloudThat, you can strengthen your practical Azure skills through hands-on lab sessions and test your knowledge before the final exam through our testprep platform. Besides our training and assistance, candidates can also:
• Join online forum discussions and study groups to enrich your knowledge.
• Have a detailed understanding of the exam structure.
• Study Microsoft documentation along with relevant whitepapers and eBooks.

What are the different types of questions candidates need to answer in the Microsoft Azure DP-100 certification exam?

The different types of DP-100 exam questions that may appear in the real exam include:
• Multiple-choice questions
• Drag and drop
• Reordering
• Short answers

Upcoming Batches

India Online Enroll
  • 7 Jul 2021 - 9 Jul 2021 (Wed - Fri)
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