4 Days

DP-100: Design & Implement Data Science Solution on Azure- Overview

The DP 100 certification provides you with the skills to implement machine learning solutions at cloud scale using Azure Machine Learning. This course is designed to integrate your existing Python and machine learning expertise, allowing you to effectively manage data ingestion and preparation, model training, deployment, and solution monitoring using Azure Machine Learning and MLflow.

After completing DP-100 course, participants will be able to:

  • Design and set up an optimal working environment for data science workloads.
  • Use Python and machine learning libraries like NumPy, Pandas, and scikit-learn to explore and analyze data.
  • Train machine learning models using various algorithms such as linear regression, logistic regression, decision trees, and random forests.
  • Implement pipelines to streamline and automate machine learning workflows.
  • Execute jobs to prepare models for production environments.
  • Manage, deploy, and monitor scalable machine learning solutions.

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Key Features of DP-100: Design & Implement Data Science Solution on Azure

  • Interactive Hands-On Labs: Gain practical experience through a series of interactive labs and exercises, allowing you to apply your newly acquired skills in real-world scenarios.

  • Personalized Learning Plans: Enjoy tailor-made training programs that cater to your unique learning requirements, ensuring you receive the most relevant knowledge and guidance.

  • Flexible Scheduling: Choose from a range of flexible dates and timings, so you can seamlessly fit your training into your busy schedule.

  • Accredited Training: Rest assured, our training is accredited and adheres to the highest quality standards, ensuring you receive top-notch education.

  • Cost-Effective Pricing: Access all these benefits at a reasonable cost, ensuring that you receive excellent value for your investment in your AI education.

The DP-100 Certification: Design & Implement Data Science Solution on Azure is ideal for a wide range of professionals:

  • AI and data science enthusiasts are eager to delve into the world of machine learning.
  • AI developers and data scientists looking to enhance their machine learning capabilities.
  • Software engineers aiming to integrate AI services into their applications.
  • IT professionals interested in leveraging Azure services for AI.
  • Students who are learning about AI and cloud services.
  • Technical team leads and managers overseeing AI project implementations.

What are the prerequisites for the DP-100 course?

    To enroll in this course, it is advised to have:

  • Basic knowledge of Python programming
  • A general understanding of AI and ML concepts
  • Some familiarity with Azure DP 100 services
  • A fundamental grasp of REST API principles
  • A willingness to explore data science and software development

What makes CloudThat a compelling choice for DP-100 training for Microsoft Azure Data Scientists?

  • CloudThat stands out as a top choice for DP 100 certification training for aspiring Microsoft Azure Data Scientists. With a proven track record of successfully training around 6.5 lakh professionals and offering training services to over 100 corporate clients globally, CloudThat has earned its reputation for delivering high-quality education.
  • Our team consists of highly experienced, certified subject matter experts and instructors, ensuring a deep understanding of Azure DP 100 concepts. The training goes beyond theory, guiding participants through hands-on tasks and real-world challenges, which helps them grasp the practical applications and power of the Microsoft Azure DP 100 certification.
  • By focusing on relevant, industry-specific use cases, our DP 100 Azure certification program enables learners to gain a comprehensive understanding of Microsoft Azure DP 100 certification requirements, making them well-prepared for career growth in this field.

Learning Objectives of DP-100 Microsoft Azure Data Scientist Training:

  • Design and implement data science solutions using Azure Machine Learning.
  • Build and train machine learning models using Python and scikit-learn.
  • Deploy machine learning models to Azure Machine Learning Service.
  • Monitor and manage machine learning models in production.
  • Use MLflow to track and manage machine learning experiments.

Course Outline Download Course Outline

  • Explore the fundamentals of Azure Machine Learning and its role in data science workloads.
  • Discover the different components of Azure Machine Learning and how they work together.
  • Learn how to create and manage Azure Machine Learning workspaces.
  • Understand the different types of machine learning models that can be trained and deployed in Azure Machine Learning.

Learning Objectives

  • Describe the role of Azure Machine Learning in data science.
  • Identify the different components of Azure Machine Learning.
  • Create and manage Azure Machine Learning workspaces.
  • Understand the different types of machine learning models.

Hands-on Labs

  • Create an Azure Machine Learning workspace.
  • Deploy a pre-trained machine learning model.

  • Learn how to prepare data for machine learning using Azure Machine Learning.
  • Understand the importance of feature engineering and how to transform data for machine learning models.
  • Explore different feature engineering techniques, such as scaling, normalization, and dimensionality reduction.
  • Use Azure Machine Learning Studio to perform data preparation and feature engineering tasks.

Learning Objectives

  • Explain the importance of data preparation for machine learning.
  • Identify different feature engineering techniques.
  • Perform data preparation and feature engineering tasks using Azure Machine Learning Studio.

Hands-on Labs

  • Prepare data for machine learning using Azure Machine Learning Studio.
  • Engineer features using Azure Machine Learning Studio.

  • Learn how to train machine learning models using Azure Machine Learning.
  • Understand the different types of machine learning algorithms and their applications.
  • Explore different training techniques, such as hyperparameter tuning and regularization.
  • Evaluate the performance of machine learning models using metrics such as accuracy, precision, and recall.

• Learning Objectives

  • Train machine learning models using Azure Machine Learning.
  • Identify different types of machine learning algorithms.
  • Tune hyperparameters of machine learning models.
  • Evaluate the performance of machine learning models.

Hands-on Labs

  • Train a machine learning model using Azure Machine Learning.
  • Evaluate the performance of a machine learning model.

  • Learn how to deploy machine learning models into production using Azure Machine Learning.
  • Understand the different deployment options, such as Azure Machine Learning Services and Azure Kubernetes Service.
  • Explore different monitoring techniques to track the performance of deployed machine learning models.
  • Use Azure Machine Learning Studio to deploy and monitor machine learning models.

Learning Objectives

  • Deploy machine learning models into production using Azure Machine Learning.
  • Identify different deployment options.
  • Monitor the performance of deployed machine learning models.
  • Deploy and monitor machine learning models using Azure Machine Learning Studio.

Hands-on Labs

  • Deploy a machine learning model into production.
  • Monitor the performance of a deployed machine learning model.

  • Introduction
  • Create an Azure Machine Learning workspace
  • Identify Azure Machine Learning resources
  • Identify Azure Machine Learning assets
  • Train models in the workspace
  • Exercise - Explore the workspace
  • Knowledge check
  • Summary

Certification details:

  • Upon completing the DP-100: Designing and Implementing a Data Science Solution on Azure course, participants will receive a Participation Certificate. This certificate serves as recognition of your dedication and commitment to advancing your knowledge in the field of data science and Azure Machine Learning.

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Frequently Asked Questions

The DP 100 certification is designed for data scientists who want to build and deploy machine learning models on Microsoft Azure. It validates your expertise in managing data, running experiments, and training models using Azure DP 100 tools and platforms.

While it's possible to take the Azure DP 100 exam without prior experience, it's recommended to have a basic understanding of data science concepts and machine learning to successfully complete the Microsoft Azure DP 100 certification.

Yes, the DP 100 Azure certification exam is available in several languages, including English, Chinese, Japanese, and more, allowing candidates from different regions to take the exam in their preferred language.

CloudThat offers comprehensive DP 100 certification training, including hands-on labs, real-world projects, and expert-led sessions, which are designed to help you master the Microsoft Azure DP 100 exam objectives effectively.

CloudThat ensures top-quality training for the DP 100 Azure certification through experienced, certified instructors and a curriculum aligned with the latest Microsoft Azure DP 100 standards, with a focus on practical applications.

Yes, the Microsoft Azure DP 100 certification exam can be taken online via a proctored environment, allowing flexibility for candidates to complete the exam from home or any remote location.

Earning the DP 100 Azure certification can significantly enhance your career prospects, often leading to higher salary opportunities in data science, machine learning, and cloud-based roles.

Absolutely! The Microsoft Azure DP 100 certification is highly valuable for professionals looking to grow in cloud-based data science roles, as it demonstrates expertise in using Azure tools for machine learning and AI projects.

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