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

DP-100: DP-100 equips you with the expertise to operate machine learning solutions at cloud scale using Azure Machine Learning. This course seamlessly integrates your existing Python and machine learning knowledge to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

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

  • Design and create a suitable working environment for data science workloads.
  • Explore data using Python and machine learning libraries like NumPy, Pandas, and scikit-learn.
  • Train machine learning models using algorithms like linear regression, logistic regression, decision trees, and random forests.
  • Implement pipelines to automate machine learning workflows.
  • Run jobs to prepare for production.
  • 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.

Who can participate in the DP-100: Design & Implement Data Science Solution on Azure Certification Training?

  • AI and data science enthusiasts eager to explore the world of machine learning.
  • AI developers and data scientists want to enhance their machine learning skills.
  • Software engineers aiming to integrate AI services into applications.
  • IT professionals looking to leverage Azure services for AI.
  • Students 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 recommended to have:

  • Basic proficiency in Python programming
  • A general awareness of AI and ML principles
  • Some exposure to Azure AI services
  • A fundamental understanding of REST API concepts
  • 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 has successfully trained a vast number of professionals, approximately 6.5 lakh individuals, and provided training services to more than 100 corporate clients across the globe.
  • We have well-trained, experienced, and certified Subject matter experts and instructors to conduct these trainings.
  • The course guides students through specific tasks and real-world challenges to help them understand the relevance, power, and usefulness of Microsoft Word.

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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Course ID: 13462

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