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DP-100T01: Designing and Implementing a Data Science Solution on Azure

Duration: 4 Days (32 Hours)

DP 100 : Designing and Implementing a Data Science Solution on Azure Course Overview:

The “Operating Machine Learning Solutions at Cloud Scale Using Azure Machine Learning” course is designed to equip participants with the knowledge and skills necessary to effectively utilize Azure Machine Learning for managing and scaling machine learning solutions. Prior familiarity with Python and machine learning concepts is assumed for this course.

Throughout the course, participants will learn various aspects of operating machine learning solutions using Azure Machine Learning. This includes data ingestion and preparation, model training and deployment, and monitoring of machine learning solutions. Participants will gain a comprehensive understanding of leveraging Azure Machine Learning and MLflow to efficiently manage these processes.

Key topics covered in the course include:

  1. Data ingestion and preparation within the Azure environment, including techniques for data transformation, cleaning, and feature engineering to optimize model performance.
  2. Model training and deployment using Azure Machine Learning, ensuring scalability and readiness for production.
  3. Monitoring machine learning solutions using Azure Machine Learning and MLflow, including tracking and evaluating model performance over time and making necessary adjustments for optimal results.

By completing this course, participants will acquire the skills to effectively operate machine learning solutions at scale using Azure Machine Learning. They will be proficient in managing data ingestion, preparation, model training, deployment, and monitoring processes. This expertise will enable them to develop and manage robust and scalable machine learning solutions within the Azure cloud environment.

Audience Profile

The course on operating machine learning solutions at cloud scale using Azure Machine Learning is specifically designed for data scientists who already possess knowledge of Python and have experience working with machine learning frameworks such as Scikit-Learn, PyTorch, and Tensorflow. This course caters to data scientists who want to enhance their skills and expertise in building and operating machine learning solutions within a cloud environment.
With the assumption of existing knowledge in Python and machine learning frameworks, this course focuses on teaching data scientists how to leverage Azure Machine Learning to develop and manage machine learning solutions in the cloud. Participants will learn how to effectively utilize Azure Machine Learning’s features and capabilities to scale their machine learning workflows and harness the power of cloud computing.
Throughout the course, participants will gain insights into best practices for building and deploying machine learning models using Azure Machine Learning. They will learn how to take advantage of cloud-based resources to improve data ingestion, model training, deployment, and monitoring processes. The course will also cover techniques for optimizing performance and scalability of machine learning solutions within the cloud environment.
By completing this course, data scientists will be equipped with the necessary skills to build and operate machine learning solutions in the cloud using Azure Machine Learning. They will learn how to leverage their existing Python and machine learning knowledge to harness the scalability and resources of the cloud for enhanced performance and efficiency. This knowledge will empower data scientists to tackle complex machine learning challenges and deliver impactful solutions within a cloud-based environment.

Job role: Data Scientist

Azure Certification Path:

Design a data ingestion strategy for machine learning projects
  • Identify your data source and format
  • Choose how to serve data to machine learning workflows
  • Design a data ingestion solution
  • Identify machine learning tasks
  • Choose a service to train a model
  • Choose between compute options
  • Understand how a model will be consumed.
  • Decide whether to deploy your model to a real-time or batch endpoint.
  • Create an Azure Machine Learning workspace.
  • Identify resources and assets.
  • Train models in the workspace.
  • The Azure Machine Learning studio.
  • The Python Software Development Kit (SDK).
  • The Azure Command Line Interface (CLI).
  • Work with Uniform Resource Identifiers (URIs).
  • Create and use datastores.
  • Create and use data assets.
  • Choose the appropriate compute target.
  • Create and use a compute instance.
  • Create and use a compute cluster.
  • Understand environments in Azure Machine Learning.
  • Explore and use curated environments.
  • Create and use custom environments.
  • Prepare your data to use AutoML for classification.
  • Configure and run an AutoML experiment.
  • Evaluate and compare models.
  • Configure to use MLflow in notebooks
  • Use MLflow for model tracking in notebooks
  • Convert a notebook to a script.
  • Test scripts in a terminal.
  • Run a script as a command job.
  • Use parameters in a command job.
  • Use MLflow when you run a script as a job.
  • Review metrics, parameters, artifacts, and models from a run.
  • Create components.
  • Build an Azure Machine Learning pipeline.
  • Run an Azure Machine Learning pipeline.
  • Define a hyperparameter search space.
  • Configure hyperparameter sampling.
  • Select an early-termination policy.
  • Run a sweep job.
  • Use managed online endpoints.
  • Deploy your MLflow model to a managed online endpoint.
  • Deploy a custom model to a managed online endpoint.
  • Test online endpoints.
  • Create a batch endpoint.
  • Deploy your MLflow model to a batch endpoint.
  • Deploy a custom model to a batch endpoint.
  • Invoke batch endpoints.

DP 100 : Designing and Implementing a Data Science Solution on Azure Course Prerequisites:

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques. 

Specifically:

  • Creating cloud resources in Microsoft Azure.
  • Using Python to explore and visualize data.
  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
  • Working with containersTo gain these prerequisite skills, take the following free online training before attending the course:
  • Explore Microsoft cloud concepts.
  • Create machine learning models.
  • Administer containers in Azure
  • If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
Q: What is the DP-100 course?

A: The DP-100 course is a training program designed to provide participants with the knowledge and skills necessary to design and implement data science solutions on the Microsoft Azure platform. It covers various topics related to data exploration, data preprocessing, modeling, machine learning algorithms, and deploying models in Azure.

A: The DP-100 course is intended for data scientists, data engineers, and other professionals interested in designing and implementing data science solutions on Azure. It is suitable for individuals with a background in data science and programming who want to leverage Azure services for their data analysis and modeling tasks.

A: There are no specific prerequisites for the DP 100 course. However, having a foundational understanding of data science concepts, proficiency in programming languages such as Python, and familiarity with Azure services will be beneficial.

A: The DP 100 course covers a range of topics, including data exploration and visualization, data preprocessing techniques, feature engineering, machine learning algorithms, model evaluation, and deployment of models using Azure services.

A: The duration of the DP100 course can vary depending on the training provider and the format of the course. It can range from a few days to several weeks, with part-time or full-time study options available.

A: Yes, TrainCrest offers the DP-100 course in an online format also, allowing you to learn at your own pace and convenience.

A: Yes, the DP100 course typically includes hands-on exercises that allow you to apply the concepts and techniques learned. These exercises may involve working with real-world datasets, implementing data preprocessing techniques, building and evaluating machine learning models, and deploying models using Azure services.

A: Yes, upon completing the DP100 course, you may receive a certificate of completion or participation from the training provider. However, it’s important to note that the DP100 certification is obtained by passing the official Microsoft DP100 exam, which is separate from the course.

A: The DP 100 course is designed to cover the topics and concepts relevant to the DP 100 exam. However, it is recommended to supplement the course with additional study materials, practice exams, and hands-on experience to ensure thorough preparation for the exam.

A: In addition to the DP 100 course, you can find additional study materials on the official Microsoft Learning website, including exam guides, documentation, and online resources. Microsoft also offers practice exams and official study guides specifically tailored for the DP 100 exam.

Discover the perfect fit for your learning journey

Choose Learning Modality

Live Online

  • Convenience
  • Cost-effective
  • Self-paced learning
  • Scalability

Classroom

  • Interaction and collaboration
  • Networking opportunities
  • Real-time feedback
  • Personal attention

Onsite

  • Familiar environment
  • Confidentiality
  • Team building
  • Immediate application

Training Exclusives

This course comes with following benefits:

  • Practice Labs.
  • Get Trained by Microsoft Certified Trainers (MCT).
  • Access to the recordings of your class sessions for 90 days.
  • Digital courseware
  • Experience 24*7 learner support.

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