Microtek Learning Logo

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

4.8
(4.8)

This technical course enhances and supports one's current knowledge of machine learning and Python to manage data preparations and ingestions, model training, and launch machine learning solution inspection in Microsoft Azure.

  • Accredited By : Microsoft Partner Logo
  • Category : Azure

Course Price : $1725 Per Participant

Course Description

DP-100T01: Designing and Implementing a Data Science Solution on Azure Training teaches the techniques to work with machine learning solutions at the cloud scale with the help of Azure Machine Learning. This technical course enhances and supports one's current knowledge of machine learning and Python to manage data preparations and ingestions, model training, and launch machine learning solution inspection in Microsoft Azure.

Our enterprise training program is best for organizations and companies. It teaches automation, management, and monitoring of machine learning using Azure Machine Learning service. This training program is ideal for data scientists with experience in machine learning frameworks and Python, who want to develop and work with machine learning solutions in the cloud. It is also very useful for individuals preparing for the Microsoft Certified: Azure Data Scientist Associate certification exam.

This training is designed based on the objectives of the course variant DP-100T01-A.

 

Training Exclusives

  • Live instructor-led interactive sessions with Microsoft Certified Trainers (MCT).
  • Access to Microsoft Official Courseware (MOC).
  • Real-time Virtual Lab Environment.
  • Experience 24*7 Learner Support.
  • Self-paced learning and flexible schedules.
Microsoft Course Microsoft Course
500+

Courses

experience experience
20+

Years of Experience

learners learners
95K+

Global Learners

What you will learn

  • green-tick Doing Data Science on Azure
  • green-tick Doing Data Science with Azure Machine Learning service
  • green-tick Automating Machine Learning with Azure Machine Learning service
  • green-tick Managing and Monitoring Machine Learning Models with the Azure Machine Learning service

Who should attend this course?

Data scientists who know Python and existing understanding of machine learning frameworks such as TensorFlow, PyTorch, and Scikit-Learn who want to create and manage machine learning solutions in the cloud are the primary audiences for this training. 

Microsoft Learning PartnerMicrosoft Learning Partner

Microtek Learning is a Microsoft Certified Partner for Learning Solutions. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).

Schedules

  • calendar Jun 12, 2024
  • time 8:30 am - 4:30 pm EST
  • location online
GTR
  • calendar Jun 26, 2024
  • time 8:30 am - 4:30 pm EST
  • location online
  • calendar Jul 10, 2024
  • time 8:30 am - 4:30 pm EST
  • location online
GTR
  • calendar Jul 24, 2024
  • time 8:30 am - 4:30 pm EST
  • location online
  • calendar Aug 07, 2024
  • time 8:30 am - 4:30 pm EST
  • location online
GTR
  • calendar Aug 21, 2024
  • time 8:30 am - 4:30 pm EST
  • location online

Can’t Find The Batch You’re Looking For?

Request a Batch

Curriculum

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.

Lesson

  • Introduction to Azure Machine Learning
  • Working with Azure Machine Learning
  • Lab: Create an Azure Machine Learning Workspace

    After completing this module, you will be able to

  • Provision an Azure Machine Learning workspace
  • Use tools and code to work with Azure Machine Learning
  • This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.

    Lesson

  • Automated Machine Learning
  • Azure Machine Learning Designer
  • Lab: Use Automated Machine Learning

    Lab: Use Azure Machine Learning Designer

    After completing this module, you will be able to

  • Use automated machine learning to train a machine learning model
  • Use Azure Machine Learning designer to train a model
  • 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.

    Lesson

  • Introduction to Experiments
  • Training and Registering Models
  • Lab: Run Experiments

    Lab: Train Models

    After completing this module, you will be able to

  • Run code-based experiments in an Azure Machine Learning workspace
  • Train and register machine learning models
  • 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.

    Lesson

  • Working with Datastores
  • Working with Datasets
  • Lab: Work with Data

    After completing this module, you will be able to

  • Create and use datastores
  • Create and use datasets
  • 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.

    Lesson

  • Working with Environments
  • Working with Compute Targets
  • Lab: Work with Compute

    After completing this module, you will be able to

  • Create and use environments
  • Create and use compute targets
  • 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.

    Lesson

  • Introduction to Pipelines
  • Publishing and Running Pipelines
  • Lab: Create a Pipeline

    After completing this module, you will be able to

    Create pipelines to automate machine learning workflows

    Publish and run pipeline services

    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.

    Lesson

  • Real-time Inferencing
  • Batch Inferencing
  • Continuous Integration and Delivery
  • Lab: Create a Real-time Inferencing Service

    Lab: Create a Batch Inferencing Service

    After completing this module, you will be able to

  • Publish a model as a real-time inference service
  • Publish a model as a batch inference service
  • Describe techniques to implement continuous integration and delivery
  • 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.

    Lesson

  • Hyperparameter Tuning
  • Automated Machine Learning
  • Lab: Tune Hyperparameters

    Lab: Use Automated Machine Learning from the SDK

    After completing this module, you will be able to

  • Optimize hyperparameters for model training
  • Use automated machine learning to find the optimal model for your data
  • Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.

    Lesson

  • Differential Privacy
  • Model Interpretability
  • Fairness
  • Lab: Explore Differential provacy

    Lab: Interpret Models

    Lab: Detect and Mitigate Unfairness

    After completing this module, you will be able to

  • Apply differential provacy to data analysis
  • Use explainers to interpret machine learning models
  • Evaluate models for fairness
  • 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.

    Lesson

  • Monitoring Models with Application Insights
  • Monitoring Data Drift
  • Lab: Monitor a Model with Application Insights

    Lab: Monitor Data Drift

    After completing this module, you will be able to

  • Use Application Insights to monitor a published model
  • Monitor data drift
  • What Exam Do I Need To Get Certified?

    • Exam DP-100

    About the Certifications

    Microsoft Certified: Azure Data Scientist Associate Certification is a role-based certification for those who apply their skills of machine learning (ML) and data science to execute and run machine learning workloads on Azure, in specific, utilizing Azure ML (Machine learning) Service. This certification at Microtek Learning entails preparing and forming a suitable working environment for data science workloads on Azure, performing data experiments, and deploying ML models into production.

    This course mainly covering the very latest concepts like Python Fundamentals, Introduction to Data Scientist, Data cleaning and collection, Visualizing data, Array Computations, Control flow and functions, and Web Scrapping. The DP-100 exam (Designing & Implementing a Data Science Solution on Azure) must be passed to obtain the certification.

    Certification Details

    Data Science Solution on Azure

    Step 1: Review the skills and knowledge required to certify.

    Step 2: Recommended training for certification:

    Step 3: Take this exam and get certified.

    • Exam DP-100

     

    Who Should Attend?

    • Data Scientists.
    • Individuals handling significant responsibilities in training and deploying machine learning models.
    • The ones preparing for the exam DP-100 and Microsoft Certified: Azure Data Scientist Associate certification.

     

    Skills Measured

    • Manage Azure resources for machine learning
    • Run experiments and train models
    • Deploy and operationalize machine learning solutions
    • Implement responsible machine learning

     

    Certification Latest Updates

    Microsoft Recertification Update:

    1. Previously, Microsoft role-based and specialty certifications were valid for two years.
    2. Starting June 2021, certifications are valid for one year, but they can be renewed online for free at Microsoft Learn.
    3. The renewal window begins six months before the cert expires. During this window, before the cert expires, you can take a free online assessment and get the cert extended by one additional year from the current expiration date.
    4. Want the best of both worlds, old and new? Certifications earned before June 2021 will be valid for two years and eligible for the new renewal process.

    Course Details

    • cert cert-green
      Certification: YES
    • skill skill-green
      Skill Level: Intermediate
    • enroll enroll-green
      Enrolled: 1478
    • duration duration green
      Duration: 3 Days

    Talk to Learning Advisor