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


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Microsoft Azure data science certification by CCS Learning Academy is an instructor let Data Engineering on Microsoft. We have created a more structured learning approach for our students only to guide you to stay up to the minute on the latest Microsoft Azure trends.
The Azure DP-100 examination is based on designing and implementing data science solutions. Responsibilities of the role include designing and creating a worthy environment looking after the workloads, managing learning models, implementing pipelines, and a lot more.
Our course not only helps you to leverage your knowledge of python but also gives detailed knowledge to manage data injection and preparation also providing machine learning solution monitoring in Microsoft Azure.
In case you are willing to enroll yourself in the DP 100 course, feel free to get in touch with CCS Learning Academy for the details.

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360° Microsoft Azure data science certification Course

The Microsoft Azure data science certification course by CCS Learning Academy comprises resources created by experienced trainers whare also industry experts. With our course, you will learn data science processes and ways to use a greater degree of automation in a seamless way.

Each course topic is broken into modules so that the students find it easier to remember the core subject matter. Microsoft Data Science certification starts with an introduction to Azure Machine Learning, followed by working on Machine Learning. We help students to get familiar with the Machine learning workspace and gradually move to the complicated parts of Azure which are running experiments, training models, and more.

What will you learn from our course?

CCS Learning Academy can help you to level up your knowledge and expertise in Data Science. With years of experience in the IT industry, we help aspirants stay up to date on Azure trends. Our course will help you to learn:

    • By applying for the course you will learn to prepare yourself for the Microsoft exam and Data Engineering on Microsoft Azure.

Will learn operating machine learning solutions in the cloud using Azure machine learning.

Learn top programming languages like python and other essential languages.

Model training and deployment along with machine learning solution monitoring.

You will get the complete details of the course in the course topic section. Go through it and understand the modules we are providing. We have subdivided the modules according to the lessons we are going to provide for your in-depth understanding.

Knowledge required to get the Azure data science certification?

A candidate opting for this course or willing to be a part of the examination needs to have some knowledge and expertise in data science. They must know Azure Machine learning and MFlow.

You must also have ideas to define and prepare the development environment, perform feature engineering, develop models, and perform feature engineering.

Benefits of Azure DP-100 training

In today’s IT market, Microsoft Azure is one of the most popular cloud computing services that helps data scientists to design and implement solutions easily. CCS Learning Academy has created this self-paced course where learners can easily understand the degree of automation.

Mentioned below are a few benefits of enrolling in the Azure data science certification:

    • Certification can prove beneficial for your career:

Data security helps companies in understanding multiple sources and valuable insights into data. Therefore, its protection is equally important and this is why data security courses provide bigger career prospects for developers.

Provide credibility to any job position: To prove credibility you need an accurate certificate and expertise. If you are a data science aspirant then the DP-100 examination can help you to become an associate and expert. The certification can also prove your dedication and commitment to the subject and make you a better professional. This course is the best one for candidates who want to initiate their journey in data security.

Validation of your skills and knowledge of data security: Microsoft Data science certification validates the skill and knowledge in machine learning and data science. It evaluates your candidature in the categories like managing resources for machine learning, running experiments, deploying machine learning solutions, implementing responsible machine learning, and much more

The Microsoft Azure data science certification by CCS Learning Academy qualifies you for your fundamental’s certification examination. Also, help you to become a qualified data scientist. Do enroll in our course if you are willing to align your skills with the azure architectural commitment.

Why choose the dp100 course from CCS Learning Academy?

CCS Learning Academy has years of experience in technology training. Indeed, we train our students under the guidance of years-old industry experts on LIVE projects. Also, the complete dp100 course is explained clearly and all modules are covered on time.

We ensure that after the completion of the course, each of the students is completely prepared for the examination. Azure data science certification requires accomplishing technical tasks like:

Preparing a machine-learning solution

Exploring data and training models

Preparing a model of deployment

Deploying and Retaining a model

CCS Learning academy believes that Azure provides better security offerings, and this is why this course is worth opting for. We have covered each module evenly and you can easily get ready for your examination. With the help of our instructor-led classes, resources, assessments, and exam materials it will definitely be easy.

Therefore, get in touch with us if you have got plans to enroll in the dp100 course in the future session. You can either give us a call or send an email for the details.







Course Topics

Module 1: Getting Started with 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.


  • 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

Module 2: No-Code 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.


  • 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

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.


  • 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

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.


  • 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

Module 5: Working with Compute

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.


  • 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

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.


  • 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

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.


  • 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

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.


  • 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

Module 9: Responsible Machine Learning

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.


  • Differential Privacy
  • Model Interpretability
  • Fairness

Lab : Explore Differential privacy

Lab : Interpret Models

Lab : Detect and Mitigate Unfairness

After completing this module, you will be able to

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

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.


  • 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

Skills Gained

Target Audience

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.


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.


  • 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 containers

To gain these prerequisite skills, take the following free online training before attending the course:

If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.


With CCS Learning Academy, you’ll receive:

  • 3 Day Certified Instructor-led training
  • Official Training Seminar Student Handbook
  • Collaboration with classmates (not currently available for self-paced course)
  • Real-world learning activities and scenarios
  • Exam scheduling support*
  • Enjoy job placement assistance for the first 12 months after course completion.
  • This course is eligible for CCS Learning Academy’s Learn and Earn Program: get a tuition fee refund of up to 50% if you are placed in a job through CCS Global Tech’s Placement Division*
  • Government and Private pricing available.*

*For more details call: 858-208-4141 or email: training@ccslearningacademy.com; sales@ccslearningacademy.com


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