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


• Duration: 3 Days
• Mode of Delivery: Online -Instructor-led training
• Job role: Azure Data Scientists
• Preparation for exam: DP-100
Cost: USD$1,750.00

In this course, the student will learn how to create and operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

20 in stock (can be backordered)


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 technologies that exist on both in the cloud and on-premises.

Skills Gained

After completing this course, students will be able to:
• Manage Azure resources for machine learning
• Run experiments and train models
• Deploy and operationalize machine learning solutions
• Implement responsible machine learning

Course outline

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


Click on the following link to see the current Course Schedule
Our minimum class-size is 3 for this course.
If there are no scheduled dates for this course, it can be customized to suit the time and skill needs of clients and it can be held online, at a rented location or at your premises.
Click on the following link below to arrange for a custom course: Enquire about a course date.

Product Information

Data is created constantly, and at an ever-increasing rate. Mobile phones, social media, imaging technologies to determine a medical diagnosis-all these and more create new data, and that must be stored somewhere for some purpose. Devices and sensors automatically generate diagnostic information that needs to be stored and processed in real time. Merely keeping up with this huge influx of data is difficult, but substantially more challenging is analyzing vast amounts of it, especially when it does not conform to traditional notions of data structure, to identify meaningful patterns and extract useful information. These challenges of the data deluge present the opportunity to transform business, government, science, and everyday life.

Data Science is: it is a combination of multiple disciplines, including business, statistics, and programming, that intends to extract meaningful insights from data by running controlled experiments similar to scientific research. The objective of any data science project is to derive valuable knowledge for the business from data in order to make better decisions. It is the responsibility of data scientists to define the goals to be achieved for a project. This requires business knowledge and expertise. In this course, you will be exposed to some examples of data science tasks from real-world datasets.

A data scientist is a specialist who applies their expertise in statistics and building machine learning models to make predictions and answer key business questions. A data scientist still needs to be able to clean, analyze, and visualize data, just like a data analyst. However, a data scientist will have more depth and expertise in these skills, and will also be able to train and optimize machine learning models. The data scientist will uncover hidden insights by leveraging both supervised (e.g. classification, regression) and unsupervised learning (e.g. clustering, neural networks, anomaly detection) methods toward their machine learning models. They are essentially training mathematical models that will allow them to better identify patterns and derive accurate predictions.

Data engineers establish the foundation that the data analysts and scientists build upon. Data engineers are responsible for constructing data pipelines and often have to use complex tools and techniques to handle data at scale. Unlike the previous two career paths, data engineering leans a lot more toward a software development skill set. At larger organizations, data engineers can have different focuses such as leveraging data tools, maintaining databases, and creating and managing data pipelines. Whatever the focus may be, a good data engineer allows a data scientist or analyst to focus on solving analytical problems, rather than having to move data from source to source. The data engineer’s mindset is often more focused on building and optimization.

Additional Information

CANCELLATION POLICY – There is never a fee for cancelling seven business days before a class for any reason. Data Vision Systems reserves the right to cancel any course due to insufficient registration or other extenuating circumstances. Participants will be advised prior to doing so.


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