Professional certification in Azure, Microsoft

DESIGNING AND IMPLEMENTING A DATA SCIENCE SOLUTION ON AZURE TRAINING DP-100

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Award:
Certification
Awarding Body:
Microsoft
Duration & Study Mode:
Full time, Part time, Evening and Weekends, Virtual online
Location:
London, Flexible online
Duration: 5 Days / 5 Weeks

Designing and implementing a Data science Solution on Azure (MDP – 100) is an appropriate course for IT professionals who are working on both the Microsoft Azure platform and the Data Science track. The paradigm of the IT industry is shifting towards the update and newly invented technologies like Data Science, Virtualization, Cloud Computing, and so on. The content of this course is a blend of both the Azure cloud services and Data Science. The subject matter of this course is distributed in several modules, which are mentioned below:

This is the first module of the course that introduces the process of Data Science, the introduction of Azure notebooks, and the methods of integrating Data Science with Azure infrastructure. In the second module of this course, you will learn about how to integrate Data Science with Azure Machine Learning services.

Prerequisites

  • This course has no specific prerequisites.
  • It is ideal to have at least two or more years of experience in the field of Microsoft Azure and Data Science, but not required.

What will you gain after this course

  • This course will help both the Data Scientists and the Azure Specialists to learn about the most updated tools and techniques regarding these subject matter. 
  • Designing and Implementing a Data science Solution on Azure (MDP – 100) course could be considered as an ideal course for the IT professionals who are in charge of the IT infrastructure of the organization and also responsible for merging the concept and process of Data Science with Microsoft Azure platform.

Jobs you can get
with a Designing and Implementing a Data science Solution on Azure Certification

  • Entry Level Associate Data Scientist
  • Data Scientist – Programmer Analyst
  • Machine Learning Engineer
  • Associate Data Scientist
  • Senior Data Scientist

Corporate Group Training

  • Customized Training
  • Onsite / Virtual
  • Instructor-led Delivery
  • For small to large groups

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Who is this certification for?

  • Designing and Implementing a Data science Solution on Azure (MDP - 100) course is designed for the IT professionals who are already putting their effort into Microsoft Azure Cloud infrastructure and the integration process of Data Science with Azure.
  • IT industries across the world have shifted towards cloud computing and data science, and a blend of these two aspects can help any organization to make better business decisions.
Group-Icon2
Small Groups

With small groups of students, our instructors can work closely with each student.

Schedule
Flexible Class Schedules

Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.

Instructors
Experienced Instructors

Our instructors follow a modified are personalized approach to engage students during class

Lab-Facilities
Hi-Tech Lab Facilities

Our students can access our lab facilities anytime for practical experience during and after studies.

Syllabus

  • Introduction to Azure Machine Learning
  • Working with Azure Machine Learning
  • Use automated machine learning to train a machine learning model
  • Use Azure Machine Learning designer to train a model
  • Introduction to Experiments
  • Training and Registering Models
  • Create and use datastores
  • Create and use datasets
  • Create and use environments
  • Create and use compute targets
  • Create pipelines to automate machine learning workflows
  • Publish and run pipeline services
  • 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
  • Optimize hyperparameters for model training
  • Use automated machine learning to find the optimal model for your data
  • Apply differential privacy to data analysis
  • Use explainers to interpret machine learning models
  • Evaluate models for fairness
  • Monitoring Models with Application Insights
  • Monitoring Data Drift

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