Microsoft Cloud Workshop: Machine Learning Course Overview

Microsoft Cloud Workshop: Machine Learning Course Overview

The Microsoft Cloud Workshop: Machine Learning course is a comprehensive program designed for learners to gain hands-on experience in applying machine learning solutions using Microsoft Azure. It consists of two main modules:

Module 1: Whiteboard Design Session - Machine Learning, guides participants through a customer case study, prompting them to design a proof of concept solution and present it, fostering a deep understanding of real-world scenarios.

Module 2: Hands-on lab - Machine Learning, offers practical exercises where learners create models using automated machine learning, delve into model explainability, and work with deep learning models for time series data and text classification. Participants will also explore scoring of streaming telemetry with their forecast models.

By the end of the course, learners will have developed the skills to design and implement robust machine learning solutions, preparing them for challenges in the field of data science and AI.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

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Course Prerequisites

To ensure a successful learning experience in the Microsoft Cloud Workshop: Machine Learning course, participants should come equipped with the following minimum prerequisites:


  • Basic understanding of cloud computing concepts, particularly within the Microsoft Azure ecosystem.
  • Familiarity with Azure services, including Azure Machine Learning and Azure Databricks.
  • Foundational knowledge of machine learning concepts and principles.
  • Experience with Python programming, as it is commonly used in machine learning tasks and labs.
  • Basic proficiency in data handling and manipulation using libraries such as Pandas and NumPy.
  • An understanding of data science and analytics processes.
  • Prior exposure to or experience with creating and deploying machine learning models is helpful, though not strictly necessary.

These prerequisites are designed to ensure that learners can effectively engage with the course content and participate in hands-on lab activities. If you're new to some of these concepts, we recommend exploring introductory materials or courses in cloud computing, Python programming, and machine learning before enrolling in this workshop.


Target Audience for Microsoft Cloud Workshop: Machine Learning

  1. The Microsoft Cloud Workshop: Machine Learning course offers hands-on experience in creating advanced machine learning models, catering to IT professionals and data scientists.


  2. Target audience for the course includes:


  • Data Scientists
  • Machine Learning Engineers
  • AI/ML Researchers
  • Data Analysts
  • IT Professionals with an interest in machine learning
  • Cloud Solutions Architects
  • Software Engineers looking to specialize in machine learning
  • DevOps Engineers focusing on machine learning pipelines
  • Technical Team Leaders managing machine learning projects
  • Product Managers overseeing ML-based products
  • Consultants providing machine learning solutions
  • Technical Sales Professionals involved in AI/ML solutions


Learning Objectives - What you will Learn in this Microsoft Cloud Workshop: Machine Learning?

Introduction to Course Learning Outcomes and Concepts Covered

This course empowers students with hands-on experience in designing and deploying machine learning solutions using Microsoft Cloud technologies, with a focus on real-world applications and best practices.

Learning Objectives and Outcomes

  • Understand the customer case study to align machine learning solutions with business requirements.
  • Design a proof of concept for a machine learning solution that addresses a real-world problem.
  • Develop presentation skills to effectively communicate the designed solution to stakeholders.
  • Acquire practical skills in creating forecast models using Azure's automated machine learning capabilities.
  • Learn to interpret machine learning models and their predictions using model explainability tools.
  • Gain experience in building and training deep learning models, specifically Recurrent Neural Networks (RNNs), for time series analysis.
  • Master the process of registering and managing machine learning models within the Azure ecosystem.
  • Implement techniques for real-time scoring of streaming data using trained forecast models.
  • Construct deep learning models for text classification, enhancing the ability to process and analyze textual data.
  • Explore best practices for deploying and operationalizing machine learning models in a cloud environment.