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Elevate Your Team's SkillSet on Azure's Advanced Features,
from Model Creation to Deployment.

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Participants will gain comprehensive understanding of Azure and machine learning, enabling them to create, deploy, and monitor ML models. With a focus on advanced features like hyperparameter tuning and Automated ML, integration with Azure DevOps, and real-world case studies, participants gain practical skills and industry-relevant insights for effective implementation.

The course modules are structured to give an empirical value and understanding to the candidates. However, all course modules are highly customizable and can be structured to suit the requirements of your organization.

Course Outcome

Participants will gain a comprehensive understanding of Microsoft Azure, its services, and how to apply machine learning within the Azure environment.
The training provides participants with practical skills for creating, training, and deploying machine learning models using Azure services.
Participants will be proficient in utilizing advanced features such as hyperparameter tuning, Automated Machine Learning, and the creation of Azure ML pipelines.
The integration with Azure DevOps emphasizes the importance of incorporating machine learning into standard development and deployment practices.

Why Mazenet?

  • Expert Faculty

    Our Faculty comprises of 300+ SMEs with many years of experience. All our trainers possess a minimum of 8+ years of experience.

  • Proven Track Record

    We have served over 200+ global corporate clients, consistently maintaining a 99% success rate in meeting training objectives for 300+ technologies with quick turnaround time.

  • Blended Learning

    We provide course content over any platform that our clients prefer. You can choose an exclusive platform or a combination of ILT, VILT, and DLP.

  • Learning Paths

    The learning paths are very defined with clear benchmarks. Quantitative assessments at regular intervals measure the success of the learning program.

  • Case Study

    We have amassed over 10,000 case studies to support training delivery. Candidates will be trained to work on any real-time business vertical immediately after the training.

  • 24*7 Global Availability

    We are equipped to conduct training on any day, date or time. We have delivered training pan India, Singapore, North America, Hong Kong, Egypt and Australia.

Key Features

  • Customized Training Modules

    Training programs are highly flexible with module customizations to suit the requirements of the business units.

  • Certification

    The training can be supplemented with appropriate certifications that are recognized across the industry.

  • Multi-language Support

    Course content can be delivered in English, Spanish, Japanese, Korean or any other language upon request.

  • Personalized Training Reports

    Candidates are assessed individually at regular intervals and are provided unique learning suggestions to suit their learning calibre.

  • Industry-Oriented Training

    Industry-oriented training, completing which, candidates can be immediately deployed for billable projects.

  • Diverse Training Platforms

    Choose from Instructor-Led Training, Virtual Instructor-Led Training, Digital Learning Platform and Blended Training platforms

Course Preview

  • Introduction to Microsoft Azure and its services
  • Overview of machine learning and its applications
  • Azure Basics
  • Understanding the Azure portal
  • Setting up an Azure Machine Learning workspace

  • Core Concepts
  • Introduction to Azure Machine Learning Studio
  • Understanding datasets and experiments
  • Model Training and Deployment
  • Creating and training a basic machine learning model
  • Deploying a model in Azure

  • Hyperparameter Tuning
  • Exploring hyperparameter tuning in Azure ML
  • Optimizing model performances
  • Automated ML

  • Hands-on session with Automated ML

  • Azure ML Pipelines
  • Understanding and creating Azure ML pipelines
  • Orchestrating end-to-end machine learning workflows
  • Integration with Azure DevOps
  • Integrating machine learning into the DevOps lifecycle
  • Continuous integration and deployment for machine learning models

  • Model Monitoring
  • Setting up monitoring for deployed models
  • Handling data drift and model drift
  • Scaling in Azure ML
  • Strategies for scaling machine learning workloads
  • Exploring Azure Machine Learning Compute

  • Industry Use Cases
  • Case studies of real-world applications of Azure Machine Learning.
  • Best practices and lessons learned from industry implementations.