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Designing and Implementing a Data Science Solution on Azure

ICT/ IT NETWORKING & CLOUD/ MICROSOFT PRIVATE CLOUD/
Designing and Implementing a Data Science Solution on Azure
ΗΜΕΡΕΣ & ΩΡΕΣ

On demand

Duration: 3 days

ΓΛΩΣΣΑ

Course Language: Greek

Content: English

ΠΡΟΫΠΟΘΕΣΕΙΣ ΣΥΜΜΕΤΟΧΗΣ

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. Specifically:

  • 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:

  • Explore Microsoft cloud concepts.
  • Create machine learning models.
  • Administer containers in Azure

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

Με λίγα λόγια

Learn how to 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.

Audience profile
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.

Job role: Data Scientist

Τι θα μάθετε

  • Module 1: Getting Started with Azure Machine Learning Module 9: Create Paginated Reports in Power BI
  • Module 2: No-Code Machine Learning
  • Module 3: Running Experiments and Training Models
  • Module 4: Working with Data
  • Module 5: Working with Compute
  • Module 6: Orchestrating Operations with Pipelines
  • Module 7: Deploying and Consuming Models
  • Module 8: Training Optimal Models
  • Module 9: Responsible Machine Learning
  • Module 10: Monitoring Models

ΚΑΤΗΓΟΡΙΕΣ ΣΕΜΙΝΑΡΙΩΝ