google map image

NetCom Learning
Memphis, TNX2 Training Center

5100 Poplar Avenue
Memphis, TNX2

Ideal for those pursuing Azure DP-100 in Memphis, Azure certification, or looking to advance through Azure training, this Azure Data Science certification course will prepare you to ace the Microsoft Certified: Azure Data Scientist Associate exam. Upgrade your skills in automating and managing data science models with Azure Machine Learning service.

DP-100 Course Objectives in Memphis

  • Doing Data Science on Azure
  • Doing Data Science with Azure Machine Learning service
  • Automate Machine Learning with Azure Machine Learning service
  • Manage and Monitor Machine Learning Models with the Azure Machine Learning service

google map image

NetCom Learning
Memphis, TNX2 Training Center

5100 Poplar Avenue
Memphis, TNX2

Upcoming Schedules

Exam and Certification

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

Who should attend Azure Data Science Course in Memphis

  • Data Scientist
  • AI Engineer

Prerequisites for Azure DP-100 Course in Memphis

Required

  • Creating cloud resources in Microsoft Azure.
  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
  • Working with containers

Data Science Solution on Azure Course Outline in Memphis

Design a machine learning solution
arrow iconarrow icon

  • Design a data ingestion strategy for machine learning projects
    • Identify your data source and format
    • Choose how to serve data to machine learning workflows
    • Design a data ingestion solution
  • Design a machine learning model training solution
    • Design a solution to get and prepare data.
    • Choose a service and compute to train a model.
    • Prepare for model deployment options.
  • Design a model deployment solution
    • Understand how a model will be consumed.
    • Decide whether to deploy your model to a real-time or batch endpoint.
  • Design a machine learning operations solution
    • Explore an MLOps architecture.
    • Design for monitoring.
    • Design for retraining.

Resources