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Add your ODL content here.

Price: $295.00

Train your entire team with our Private Group Training solutions. AstraTech specializes in creating customized training programs to fit your organization’s unique learning needs and business objectives. Take advantage of multiple student discounts, customized curriculum and flexible scheduling with Private Group Training from AstraTech!

    On-Demand Learning (ODL) is a self-paced training solution designed to work around your individual schedule, budget, and learning needs. Our ODL courses provide you with exclusive access to interactive platforms combining high-quality instructor videos from subject matter experts, official courseware and study materials, hands-on labs, practice exercises, skill assessments and knowledge checks. The modular, self-paced ODL course structure adapts to your unique learning needs and style, providing an optimal training experience.

    Train your entire team with our Private Group Training solutions. AstraTech specializes in creating customized training programs to fit your organization’s unique learning needs and business objectives. Take advantage of multiple student discounts, customized curriculum and flexible scheduling with Private Group Training from AstraTech!

    DP-3007 Train and deploy a machine learning model with Azure Machine Learning

    $295 - $695 | 1 day

    Upgrade your tech skills with this , training course. Launch your career to the stars with AstraTech IT Certification Training!

    Please select a Learning Method below to get started:

    There are currently no upcoming course dates scheduled. Please complete the form to request a date/receive schedule update notifications:

      • Microsoft Azure
      • Microsoft Technical

      About this Course

      SKILL LEVEL: 
      • Intermediate
      TOPICS: 

      DP-3007 Train and deploy a machine learning model with Azure Machine Learning Course Outline

      To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.

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      To maximize the benefits of this course, participants should have familiarity with the data science process. While the course doesn’t delve deeply into data science concepts, a basic understanding is recommended. Additionally, familiarity with Python is essential, as the course focuses on utilizing the Python SDK for interacting with Azure Machine Learning.

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      1 – Make data available in Azure Machine Learning

      • Understand URIs
      • Create a datastore
      • Create a data asset

      2 – Work with compute targets in Azure Machine Learning

      • Choose the appropriate compute target
      • Create and use a compute instance
      • Create and use a compute cluster

      3 – Work with environments in Azure Machine Learning

      • Understand environments
      • Explore and use curated environments
      • Create and use custom environments

      4 – Run a training script as a command job in Azure Machine Learning

      • Convert a notebook to a script
      • Run a script as a command job
      • Use parameters in a command job

      5 – Track model training with MLflow in jobs

      • Track metrics with MLflow
      • View metrics and evaluate models

      6 – Register an MLflow model in Azure Machine Learning

      • Log models with MLflow
      • Understand the MLflow model format
      • Register an MLflow model

      7 – Deploy a model to a managed online endpoint

      • Explore managed online endpoints
      • Deploy your MLflow model to a managed online endpoint
      • Deploy a model to a managed online endpoint
      • Test managed online endpoints