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Designing and Implementing a Data Science Solution on Azure
Course Description
Overview
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 with Azure Machine Learning and MLflow.Objectives
Audience
Prerequisites
-
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 AzureIf you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
Topics
- Introduction
- Identify your data source and format
- Choose how to serve data to machine learning workflows
- Design a data ingestion solution
- Exercise: Design a data ingestion strategy
- Knowledge check
- Introduction
- Identify machine learning tasks
- Choose a service to train a machine learning model
- Decide between compute options
- Exercise: Design a model training strategy
- Knowledge check
- Introduction
- Understand how model will be consumed
- Decide on real-time or batch deployment
- Exercise - Design a deployment solution
- Introduction
- Explore an MLOps architecture
- Design for monitoring
- Design for retraining
- Knowledge check
- Introduction
- Create an Azure Machine Learning workspace
- Identify Azure Machine Learning resources
- Identify Azure Machine Learning assets
- Train models in the workspace
- Exercise - Explore the workspace
- Knowledge check
- Introduction
- Explore the studio
- Explore the Python SDK
- Explore the CLI
- Exercise-Explore the developer tools
- Knowledge check
- Introduction
- Understand URIs
- Create a datastore
- Create a data asset
- Exercise - Make data available
- Knowledge check
- Introduction
- Choose the appropriate compute target
- Create and use a compute instance
- Create and use a compute cluster
- Exercise - Work with compute resources
- Knowledge check
- Introduction
- Understand environments
- Explore and use curated environments
- Create and use custom environments
- Exercise - Work with environments
- Knowledge check
- Introduction
- Preprocess data and configure featurization
- Run an Automated Machine Learning experiment
- Evaluate and compare models
- Exercise - Find the best classification model
- Knowledge check
- Introduction
- Configure MLflow for model tracking in notebooks
- Train and track models in notebooks
- Exercise - Track model training
- Knowledge check
- Introduction
- Convert a notebook to a script
- Run a script as a command job
- Use parameters in a command job
- Exercise - Run a training script as a command job
- Knowledge check
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
- Exercise - Use MLflow to track training jobs
- Knowledge check
- Introduction
- Define a search space
- Configure a sampling method
- Configure early termination
- Use a sweep job for hyperparameter tuning
- Exercise - Run a sweep job
- Knowledge check
- Introduction
- Create components
- Create a pipeline
- Run a pipeline job
- Exercise - Run a pipeline job
- Knowledge check
- Introduction
- Log models with MLflow
- Understand the MLflow model format
- Register an MLflow model
- Exercise - Log and register models with MLflow
- Knowledge check
- Introduction
- Understand Responsible AI
- Create the Responsible AI dashboard
- Evaluate the Responsible AI dashboard
- Exercise - Explore the Responsible AI dashboard
- Knowledge check
- Introduction
- 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
- Exercise - Deploy an MLflow model to an online endpoint
- Knowledge check
- Introduction
- Understand and create batch endpoints
- Deploy your MLflow model to a batch endpoint
- Deploy a custom model to a batch endpoint
- Invoke and troubleshoot batch endpoints
- Exercise - Deploy an MLflow model to a batch endpoint
- Knowledge check
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Self-Paced Training Info
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- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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