Data Science
Showing 469–480 of 1577 results
Deploy a model to a batch endpoint
Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you'll trigger a batch scoring job.
Deploy a model to a managed online endpoint
Learn how to deploy models to a managed online endpoint for real-time inferencing.
Deploy a model with GitHub Actions
Learn how to automate and test model deployment with GitHub Actions and the Azure Machine Learning CLI (v2).
Deploy an Azure Machine Learning model to a managed endpoint with CLI (v2)
Use the Azure Machine Learning CLI (v2) to deploy a machine learning model to a managed online endpoint.
Deploy and consume models with Azure Machine Learning
Learn how to deploy a model to an endpoint. When you deploy a model, you can get real-time or batch predictions by calling the endpoint.
Deploy deep learning workloads to production with Azure Machine Learning
Deploying large-scale models for real-time inferencing is challenging because of the model's size. Learn what you can do and which frameworks you can use to optimize your model's performance during model scoring.
Deploy IaaS solutions with Azure SQL
Configure virtual machine sizing, storage, and networking options to ensure adequate performance for your database workloads. Choose and configure appropriate high availability options.
Deploy PaaS solutions with Azure SQL
Provision and deploy Azure SQL Database and Azure SQL managed instance. Select the appropriate options when performing a migration to the SQL PaaS platform.
Deploy workloads with Azure Databricks Workflows
Deploying workloads with Azure Databricks Workflows involves orchestrating and automating complex data processing pipelines, machine learning workflows, and analytics tasks. In this module, you learn how to deploy workloads with Databricks Workflows.
Deploying and Publishing Power BI Reports
Power BI is an intuitive business analytics tool. This course will cover all the different ways to deploy and publish Power BI reports, starting at five users and scaling up to 5000.
Deploying Machine Learning Solutions
This course covers the important conceptual reasons why models underperform post-deployment, the actual implementation of model deployment using Python Flask, using serverless, cloud-based compute options and using platform-specific machine learning frameworks.
Deploying PyTorch Models in Production: PyTorch Playbook
This course covers the important aspects of performing distributed training of PyTorch models, using the multiprocessing, data-parallel, and distributed data-parallel approaches. It also discusses which you can host PyTorch models for prediction.