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WeAreDevelopers LIVE • Mar 25, 2022

Introduction to Azure Machine Learning

Jose Luis Latorre Millas

Ditch weeks of hardware configuration and instantly provision on-demand GPU clusters. Azure Machine Learning accelerates your pipeline from automated feature engineering to cross-platform edge deployment.

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#1 about 4 min

Overcoming hardware configuration barriers in machine learning

Using pre-configured cloud environments eliminates the time-consuming process of setting up local machine learning infrastructure.

#2 about 5 min

Refreshing core concepts and workflows of artificial intelligence

Standardizing data extraction and training phases allows predictive models to correctly process complex data patterns natively.

#3 about 2 min

Navigating the components of Azure Machine Learning platform

Leveraging integrated cloud frameworks prevents experimental slowdowns by centralizing models and compute resources into scalable solutions.

#4 about 3 min

Structuring the backend architecture of machine learning workspaces

Connecting dependency resources like key vaults and container registries creates a secure foundation for running predictive endpoints.

#5 about 3 min

Creating machine learning workspaces in the Azure portal

Provisioning resource pools directly through the portal streamlines the deployment of comprehensive organizational machine learning backends.

#6 about 5 min

Managing virtual infrastructure within Azure Machine Learning Studio

Assigning multi-tenancy quotas and GPU-backed clusters ensures team operations scale precisely to complex neural network requirements.

#7 about 7 min

Visualizing data pipelines using Azure Machine Learning Designer

Utilizing an interactive drag-and-drop workspace simplifies the visual validation of normalization functions and dataset splittings.

#8 about 9 min

Automating optimal parameter selection using automated machine learning

Deploying intelligent evaluation mechanisms dynamically isolates top-performing parameters across multiple class-balancing algorithms without manual intervention.

#9 about 10 min

Developing programmatic training workflows using Python Jupyter Notebooks

Integrating interactive code documentation directly within computational clusters provides engineers with precise programmatic control over model configuration.

#10 about 3 min

Standardizing interoperable model deployments with the ONNX framework

Packaging training iterations inside a uniform execution framework creates portability across dissimilar platform architectures and edge devices.

#11 about 8 min

Exploring pathways into the machine learning engineering field

Utilizing managed algorithmic service APIs provides immediate analytical functionality for teams without requiring dedicated data scientists.

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