Structure the development workflow
The talk considered how Azure Machine Learning could support training, deployment, and monitoring with Databricks.
Archived Sydney meetup · February session
This community session has finished. Its record covers the development workflow, model-hosting choices, and Databricks platform topics published for the evening.
Archive status
The page names a February session but does not keep its calendar date. It does keep the evening times and speaker names.

Technical agenda
The topics reflect the Microsoft and Databricks tools available at the time.
The talk considered how Azure Machine Learning could support training, deployment, and monitoring with Databricks.
Kubernetes, standalone containers, and serverless options formed the published comparison set.
The agenda covered the handoff between data scientists and machine-learning engineers.
The closing presentation described the core pillars of the Databricks service offering at the time.
Published presenters
The page keeps the speakers named in the schedule. Their roles may have changed.
Review our consulting scope or browse the event archive for other technical sessions.
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