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Azure Databricks support both Classic and Deep Learning ML Algorithms to analyse Large DataSets at scale. The Integrated Notebook experience gives the Data Scientists and Data Engineers to do exploratory Data Analysis, also feels like native to Jupyter notebook users. In this session we will extract intelligence from Higgs Dataset (Particle Physics) by running Classic and Deep Learning models using Azure Databricks. We will also peek into AMl service's integration with Azure Databricks for managing the end-to-end machine learning lifecycle.
In this session we'll look at ETL metadata, use it to drive process execution, and see benefits quickly emerge. I'll show how a metadata-first approach reduces complexity, enhances resilience and allows ETL processing to become self-organising.
PowerShell loves Power BI. In this session we will take a look at how to manage your datasets, security, monitor licensing and more, all through the ultimate administration interface: PowerShell!
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