Accelerating Machine Learning on Databricks

Learn how you can combine CARTO and Python for spatial data science from the comfort of your own Jupyter notebook. In this technical webinar, Andy Eschbacher  (Data Scientist at CARTO) and Joe Pringle (VP - North America at CARTO) show how to apply CARTOframes and CARTO's Python SDK to build powerful end-to-end spatial data science workflows. This webinar specifically focus on two areas: Using CARTO in a Jupyter notebook. Demo examples of custom data science web applications built with CARTOframes and CARTO VL.
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Spotlight

OTHER ON-DEMAND WEBINARS

How to Operationalize Data Mapping for Engineering

In this webinar, we will discuss the importance of data maps in building a privacy program. We look at the gap between privacy and engineering teams, the problems of scaling privacy programs with a business process approach to data mapping, and how an application-led approach helps you develop dynamic & complete data maps.
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Automated Data and Analytics Workload Modernization

Moving on-premises legacy data and analytics workloads to the cloud is unavoidable if you want to overcome infrastructural constraints, facilitate proactive analytics, and lower costs. You need a service that enables seamless scalability for petabyte-scale data processing, interactive analytics, and machine learning. However, end-to-end, automated workload transformation and optimization on serverless services is not straightforward.
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Experience limitless analytics with Azure Synapse Analytics

View this webinar where our experts discussed the new era of analytics with the Microsoft Azure Synapse Analytics platform. It is a limitless analytics service with unmatched time to insight that bring together data integration, enterprise data warehousing and big data analytics – all into a single service.
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Expert Panel: The Importance of Governance in Data Modernization

Data modernization, especially through cloud migration, is critical to realizing value from today’s fast, diverse, and high-volume data. Modernization is about overhauling legacy data management and practices that hold organizations back from achieving business goals, improving resilience, and reducing risk. However, often overlooked in the rush to develop new applications and migrate data is modernizing data governance. Poor attention to data governance will bake problems into overall modernization efforts that become hard to correct, increase risks, and ultimately reduce the value of data.
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