Modernise Your Analytics Platform with Databricks and Azure
Executive Networking Breakfast & Learn
Dates TBA for 2021 (please register your interest to get notified)
Four Seasons Hotel, Sydney
About the Event
Building a modern data platform and incorporating intelligence into your applications and analytics is unfamiliar territory for many.
Advanced analytics can give you a competitive advantage and unlock new insights in your data that was not previously possible without major investment and effort.
Join us for breakfast to learn how your organisation can modernise its analytics platform using scalable and secure cloud-based technology on Microsoft Azure.
The benefits of a modern data platform architecture:
At this session we’ll demonstrate:
This event is intended for: CIOs, CTOs, IT Managers, CDAOs, and other related roles.
After the session you will have a clear idea of the technical landscape and options available to deliver a modern analytics platform to allow you
to focus on valuable business insights.
8:30 AM – 9 AM: Registration, Networking & Breakfast
9 AM – 10:30 AM: Expert-Run Sessions
10:30 AM – 11 AM: Networking & Morning Tea
Date and Venue
Databricks is a managed Spark service which adds a notebook environment, git integration and unified analytics platform backed by the power and huge scale of Spark. No more building and maintain Spark clusters! Databricks was started by the creators of the Spark project so there is a close connection which results in a better product. It’s easy to get started with Databricks with it’s elastic, PAYG pricing, whereas a Spark cluster would take significant investment and resources to set up before it could be used.
ML Ops is DevOps for a Machine Learning Project (sometimes incorrectly labelled as an AI project). A DevOps culture is important to increase the speed of iterations, time to market, ability to change quickly and monitor deployed code. It greatly improves quality and an ML project is no different.
It’s easy to turn your organisations data repository into a dumping ground filled with duplicate, stale or invalid data. There’s a number of steps to take to prevent this starting with a good data lake design, data governance and data lineage.
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