Richard Conway is a founder of cloud data consultancy Elastacloud specializing in big data on Microsoft Azure. He is a programmer and author of 22 years, having traversed languages such as C++, C#, Java, and more recently Scala, Python, and R. He is a regular contributor to open source projects including the Apache stack. In his spare time he runs the UK Azure Users Group covering all aspects of the Microsoft cloud and the popular kids programming event AzureCraft helping kids to build AIs in Minecraft.
November 18, 2020 04:00 PM PT
Data Protection is still at the forefront of multiple companies minds with potential GDPR fines of up to 4% of their global annual turnover (creating a current theoretical max fine of $20bn). GDPR effects countries across the world, not just those in Europe, leaving many companies still playing catch up. Additional acts and legislation are coming into place such as CCPA meaning Data Protection is a constantly evolving landscape, with fines that can literally decimate some business. In this session we will go through how we have worked with our customers to create an Azure and AWS implementation of a Data Protection Engine covering Protection, Detection, Re-Identification and Erasure of PII data.
The solution is built with Security and Auditability at the centre of the Architecture, with special consideration for managing a single application across two public clouds; leading us to using Databricks, Delta Lake, Kubernetes and PowerBI. We will deep dive into using Spark to create multiple techniques of Data Protection and how using AI can start to become a game changer in Detecting PII that has been missed in Data. Exploring how Delta Lake empowers us to share PII tokens between cloud providers with ACID transactions, auditing and versioning of data.
With a final look at how Deep Neural Networks can be used to Detect PII within Data, this will be demo packed session. We hope this session shows you that Data Protection doesn't have to be an off the shelf black box, but you can own the risk and solution within your own platform, whilst still remaining secure and compliant.
Speakers: Sandy May and Richard Conway
October 3, 2018 05:00 PM PT
Renewables AI is at the forefront of innovation in the solar energy market. As the name suggests, we use AI to make predictions on energy output from large portfolios of solar farms. This talk lays out the fundamental architecture, technology and approaches that make the platform work beginning with key features of the Azure Databricks cloud and how it works seamlessly with Azure Data Lake and Azure Event Hubs. There will be good coverage of ML and DL Pipelines and how they are used with image recognition and machine learning through Structured Streaming to make real-time decisions.
Key Takeaways: Prediction of next day irradiance and power ratios with real-time accuracies of 95% Structured streaming of IoT data from hundreds of thousands of inverters at 5 minute intervals Real-time joining of weather data and several other external datasets Use of Deep Learning Pipelines and advanced time series methods to predict 48 hours of future energy production Near-real time processing of image data at frequent intervals to predict cloud cover from onsite cameras and drones Analysis of data and preventative maintenance of fan failures in solar inverters
Session hashtag: #SAISDD11