Yousry Mohamed
Lead Consultant at Cuusoo
Brisbane, Australia
Yousry is a lead consultant working for Cuusoo. He is very passionate about all things data including Big Data, Machine Learning and IoT.
His experience spans application development stack like C#, web technologies and integration in addition to big data and AI stack. In the last few years, he was mainly working on applications involving Spark, DataBricks, Data Factory & Azure IoT.
Area of Expertise
Topics
Video Analytics for mere mortals with NVIDIA DeepStream SDK
There are billions of cameras and sensors worldwide, capturing a flood of data that can be used to generate business insights, unlock process efficiencies and improve revenue streams.
Whether it’s at a traffic intersection to reduce vehicle congestion, health and safety monitoring at hospitals, surveying retail aisles for better customer satisfaction or at a manufacturing facility to detect component defects - every application demands reliable, real-time Intelligent Video Analytics (IVA).
In this session, you will learn about NVIDIA DeepStream SDK which helps developers deliver robust video analytics solutions without the need to reinvent wheels or know how to demystify a deep neural network design.
You will get to know how to:
- Use OOTB computer vision models
- Deploy solutions on edge devices like the NVIDIA Jetson family.
- Integrate with IoT products like Azure IoT Edge
- Handle real world requirements like event-based video recording, remote management and OTA update.
If you have a video analytics problem, you don't want to miss this session. In addition to the business use cases, it's lots of fun!
Tips and tricks for robust big data applications
Does anyone still care about big data? Some people may think it's an ancient buzzword that doesn't attract the cool kids in the era of self-driving cars.
Well, big data is still here and will continue to be relevant as long as people and businesses need to count things.
In this session, you will learn several tips and tricks critical to any successful big data application.
You will see how to optimise data storage in columnar files to save storage costs and also improve compute performance.
If garbage in means garbage out, then your data should be of high quality.
You will learn also how to unit test big data applications and how to assure high quality data outcomes.
With the explosion of data sources a single organisation has to handle, it's very critical to have governance and tracking.
You will see how you can track the lineage of a certain dataset so you will always be ready for the toughest auditing or compliance checks.
Finally, I will show you some tools to help with automation and collaboration.
Tips and tricks for robust big data applications
Does anyone still care about big data?
Some people may think it's an ancient buzzword that doesn't attract the cool kids in the era of self-driving cars.
Well, big data is still here and will continue to be relevant as long as people and businesses need to count things.
In this session, you will learn several tips and tricks critical to any successful big data application.
You will see how to optimise data storage in columnar files to save storage costs and also improve compute performance.
If garbage in means garbage out, then your data should be of high quality.
You will learn also how to unit test big data applications and how to assure high quality data outcomes.
With the explosion of data sources a single organisation has to handle, it's very critical to have governance and tracking.
You will see how you can track the lineage of a certain dataset so you will always be ready for the toughest auditing or compliance checks.
Finally, I will show you some tools to help with automation and collaboration.
Power BI for developers
Power BI has become one of the leading BI and visualization tools. You will learn more about Power BI as an app developer. We will cover embedding, CI/CD, unit testing, tooling and documentation.
Big data from the trenches: Demystifying the buzzword
The term big data might sound old-fashioned now in the age of deep learning and thinking machines. Guess what, it's more important even than before.
Have you ever faced the dilemma of nvarchr(4000) vs nvarchar(max)? Have you ever had two datasets/arrays and were asked to see if they are similar or not? Can you tell at least one anecdote about an automated process done using bash/PowerShell script where some parts failed silently and how hard it was to troubleshoot them and dig inside tons of log files?
These problems also happen in big data applications in one form or another and their scale and complexity is even much worse. Taking the learning and experience from real world big data applications, you will learn how such challenges are managed in big data world.
You will learn about file formats used to support storage of petabytes of data while also allowing quick search. You will see how statistics and data visualisation are used to inspect inputs or confirm conclusions about final outputs. You will appreciate the value of orchestration and monitoring tools used to manage sophisticated processes that span days or even weeks. You will learn how tools like Spark are designed to recover from failures in such distributed environments built on commodity hardware.
By the end of this session, you will have a high level understanding of how big data applications are developed and operated and where to start your big data journey with tools like Hadoop, Hive and Spark.
AI on the edge: aka Sharp Blade Technology
IoT has started to gain traction in the last few years. Still, the initial premise was to collect device measurements or telemetry and submit them for offline analysis on another machine or on the cloud.
With the introduction of devices like NVIDIA Jetson, Google Coral and Intel neural compute stick; devices have now enough power to do advanced analytics without the dependency on the cloud or even an internet connection. This has several benefits related to latency, security and overall solution cost.
In this demo-rich session, we'll see how to do AI on edge devices using Azure IoT Edge platform. You will learn how to bring your own models or open source ones and run them on the device. Sync-ing data whether it's raw data or prediction results to a reporting dashboard is a very common use case that we will explore as well. By the end of the session, you will be ready to get started building your own smart IoT solutions.
Azure Machine Learning done right!
Disclaimer: this isn't about the old classic drag and drop ML studio!
Developing machine learning models or data science applications on Microsoft stack is not a very nice experience. The main tool is Azure Machine Learning Studio and it doesn't provide a good story for DevOps, Source Control or using open source ML tools/IDEs heavily used by data scientists.
A new cloud service has been recently added to Azure to solve those problems and make ML and app development go hand in hand and in harmony.
Meet Azure Machine Learning Service! It's a one-stop shop that you can use to develop, deploy and manage machine learning models.
In this demo-packed session, we will cover the main features of this service. We will start with basic topics by developing a simple model and deploying it as web service in a CI/CD fashion. Then we will move to more advanced use cases like automatic model selection, consuming ONNX models and distributed model training.
P.S. If you wonder if this is the ML workbench tool? Not exactly as the workbench has been deprecated and replaced with this cloud service along with its SDK & APIs.
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