I’ll be sharing some experiences and insights around Machine Learning, Computer Vision and IoT. Here are my session details.
How a PoC at home can scale to Enterprise Level using Custom Vision APIs (v2!)
It all started with a DIY project to use Computer Vision for security cameras at home. A custom Machine Learning model is the core component used to analyze pictures to detect people, animals and more in a house environment. The AI processing is performed at the edge, in dedicated hardware and the collected information is stored in the cloud.
The same idea can be applied to several CCTV scenarios, like parking lots, train stations, malls and more. However, moving this into enterprise scale brings a set of challenges, which are going to be described and explained in this session.
In this new version of the session, we will start from scratch and create a complete “Parking Garage Open Space Tracker” solution with live devices and live cars (small ones, of course)
It was a placer to share some amazing time with the Mississauga .Net User Group last night in my last session of the decade. It was a full night focused on Artificial Intelligence and Machine Learning, and as usual is time to share the resources used in the session.
With the massive volumes of data generated today about every aspect of a business finding deep insights from the data can be challenging. This session will introduce how to incorporate the sophisticated pre-trained machine learning models into Power BI.
AI and Cognitive Services provide powerful ways to extract actionable insights from a variety of unstructured sources like documents, images, and social media feeds through Azure Services like Sentiment Analysis, Key Phrase Extraction, Language Detection, and Image Tagging.
Getting Started with Machine Learning.Net and AutoML
Machine Learning has moved out of the lab and into production systems. Understanding how to work with this technology is one of the essential skills for developers today. In this session, you will learn the basics of machine learning, how to use existing models and services in your apps, and how to get started with creating your own simple models. And if you are a .Net developer, we will cover the basis of Machine Learning.Net, a complete ML framework to work with C#, F# or any other .Net Core language.
"Cybertelligence": Using AI to Fight the Dangers of Cyber Threats
Cyber threats are considered the top priorities for businesses nowadays, with Cybersecurity Ventures predicts cybercrime will cost the world in excess of $6 trillion annually by 2021 (up from $3 trillion in 2015), the challenge becomes bigger and the human resources to fight the threats are not catching up. The need of AI based automated cybersecurity solutions that will help human cybersecurity experts in this battle is becoming more of a necessity than complimentary. This talk will concentrateon the role of AI in fighting cyber crimes.
Topics: 1- The OODA loop 2- Brief history of cyber crime combat 3- Dark Web and AI combat 4- Mass Surveillance and AI 5- Breaking RSA with AI and Quantum Computing (Shor's Algorithm) 6- The future of AI protection with Quantum Keys 7- The Battle with AI Biases in Cybersecurity 8- AI vs Human in Security, who wins? 9- Q & A
Machine learning is a data science technique to use massive historical data to forecast future behaviors, outcomes, and trends without being explicitly programmed. This session will introduce how to incorporate Machine Learning predictive models into Power BI to gain better predictions and insights about the future.
The ability to visualize and invoke insights from these models, in your reports and dashboards and other analytics, can help disseminate these insights to the business users who need it the most. This makes collaboration among business analysts and data scientists easier and faster than ever before.
Azure Machine Learning Services is a one-stop shop to train, deploy, automate, manage, and track your machine learning models. You can do that in Python (PyTorch, TensorFlow, and scikit-learn), R, or zero-code/low-code options. At the beginning, we will spend few minutes to familiarize ourselves with the ML/Data Science practice in general. Then, I will present a few examples of Python and zero-code options to build and deploy machine learning models, including Auto-ML (Automated Machine Learning) in Azure. Towards the end of the session, I will show-case how to consume a ML model from within a Power BI report. This session is appropriate for cloud developers/architects who are new to Machine Learning, but also for data scientist and data engineers who are new to Azure
Azure Cognitive Speech Services Azure Speaker Recognition Azure CLI scripts/code Live Demos 1-Voice Identification (Recording few voices) 2-Voice Verification (who was speaking) 3-Voice Verification in different language
AI is definitely the new buzz in the tech world. Everybody wants it, everybody's using it (even unknowingly). AI solved a lot of issues and changed our lives for better, but, years into experiencing AI we know now that there is a downside of using it. Mostly an ethical one. So, do we drop the whole "AI thing"? Keep it as it is and accept the downside? Or proceed with caution? This is what this session is trying to highlight.
Topics: 1- AI Ethics: Why important? Why Now? 2- The dilemma and the major issues. 3- Machine Biases (the goo, the bad and the ugly) 4- The Moral Algorithm (Interpretability and Transparency) 5- Major Bias Cases 6- Artificial Consciousness as a Solution (Advancements and Challenges) 7- Conclusion and Suggestions 8- Q & A
Enriching the Power Platform with AI capabilities through AI Builder
Combining all three of the aspects of the Microsoft Power Platform – Power Apps, Microsoft BI & Power Automate and enriching them with advanced technology like AI builder gives businesses the required edge to stay relevant in the world of automation and artificial intelligence.
Microsoft’s Power Platform consists of three services Power BI, Power Apps and Flow. All three of these services helps users in their respective disciplines. With the help of Power Platform, users can integrate the capabilities of the three platforms to create streamlined solutions.
Users can now make their workflows and applications more efficient with the help of AI Builder. The scenario will allow businesses to become more intelligent and enable them to align their operations with the dynamic needs of modern consumers.
In this session, you will learn -How can you start using AI-Builder for your business? -Using AI-Builder with Power BI, Power Apps and Power Automate
The partner opportunity for AI is worth a whopping $118 billion,* and I want to help you make the most of it. Join me and SYNNEX for a webinar on Thursday, December 12, at 2 PM ET where I’ll show you how you can capitalize on Azure AI solutions that solve real-world problems for your customers.
What makes the AI opportunity so profitable for partners
How to create apps that see, hear, speak, and understand with solutions like Azure Cognitive Services and Power Apps
Steps for implementing AI and machine learning
Plus, I’ll provide demos of key solutions—you’ll see how easy it is to create a QnA bot with Azure Cognitive Services, or machine learning solutions with Power Apps. Register today!
*Tractica. Artificial Intelligence Software Market to Reach USD$89.8 Billion in Annual Worldwide Revenue by 2025. 2017.
Tailwind Traders has a lot of legacy data that they’d like their developers to leverage in their apps – from various sources, both structured and unstructured, and including images, forms, and several others. In this session, learn how the team used Azure Cognitive Search to make sense of this data in a short amount of time and with amazing success. We discuss tons of AI concepts, like the ingest-enrich-explore pattern, search skillsets, cognitive skills, natural language processing, computer vision, and beyond.VIEW MORE
Using pre-built AI to solve business challenges
As a data-driven company, Tailwind Traders understands the importance of using artificial intelligence to improve business processes and delight customers. Before investing in an AI team, their existing developers were able to demonstrate some quick wins using pre-built AI technologies. In this session, we show how you can use Azure Cognitive Services to extract insights from retail data and go into the neural networks behind computer vision. Learn how it works and how to augment the pre-built AI with your own images for custom image recognition applications.VIEW MORE
Start building machine learning models faster than you think
Tailwind Traders uses custom machine learning models to fix their inventory issues – without changing their software development life cycle! How? Azure Machine Learning Visual Interface. In this session, learn the data science process that Tailwind Traders’ uses and get an introduction to Azure Machine Learning Visual Interface. See how to find, import, and prepare data, select a machine learning algorithm, train and test the model, and deploy a complete model to an API. Get the tips, best practices, and resources you and your development team need to continue your machine learning journey, build your first model, and more.VIEW MORE
Taking models to the next level with Azure Machine Learning best practices
Tailwind Traders’ data science team uses natural language processing (NLP), and recently discovered how to fine tune and build a baseline models with Automated ML. In this session, learn what Automated ML is and why it’s so powerful, then dive into how to improve upon baseline models using examples from the NLP best practices repository. We highlight Azure Machine Learning key features and how you can apply them to your organization, including: low priority compute instances, distributed training with auto scale, hyperparameter optimization, collaboration, logging, and deployment.VIEW MORE
Machine learning operations: Applying DevOps to data science
Many companies have adopted DevOps practices to improve their software delivery, but these same techniques are rarely applied to machine learning projects. Collaboration between developers and data scientists can be limited and deploying models to production in a consistent, trustworthy way is often a pipe dream. In this session, learn how Tailwind Traders applied DevOps practices to their machine learning projects using Azure DevOps and Azure Machine Learning Service. We show automated training, scoring, and storage of versioned models, wrap the models in Docker containers, and deploy them to Azure Container Instances or Azure Kubernetes Service. We even collect continuous feedback on model behavior so we know when to retrain.VIEW MORE
Download all the slides and videos
And, finally if you want all these sessions material, just
Access “Get the bulk session resource download script” at the bottom of the page in one of the sessions.
Open a PowerShell window to the directory in which the script is located.