Sam Nasr
Sr. Software Engineer, Trainer (NIS Technologies)
Cleveland, Ohio, United States
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Sam Nasr has been a software developer since 1995, focusing mostly on Microsoft technologies. He’s a Sr. Software Engineer with NIS Technologies where he consults and teaches clients about the latest .NET technologies. Sam has achieved multiple certifications from Microsoft (MCSA, MCAD, MCTS, MCT), and is the leader of the Cleveland C# since 2003. In addition, he’s the leader of the .NET Study Group, Azure Cleveland User Group, an author for Visual Studio Magazine, and a 7x Microsoft MVP. When not coding, Sam loves spending time with his family and friends or volunteering at his local church. You can learn more about Sam by visiting https://linktr.ee/SamNasr.
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Making Apps Converse in Natural Language
Natural Language Understanding (NLU) is part of Azure AI Services. It's built on the interactive machine learning and language understanding research from Microsoft Research. NLU provides the capability to understand a person’s natural language and respond with actions specified by application code. In this session we'll examine how this powerful feature can be integrated into applications, offering a more natural interaction with a device.
• Basic concepts of Language Understanding Intelligent Service
• Integrating custom code with Azure AI
• Leveraging existing data for use with AI features
Build Your Own Copilot
Microsoft Copilot was launched in February 2023 as a chatbot for coding assistance. The application has since grown from providing GitHub coding examples and permeated into a variety of platforms, such as Microsoft 365, Power Platform, and Windows. Now it offers the ability for professional software developers to create custom copilot experiences using natural language or a graphical interface. In this session, we’ll explore Copilot from basic to advanced and the tools available for creating a rich user experience.
Creating ML models using Azure Cosmos DB
Automated ML is an emerging field in Machine Learning that helps developers build Machine Learning models and solutions without understanding the complexity of Learning Algorithms, and Hyper parameter tuning. With Azure Machine Learning's automated machine learning capability, given a dataset and a few configuration parameters, you will get a trained high quality Machine Learning model for the dataset that you can use for Predictions. You will learn how to use it for productivity gains, and empowering domain experts.
Introduction to AI for Developers
Artificial Intelligence (AI) is a vast and rapidly expanding field. Like any new technology names and concepts can get confusing. In this session we’ll look at the building blocks of AI and discuss the differences between AI, Machine Learning, and Deep Learning. We’ll explore how to get started using AI, as well as various product offerings from Microsoft and how they can be applicable to developers in various business verticals. This session will serve as an overview, as well as writing code to utilize Machine Learning features.
Into to Azure AI Content Safety
Azure AI Content Safety is a new service that helps you detect and filter harmful content, wether it's user-generated or AI-generated content. It can monitor both text and image context across categories and languages, providing severity scores to help prioritize content for review. This new service can be leveraged through our API, or interactive studio, as well as through Azure OpenAI, Copilot and Bing.
Data Insights with AI in Fabric
By leveraging AI within the Fabric platform, organizations can unlock deeper insights from their data, driving more informed decision-making and fostering innovation. We’ll delve into key AI techniques used in Fabric, such as machine learning, natural language processing, and predictive analytics. Attendees will gain an understanding of how these technologies enhance data processing, uncover hidden patterns, and provide actionable recommendations. Join us to discover how AI-powered data insights in Fabric can revolutionize your approach to data-driven strategies.
The Good, the Bad, and the Ugly of AI
Artificial Intelligence (AI) is a vast and rapidly expanding field. Looking around us, it appears every facet of our life is influenced by AI or soon will be. In this session, we'll discuss the fundamentals of AI and Machine Learning and explore its advantages, disadvantages, as well as the ugly side when this technology is misused. This session will serve as an overview for the general public, as well as hobbyists interested in learning more.
Overview of Azure Speech Services
Speech Services is one of five categories offered by Azure Cognitive Services. Within the Speech category, there are four services:
• Speech to Text
• Text to Speech
• Speech Translation
• Speaker Recognition
During this session, we’ll explore how each service works and requirements for proper setup and use. In addition, we’ll look at ways these services can be combined with other services to provide a richer experience.
Overview of Azure AI Services
Azure AI Services provide the ability for applications to interact with users with the human touch.
By using these services, applications now have the ability to see, hear, speak, and understand human interactions. This is made accessible by a set of rich APIs for use in web, desktop, or mobile applications in virtually every language. In this presentation, we’ll discuss the four major categories of Azure AI Services, and briefly view the rich features within each one.
Intro to Spark Machine Learning in Azure Synapse
Many organizations utilize more diverse products and services to accomodate their end users. This creates various distributed data models where Machine Learning would be needed. The Spark Machine Learning library provides the ability for developers to focus on the Machine Learning model using distributed data, while abstracting away the complexities surrounding it such as configuration or infrastructure.
Azure Synapse Analytics is a service that brings together data integration, enterprise data warehousing, and big data analytics. It allows data to be queried using either serverless or dedicated options—at scale. Azure Synapse also has the ability to incorporate Machine Learning solutions with Spark. In this session, we'll discuss how this functionality can be implemented as part of a Azure Data solution.
Data Cleansing using Data Bricks
Machine Learning is highly dependent on adequate data. Not only does quantity matter, but more importantly quality. In this session we’ll cover how to build a custom automated process using Data Bricks. This will provide methods for cleaning data in a data lake using functions in Azure.
Storing and Searching Files in SQL Server Filetables
FileTables is a SQL Server features that allows easy storage and access to documents dynamically. After some initial setup, users can simply drag files into a folder and then be able to access their meta-data and contents through SQL Server, seamlessly. Due to the way FileTable uses the FileStream feature, it stores files on the file system but maintains the metadata in the database. This allows full control over the file while not bloating the database.
SQL Server Security Features
SQL Server provides several new security features for developers and architects. Features such as Dynamic Data Masking (DDM), "Always Encrypted", and Row-Level Security provide an additional level of security natively through the database server. We'll explore the implementation of these features on the client/server for data in transmission or at rest. In addition, we'll examine built-in features and custom implementations.
Adding Machine Learning to .Net Applications
Machine Learning has been gaining wide acceptance due to its ability to make a determination in various scenarios based on specific training. It is now available to .Net developers for integration into applications. This allows it to be effective and ubiquitous in the enterprise. .Net developers can now incorporate machine learning into line-of-business applications using Visual Studio and C#. This session will walk you through the fundamentals of creating an ML integrated application and ongoing model training.
Artificial Intelligence Programming with T-SQL
Artificial Intelligence has been available to developers via API or libraries for integration with Code. Now SQL Server offers Artificial Intelligence via T-SQL. In this session we'll look at specifying a model for making decisions about time series data, programming the model using T-SQL, and evaluating the results of the model.
Building Decision Intelligence into Applications
Most web applications simply provide the content for the user along with a standard list of links and articles. Wouldn't it be nice to be able to customize this list of links for each user, making it a better user experience? The Azure Custom Decision Service provides contextual decision-making, allowing for a more robust user experience. It does so by converting content into features for Machine Learning. This technology utilizes several other Microsoft Cognitive Services, such as Entity Linking, Text Analytics, Emotion, and Computer Vision for a more personalized and intelligent experience.
Data Time Travel with SQL Server Temporal Tables
Many times developers have to support users by answering the “What happened to the data?” The task of retrieving data from a specific point in time is not an easy one. Often this involves retrieving a backup and restoring the data in question (hopefully!). SQL Server 2016 introduced Temporal Tables, allowing a developer to retrieve data from a specific point in time, without backups. With a few TSQL commands a historical table can be created, automatically updated, and readily accessed.
Giving Sight to Applications
The Custom Vision Service is part of Azure's Cognitive Services. It allows you to build classifiers that recognize specific content in images. Through machine learning, a classifier can be trained to recognize key factors in a picture that are specific to your application. In addition, the Custom Vision Service could be used for filtering image content for a web site, expediting the content filtering process.
Creating ML Models with Python and SQL Server
This session explores the seamless integration of Python and SQL Server for developing, deploying, and managing machine learning models. The presentation will cover practical examples of building models using popular Python libraries and demonstrate techniques for storing, versioning, and invoking models through SQL Server. Participants will leave with actionable insights and best practices for leveraging Python and SQL Server in real-world ML applications.
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