Luis Beltran

Information & Communications Technology

Azure Mobile Development Enterprise Architecture

Zlín, Czechia

Luis Beltran

Microsoft MVP, Xamarin Certified Mobile Developer

Hello! My name is Luis. I'm a Microsoft MVP in Developer Technologies and AI. I am currently pursuing a PhD in Engineering Informatics at Tomas Bata University in Zlin, Czech Republic.

I am a fan of software development, particularly mobile apps with Xamarin integrated with cloud computing services, such as Azure. Moreover, Artificial Intelligence is amazing and one of my favorite topics!

I've been developing .NET apps for more than 10 years. I find C# a really powerful language which allow us to create different kinds of software.

I enjoy sharing my knowledge with others, either at writing a blog post or streaming an online session. Of course, activities such as talking for a conference and answering peeps' questions in social networks are included as well.

Current sessions

Reconocimiento de objetos en videos con Azure Video Indexer y Custom Vision

En este workshop exploraremos cómo combinar el poder de Video Indexer y Azure Custom Vision en una aplicación que nos permita reconocer cubrebocas (mascarillas / barbijos) en videos.

Mientras Video Indexer nos proporciona información detallada de lo que sucede en un video (subtítulos, personas, tópicos), también nos proporciona fotogramas con su descripción general. Sin embargo, tal vez requerimos que dichas imágenes sean asociadas a clases específicas.

Ahí es donde entra Custom Vision. Podemos entrenar un modelo con imágenes que representan las distintas etiquetas que se desea reconocer. A continuación, pasamos los fotogramas al detector de objetos generado con Custom Vision y se pueda reconocer la presencia (o no) del elemento específico. Ésa es la idea que vamos a reflejar en este taller.


Let's play Rock, Paper, Scissors with ML.NET!

Image classification refers to the task of assigning a label to an image. You can use ML.NET's powerful API to learn features from labeled images and then train a model that recognizes if an image contains any of the target classes.

Let's demonstrate how to perform image classification with ML.NET and then have some fun by incorporating the output model in a Rock, Paper, Scissors game where players use hand signals in front of a camera. The application takes the photo, sends it to the model, and returns the tag, which is in turn sent back to the application to determine the result.


Cómo crear un ciclo completo de BD, AKS y Frontend en 60 minutos o le devolvemos su dinero

En esta sesión veremos un ciclo completo de una solución de software, desde la base de datos hasta la implementación en un frontend. Todo en menos de una hora.

Tecnologías involucradas:
- Implementación y administración de Azure PostgreSQL
- Cloud Native con Azure Kubernetes Service
- Azure Video Indexer para procesamiento y análisis de videos


Building database interactions with users through chatbots

The development of Artificial Intelligence is increasingly present in our lives and as time goes by, its presence will grow thanks to the momentum that enterprises are currently providing.

One of the most engaging AI applications is chatbots, which interact with users in real-time to assist them to perform a task -such as booking a hotel, answering a question or looking for specific information on the Internet- while simulating that a real human is behind the scene.

Data is knowledge, and the data that has been stored in your Azure SQL database can be used as an input for a bot which assists a company's customers in order to process the information for them and return expected results.

This session will be focused on explaining the actors involved when building a bot capable of obtaining data from your storage, including Azure SQL Database, Microsoft Bot Framework and LUIS (Language Understanding Intelligent Services). And you can integrate your bot on different channels such as Facebook Messenger, Microsoft Teams or even in your own website/app through Direct Line.


Azure Video Indexer: Advanced metadata extraction from video and audio content

Video Indexer provides the ability to extract deep insights from video and audio (without the need for data analysis or coding skills) using multi-channel (voice, visual) based machine learning models.

The service enables deep search, reduces operational costs, enables new monetization opportunities, and creates new user experiences on large video archives.

We are going to explore the service and create an app that obtains information from multimedia files.


Análisis de transmisiones en vivo con Azure Video Indexer

Azure Media Services Video Indexer es un servicio de nube diseñado para analizar y extraer información detallada (palabras, texto escrito, rostros, personas, emociones, temas, marcas...) de archivos multimedia ya creados de antemano. Sin embargo, para algunos casos de uso, es importante obtener la información de los medios de una transmisión en vivo lo más rápido posible. Por ejemplo, los productores de contenido podrían utilizar los metadatos en una transmisión en vivo para automatizar la producción de televisión.

En esta sesión explicaremos una solución que permite utilizar Video Indexer en el análisis casi en tiempo real de transmisiones en vivo; esta solución integra otros servicios de Azure, como Logic Apps, Azure Functions, Azure Media Player y CosmosDB.


Developing a serverless WhatsApp chatbot

Chatbots are AI software capable of interacting with users in natural language. They are able to detect users' intentions by extracting keywords from their messages and then provide an appropriate response to their requests. They are important nowadays as they can be used for repetitive, time-consuming tasks in effective ways, allowing companies to focus in other activities and optimize resources (especially human resources).

Microsoft provides LUIS and Bot Framework to develop and create bots the easy way. Without being an AI-expert, you can develop and deploy conversational bots. Add Azure Bot Service to the equation and you'll have a bot connected to the cloud which can be inserted in several channels, such as Skype, Microsoft Teams, Facebook Messenger and even to your website through the WebChat channel with very few configuration steps.

However, some channels are not currently available in the Azure Bot Service offer. WhatsApp, the most popular messenger app with 1.6 billion monthly users as of July 2019 is a notable example. And our customers would love to communicate with our apps through this messenger application.

So, how can we tackle this problem? Azure Functions to the rescue! We can simplify things by creating a serverless code that is connected to a WhatsApp number through webhooks. This means that an Azure Function will be triggered every time a user sends a message to this number. And we can infuse intelligence in the reply by connecting it to a LUIS model for natural language processing. In short, we will be using three technologies:

* LUIS (Language Understanding Intelligent Service)
* Azure Functions
* Twilio API (for accessing WhatsApp)


ML.NET: Machine Learning (and deep learning!) for the C# developer

ML.NET is an open-source framework for machine learning that is available for .NET developers (C# / F#) that allows you to train, build and deploy ML models for several scenarios: sentiment analysis, value prediction, even image classification. The models can be saved and consumed from other applications (web, desktop, mobile).

Let's explore what ML.NET has to offer to the C# developer and demonstrate several scenarios (including deep learning) as we understand its powerful capabilities. Even if you are initiating your machine learning experience, you can start creating ML solutions!


Incorpora Inteligencia Artificial en tu negocio con AI Builder

AI Builder es una capacidad de Power Platform que mejora el rendimiento de tu empresa con herramientas para automatizar procesos y predecir resultados. Puedes incorporar rápidamente inteligencia artificial a aplicaciones y flujos que se conectan a datos empresariales almacenados en Microsoft Dataverse o en SharePoint, OneDrive o Azure.

En esta sesión crearemos un modelo de IA utilizando las herramientas de AI Builder de una forma ágil y sencilla, sin necesidad de escribir código.


Transfer Learning en Deep Learning utilizando TensorFlow & ML.NET

Transfer Learning es una técnica de Machine Learning en la que un modelo desarrollado inicialmente para una tarea específica sirve ahora como punto de partida para otro modelo en una segunda tarea más general. Es bastante útil en Deep Learning, ya que los recursos informáticos y de tiempo son limitados, por lo que un modelo previamente entrenado se puede utilizar como entrada para una tarea de procesamiento de lenguaje natural o visión por computadora.

Demostremos cómo funciona Transfer Learning en ML.NET explorando el siguiente escenario:

- En primer lugar, se incorporará un modelo de ML pre-entrenado Inception (TensorFlow) en un flujo de trabajo ML.NET.

- Luego, Transfer Learning se aplica a este modelo utilizando la API de clasificación de imágenes ML.NET para crear un nuevo modelo de Machine Learning personalizado que identifica categorías de imágenes. Todo el conocimiento adquirido al resolver el problema de clasificación inicial es útil para reducir el tiempo de entrenamiento y resolver una segunda clasificación.

- Si el tiempo lo permite, podemos implementar este modelo en una API web para su consumo desde otra aplicación (como una aplicación móvil)


Open XML SDK - Managing Office docs from your .NET apps

OpenXML SDK for Office is an open-source package that allows you to create, edit and manage Word, Excel, and Power Point documents in .NET applications.

In this session, let's discover what OpenXML SDK offers in terms of functionality, and explore sample scenarios, such as creating a document letter, adding charts in an spreadsheet, opening, searching and editing Office documents or even adding slides in presentations.


Dockerize your Machine Learning models

When creating a machine learning model with Python, a common question is how to make the solution available for consumption from client apps or even for testing.

The objective of this session is to explain how a portable and consumable machine learning solution can be integrated within external applications.

This session is 100% practical and we will use the following technologies:

* Python (and various libraries) to create a classification Machine Learning model
* SQLite or SQL Server as data repository
* ASP .NET Core for the API the apps will connect to in order to consume the ML model
* Docker to create a container that includes the entire solution
* (If time allows it) A mobile application in Xamarin to interact with the implemented ML solution


Cosmos DB + Azure Functions: A serverless database processing

With the native integration between Cosmos DB and Azure Functions, you can create database triggers, input bindings, and output bindings directly from your account.

Using Azure Functions and Cosmos DB, you can create and deploy event-driven serverless apps with low-latency access to rich data for a global user base.

In this session, we'll explore what it takes to setup a serverless environment capable of performing CRUD operations on a Cosmos DB account, as well as some recommendations and use cases.


Clasificación de imágenes usando Deep Learning en ML.NET

ML.NET es un framework open-source para desarrollares .NET que desean construir, entrenar e implementar modelos de machine learning para diferentes escenarios: análisis de sentimientos, predicción de valores, clasificación de imágenes y más, con la posiblidad de guardar el modelo y exportarlo para su uso en otro tipo de aplicaciones (web, consola, móvil, etc)

En esta sesión exploraremos ML.NET y demostraremos su uso mediante la creación de un modelo clasificador de imágenes que utiliza deep learning.


Microsoft Graph: Connecting to the data that drives productivity

Microsoft Graph is the gateway to data and intelligence in Microsoft 365. It provides a unified programmability model that you can use to access the tremendous amount of data in Microsoft 365, Security, and other services.

In this session we will explore the Microsoft Graph API to enable our apps (in this case, a Xamarin mobile app) to interact with several Office 365 services including Teams and One Drive in a simple, efficient way.

We will also use the Open Office SDK to create Excel and Word documents from our application that will be uploaded to One Drive using the Microsoft Graph API.

We will also cover the topic of authentication, since this app will be registered to Azure AD so only authorized users can access it.


Developing Enterprise mobile apps with Xamarin and .NET

.NET is the framework to develop everything, mobile applications included. It is a complete platform that allows you to integrate several services and libraries in order to build useful applications.

In this session I'll explain how to build a mobile application:
* with a great design
* which uses Office 365 authentication
* which creates Word and Excel documents using Open XML SDK
* which uses Graph API to share documents and information between members
* which adds AI capabilities using Azure Cognitive Services

All in one hour!


Xamarin.Forms 5: Desarrollo veloz de hermosas apps móviles con menos código

Xamarin.Forms 5 viene equipado con nuevas características para hacer más fácil que nunca desarrollar apps cross-platform rápidamente y con un diseño atractivo. En esta sesión veamos lo nuevo que ofrece Xamarin.Forms 5, así como una revisión al Xamarin Community Toolkit.


Using your database for Machine Learning with ML.NET

ML.NET is an open-source framework for machine learning that is available for .NET developers (C# / F#) that allows you to train, build and deploy ML models for several scenarios: sentiment analysis, value prediction, even image classification. The models can be saved and consumed from other applications (web, desktop, mobile).

To accomplish this ML.NET accepts data that comes from files, in-memory lists and also, databases.

In this session, I'll explain what is ML.NET and how to build a ML model from data that is stored in an Azure SQL database. The connection will be made through Model Builder (integrated in Visual Studio) and also from C# code (thanks to the ML.NET API).


Developing a puzzle mobile app!

During X-mas, it is quite common to spend time with your beloved ones talking about life and playing games. One of the most challenging ones is puzzles, and I can teach you how to create a puzzle mobile application game with Christmas pictures that will amaze your friends.

This session is about having fun developing a Xamarin mobile application, which is a Microsoft technology for Android and iOS apps creation using C#.


Helping Santa with the power of Azure AI

Santa has a lot of work during this season. From reading each letter to preparing & delivering gifts, can we help him somehow to reduce his workload?

Enter Azure Cognitive Services, a set of AI services ready-for-use in our applications. For instance, we can use OCR capabilities to retrieve the text of a letter, then process it with Text Analytics to analyze the sentiment and decide if it should go into Santa's Naughty or Nice List.

In this blog post, I will explain how to create a mobile application which uses the aforementioned services to scan a letter and analyze it to assist Santa in this hard-working task.


Deep Learning in Xamarin apps! Enter ML.NET

ML.NET is an open-source framework for machine learning that is available for .NET developers. We can train, build and deploy ML models for several scenarios: sentiment analysis, value prediction, and image classification with deep learning. The models can then be saved and consumed from other applications (web, desktop & mobile for instance, mainly exposing it as a REST API).

In this session, we will explore ML.NET and the above scenarios. Then, we'll create a deep learning model for image classification that will be saved & included in a REST API to make this ML task available to a Xamarin mobile app that sends images as part of a request to a server.


ML.NET + Azure Functions = Serverless Machine Learning

Azure Functions proporciona una manera sencilla de ejecutar pequeños fragmentos de código a escala en un entorno administrado "sin servidor" en la nube. Su versatilidad llega al punto de que es posible desplegar en la nube modelos de machine learning previamente entrenados con ML.NET para realizar predicciones en escenarios controlados por eventos, tales como solicitudes HTTP, horarios programados o incluso justo después de cargar un nuevo archivo.

En esta sesión explicaremos cómo crear un modelo de predicción de machine learning con ML.NET que se integra en una Azure Functions para ser ejecutada cada vez que un nuevo archivo (blob) es añadido en un contenedor de Azure Storage.


Introducción a MVU con Comet

Durante el pasado Microsoft Build 2020, .NET MAUI fue anunciado como la propuesta de un .NET unificado que permitirá a los desarrolladores enfocarse en una experiencia de un solo proyecto, implementando no solo en múltiples dispositivos sino también en plataformas.

Si bien .NET MAUI admitirá los patrones conocidos MVVM y XAML, su hoja de ruta también incluye una oportunidad para aprender y aprovechar las ventajas de patrones modernos, como es el caso de MVU.

La pregunta es: ¿cómo puedo prepararme para desarrollar aplicaciones de C# fluidas? Conoce Comet, un nuevo prototipo de Framework UI para escribir interfaces de usuario de aplicaciones que sigue el patrón MVU, soporta Hot Reload y permite compilar la app en múltiples plataformas: móvil (Xamarin Forms/nativo), desktop (WPF, Mac OS), web (Blazor) con C#.

Si bien está en fase experimental y se considera una prueba de concepto, sin duda es un enfoque interesante que ayudará a prepararte para lo que viene en la evolución de Xamarin cuando llegue .NET MAUI

En esta sesión discutiremos los conceptos básicos de MVU y hablaremos de Comet con código, demos y conceptos esenciales.


Developing an object detector solution with Azure Custom Vision .NET SDK

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve image classifiers that adapt to your needs without a background in AI advanced techniques.

One of Custom Vision functionalities is Object Detection, which both identifies a target element in a picture and returns its location (coordinates) in the image. This is particularly useful in scenarios where there are several objects but only one of them is relevant.

In this presentation, the Custom Vision service will be described, with focus on how to deliver an object detection model by using the .NET SDK. This model can be accessed by applications either in online (by using the Prediction API) or offline modes (by exporting it to a platform, such as TensorFlow), and some cases will be demonstrated in a mobile application.


Construyendo un bot de música de videojuegos

En esta sesión describiré la solución tecnológica que se desarrolló para una comunidad internacional de fans de la música de videojuegos que consiste en un bot que interactúa con los usuarios de un chat de streaming en YouTube y que por medio de comandos reproduce canciones, añade nueva música a la colección, y más funciones.

Este bot fue construido utilizando tecnología de Google (YouTube API & Google Sheets) y Microsoft (Azure Functions, WPF & web apps, Azure Storage, etc).

¡Mostraré las bases para que puedas construir tu propio bot musical!


Transfer Learning for Deep Learning: From Custom Vision to TensorFlow & ML.NET

Transfer learning is a machine learning technique in which a model that was developed for an initial task serves now as the starting point for a model on a second duty. It is quite useful in Deep Learning since compute and time resources are limited, so you a pre-trained model can be used as an input for a computer vision or natural language processing task.

Let's demonstrate how Transfer Learning works in ML.NET by exploring the following scenario:

- Firstly, an Azure Custom Vision image classification model that uses the Open Images Dataset is trained, published and exported to TensorFlow.

- Then, transfer learning is applied to this model using the ML.NET Image Classification API in order to create a new, custom deep learning model to identify specific image categories. All the knowledge gained when solving the initial classification problem is useful for shortcutting another training process and solve a second classification.


Developing C# bot apps for Microsoft Teams

Microsoft Teams is the hub for team collaboration which integrates people and tools to improve productivity within your organization. From chat-based collaboration to web conferences, it brings effectivity within your business to the next level.

Customizing your Microsoft Teams workspace is possible thanks to the developer platform, which allows you to extend the capabilities of the product and roll your own custom applications into your organization. Furthermore, these solutions can be distributed publicly to other enterprises, either for free or with a price, via AppSource, the Microsoft ecosystem for app publication.

In this session, the following real-case scenario will be explored: A C# bot application which uses LUIS and the Bot Framework SDK understands users’ messages and retrieves information from an Azure SQL Database through an ASP .NET Core Web Api. The app is hosted on Azure. AppStudio, a tool integrated in Microsoft Teams, is used to publish an app package, then register it in Teams and finally publish it for distribution and testing within your Teams workspace members.


Serverless mobile database access with Azure Functions, Xamarin and SQLAzure

In this session, I will talk about Azure Functions, which is Microsoft serverless computing platform which allows developers to direct their attention to event and application scenarios rather than having to worry about the infrastructure.

With a range of more than 50 scenarios/templates available in Azure Functions, we will focus our attention on designing functions that interact with SQL Azure databases according to events, for instance an HTTP request on demand that allows to add information in a table or a data cleansing process that runs every day periodically at a specific time.

In addition, I will demonstrate how to combine several powerful technologies available in Azure Functions. They can be used as a backend which connects to a SQL Azure database and then sends emails to customers by using SendGrid. Everything will be consumed from an Android mobile application developed with Xamarin.


IA enriquecida con Azure Cognitive Search

El enriquecimiento de Inteligencia Artificial es una capacidad de indexación de Azure Cognitive Search que habilita la extracción de información en imágenes, archivos y otras fuentes de datos no estructurados.

Las tareas de extracción y enriquecimiento se implementan a través de habilidades cognitivas, tales como el procesamiento del lenguaje natural y el procesamiento de imágenes con varias posibilidades: reconocimiento de entidades, detección de lenguaje, detección de sentimientos, OCR, detección de rostros, etc.

En esta sesión se describirá el servicio Azure Cognitive Search y se construirá un pipeline de enriquecimiento capaz de extraer información de documentos PDF con el cual se generará un sitio web o una app móvil para realizar búsquedas de información.


Construyendo un clasificador de imágenes offline y móvil con Azure Custom Vision y Xamarin

Azure Custom Vision permite crear poderosos clasificadores de imágenes en cuestión de minutos sin requerir una expertiz compleja en inteligencia artificial. Simplemente alimenta el servicio con imágenes para que éste se adapte a tus propias necesidades, etiquétalas y entrena un modelo que puede ser publicado en línea, disponible para ser utilizado por tus aplicaciones. El servicio cuenta con un SDK que te permite automatizar el proceso.

Mejor aún, este modelo puede ser exportado como paquete (Tensorflow, CoreML, ONNX), ideal para escenarios offline con resultados en tiempo real (sin latencia), típico de aplicaciones móviles (o web).

En esta sesión el servicio Custom Vision será descrito. Posteriormente, un clasificador de imágenes será creado utilizando el SDK disponible para .NET a partir de miles de imágenes. El modelo producido será exportado en formatos Tensorflow y CoreML para integrarlo en aplicaciones móviles de Android e iOS, respectivamente.


Building real-time image classifiers for mobile apps with Azure Custom Vision

Azure Custom Vision allows you to create powerful image classifiers in minutes to without having to be an AI expert. You feed the service with images -so the service adapts to your own needs-, tag them and train a model that can be published to an endpoint URL for further requests. You can also use the Custom Vision SDK to automatize the process.

Furthermore, this model can also be exported for offline, real-time classification experiences. For instance, you can embed the classifier into a mobile application, or a website.

In this session, the Custom Vision service will be described. An image classifier will be created by using the .NET SDK and thousands of images. The output model will be exported to both Tensorflow and CoreML to integrate it into an Android and iOS mobile applications, respectively.


AI enrichment with Azure Cognitive Search

AI enrichment is a capability of Azure Cognitive Search indexing that is used to extract latent information from images, blobs, and other unstructured, non-text data sources - enriching the content to make it more searchable in an index or knowledge store.

Both extraction and enrichment tasks are implemented through cognitive skills such as natural language processing and image processing skills with several possibilities: Entity recognition, language detection, sentiment detection, OCR, face detection...

In this session, the Azure Cognitive Search service will be explored and described. Then, an enrichment pipeline capable of extracting information from PDF documents will be built in order to demonstrate a use case scenario, along with requirements, costs, related Azure services and a website/mobile app that allows users to look for information from the PDF files.


Acceso serverless a tus bases de datos desde apps móviles: Azure Functions, SQL Azure & Xamarin

Azure Functions es la plataforma de cómputo serverless de Microsoft que permite a los desarrolladores enfocar su atención en escenarios de eventos y aplicaciones sin tener que preocuparse por la infraestructura.

Es posible diseñar funciones que interactuan con bases de datos SQL Azure basadas en eventos, por ejemplo una petición HTTP bajo demanda que permite insertar datos o programar un proceso de limpieza de tablas que se ejecuta periódicamente a una hora específica.

En esta sesión demostraré que es posible combinar varias tecnologías para generar una solución poderosa. Azure Functions puede ser utilizado como un backend que se conecta a una base de datos SQL Azure para enviar correos electrónicos (con SendGrid). Una app móvil diseñada con Xamarin será la encargada de orquestar todo el proceso.


Boost your business reliability with Azure Anomaly Detector

Time-series data is more than just numbers. Identifying patterns and trends is important, but monitoring and detecting abnormalities in your data are relevant tasks that helps businesses to adapt and solve problems.

Anomaly Detector is an Azure AI service that helps you foresee problems before they occur. Through an API, this service ingests time-series data and selects the best-fitting detection model for your data to ensure high accuracy.

In this session, the Anomaly Detector service will be described, including terms, algorithms, parameters, costs, and best practices. A mobile app that analyzes time-series data and finds anomalies will be used to demonstrate the service.


Build a mobile chatbot with Xamarin & Bot Framework

The development of Artificial Intelligence is increasingly present in our lives and as time goes by, its presence will grow thanks to the momentum that enterprises are currently providing.

One of the most engaging AI applications are chatbots, which allow users to interact in real-time with a smart entity which assists them to perform a task -such as booking a hotel, answering a question or looking for specific information on the Internet- while simulating that a real human is behind the scene.

This workshop will help attendees to build three things:

* A LUIS application capable of understand and process users' messages to get the intention and important entities within the message.
* A web application which contains all the bot logic. This is built by using Bot Framework and will be published in Azure.
* A cross-platform mobile app built with Xamarin which makes use of the bot application by sending requests (from users) and listening to replies (from the bot).


Exploring the Microsoft Azure AI Engineer Associate Certification

Do you use Cognitive Services or Machine Learning to architect and implement AI solutions involving natural language processing, speech, computer vision, bots, or agents?

Say no more, you can become a Microsoft Azure AI Engineer Associate! And if you don't use them, don't worry, you can learn and create powerful AI models!

In this session, we'll explore the Training Path for the AI-100 Microsoft Certification Exam by explaining the concepts and demos which will give you an insight for the certification. We'll get useful experience on:

* Azure Machine Learning Studio for publishing ML experiments.
* Azure Cognitive Services usage for speech translation, image classification, and texr evaluation tasks.
* Bots implementation with Azure Bot Service

We'll also talk about best-practices, recommendations and advices on Azure AI services in this session.


Deploying local bots with Bot Framework and LUIS Docker containers

Bot Framework allows developers to seamlessly build conversational bots which can later be published to services such as Slack, Skype, Messenger and more! However, there might be particular cases in which bots need to be deployed as local services (e.g. Intranets, data compliance, limited external-networks access) and also use language understanding capabilities.

Enter Docker containers. Certain Azure Cognitive Services can now be deployed as containers to deliver AI-driven solutions which doesn't send the data to an external network but to an internal server. One of these services is LUIS, which provides query predictions and whose packages can be integrated in the server for local consumption.

In this session, the following scenario will be explored: A cross-platform mobile app which establishes a connection to a bot (which was created by using Bot Framework and which uses a LUIS package produced from utterances and examples) that is used by customers who want to obtain products and services information. Data comes from a local SQL Server database.


Past and future events

Azure Summit

13 Sep - 19 Sep 2021

Virtual 2021 Data.SQL.Saturday.LA

12 Jun 2021

Azure Days 2021 de ConoSur.Tech

21 May - 22 May 2021

Cloud Lunch and Learn Marathon 2021

13 May - 14 May 2021

The Virtual ML.NET Community Conference 2021

7 May - 8 May 2021

Azure Cosmos DB Conf

20 Apr - 21 Apr 2021

Global AI Night 2021 - Cleveland

20 Apr 2021

Global AI Night Latinoamérica 2021

19 Apr - 24 Apr 2021

Virtual Global Azure - Verona 2021

15 Apr - 16 Apr 2021

Global Azure Latinoamérica 2021

15 Apr - 17 Apr 2021

Global Azure 2021

14 Apr - 16 Apr 2021

Global Azure 2021 - Spain

14 Apr - 16 Apr 2021

A.I. Day 2021

11 Mar 2021

SQL Server User Group Croatia 2021

9 Mar - 30 Dec 2021

Global Power Platform Bootcamp Bolivia

19 Feb - 20 Feb 2021

CodeGen 2021

12 Feb 2021

Data Saturday Guatemala 2021

23 Jan 2021

Microsoft 365 Friday California 2021

22 Jan 2021

Global AI Bootcamp 2020

15 Jan 2021

Global AI Bootcamp Singapore

15 Jan - 16 Jan 2021

Global AI Bootcamp Latinoamérica

15 Jan - 16 Jan 2021

Cloud Lunch and Learn

1 Jan - 31 Dec 2021

dotNet OpenSource Days 2020

18 Dec 2020

IT Pro|Dev Connections 2020

11 Dec - 12 Dec 2020

Festive Tech Calendar

1 Dec - 31 Dec 2020

.NET Conf Latinoamérica 2020

14 Nov - 28 Nov 2020

.NET Virtual Conference

16 Oct 2020

Xamarin Assemble 2020 **Online**

16 Jul - 18 Jul 2020

LightUp

14 Jul - 15 Jul 2020

Azure Skåne AI Day 2020

13 Jun 2020

Azure Day Rome 2020

11 Jun 2020

The Virtual ML.NET Community Conference

29 May - 30 May 2020

Microsoft 365 Virtual Marathon

27 May - 28 May 2020

Cuarentena Nights

25 May - 5 Jun 2020

Data Platform Discovery Day Europe

29 Apr 2020

Global Azure 2020 - Munich

24 Apr 2020
Munich, Bavaria, Germany

Global Azure Virtual

22 Apr - 24 Apr 2020
Seattle, Washington, United States

Virtual NetCoreConf 2020

3 Apr 2020

Global AI Bootcamp

13 Dec 2019
Munich, Bavaria, Germany

Xamarin Developer Summit

10 Jul - 12 Jul 2019
Houston, Texas, United States

Microsoft Build

6 May - 8 May 2019
Seattle, Washington, United States

Global Azure Bootcamp 2019 Munich

26 Apr 2019
Munich, Bavaria, Germany