Intelligent Assistant with Microsoft BotFrameworkMarvin Heng
A sharing of how difference pieces of technologies can be put together to be great solution for small businesses.
Technologies involved: Microsoft BotFramework, SignalR and ASP.NET Core on Azure.
www.techconnect.io
Youtube: https://www.youtube.com/watch?v=nwGFZA0h9k8&feature=youtu.be
Microsoft Cognitive Services provides APIs for computer vision, speech, language, and knowledge that can be easily integrated into applications with just a few lines of code. The APIs are powered by experts from Microsoft Research, Bing, and Azure Machine Learning and include documentation, sample code, and community support. Popular APIs include Computer Vision, Face, Emotion Recognition, Bing Speech, Language Understanding, and Academic Knowledge.
Microsoft Cognitive Services let you build apps with powerful algorithms using just a few lines of code. They work across devices and platforms such as iOS, Android, and Windows, keep improving, and are easy to set up.
This document provides an overview of Microsoft Azure machine learning and cognitive services. It describes the different machine learning and AI services available in Azure including Azure Machine Learning, Cognitive Services, and other ML options. It also provides examples of how various cognitive services APIs can be used for applications involving computer vision, language processing, speech recognition and more.
Slides from DevNexus in Atlanta GA showing Cognitive Services. Minus demos unfortunately! Best place to check all this out is https://www.microsoft.com/cognitive-services/
This document discusses Microsoft Azure machine learning and cognitive services. It provides an overview of the different machine learning and AI services available in Azure, including Azure Machine Learning, Cognitive Services, and related tools. It also provides examples of how various cognitive services APIs can be used to add capabilities like computer vision, speech recognition, and natural language processing to applications.
Microsoft Cognitive Services - Recommendations API: Your first recommendation...Bruno Paulino
Microsoft Cognitive Services provides various APIs including the Recommendations API. The document discusses getting started with the Recommendations API by enabling Cognitive Services on an Azure account, understanding the API's capabilities for different recommendation types, and how to build a recommendations model by uploading catalog and usage files to define items and user interactions. It also provides examples of integrating the API with SSIS and displaying recommendations in Power BI.
Cognitive Services: Building Smart Apps with Speech, NLP & VisionNick Landry
Your computer can recognize your voice and detect words in a speech dictation, but can it truly understand the meaning of what you are saying? Can it analyze your intent and respond accordingly? You don’t need a PhD in artificial intelligence to integrate speech and natural language understanding in your projects. Microsoft Cognitive Services (aka “Project Oxford”) is a portfolio of cloud-based REST APIs and SDKs powered by Machine Learning which enable developers to write applications which understand the content within the rapidly growing set of multimedia data. Cognitive Services API services will help you understand and interact with audio, text, image, and video. In this session, we’ll start with an overview of available services for speech recognition and speech synthesis. Then we’ll explore through live demos how to leverage the Language Understanding Intelligent Service which lets you determine intent, detect entities in user speech and improve language understanding models to more efficiently work with user data. Lastly, we’ll leverage Computer Vision APIs to detect human faces, analyze the content of images, and perform Optical Character Recognition (OCR) to detect and analyze words within a photo. Come learn how your apps can tap into the same active learning services behind the brain of Cortana, and get started writing smart applications that can understand what your users are saying.
Gracias a los Cognitive Services ahora podemos añadir inteligencia a nuestras apps de una manera sencilla. La combinación de estos servicios abren un mundo nuevo de posibilidades, por lo que durante esta charla veremos una breve introducción a los distintos servicios para pasar directamente a verlos en acción en aplicaciones y situaciones reales. Se trata de una charla introductoria en la que haremos demos y veremos cómo podemos utilizar estos servicios en nuestro código.
The document discusses Azure Cognitive Services and how it can be used to build intelligent applications. It provides an overview of the various cognitive services available like computer vision, text analytics, speech, and language services. It also discusses how these services can be used to add capabilities like search, language processing, decision making to applications. Additionally, it mentions the option to customize services by training models using Azure Custom Vision to build bespoke AI applications.
How to remove channel barriers and focus on customer experienceEpiserver
The infinite rise of marketing channels makes creating great customer experience increasingly difficult to attain. This session will show you how to break down the multichannel barriers and focus on what really matters (and converts) by serving personalised and relevant content to your customers in real time.
The document lists various cognitive services and APIs provided by Microsoft including services for language, speech, search, vision, translation, and more. It also provides suggestions for using these cognitive services such as detecting plants to provide care instructions, transcribing the visible world to help the visually impaired, creating a receipt tracking app, and generating article summaries through entity extraction. The document encourages adding additional ideas for using Microsoft's cognitive services.
Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Azure Cognitive Services are APIs, SDKs, and services available to help developers build intelligent applications without having direct AI or data science skills or knowledge.
Building real time image classifiers for mobile apps with azure custom visionLuis Beltran
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 .portal. The output model will be exported to both Tensorflow and CoreML to integrate it into an Android and iOS mobile applications, respectively.
This is my presentation for Global Azure Verona 2021, where I talked about Azure Functions and how this technology can be used to process messages that come from WhatsApp in a chatbot environment.
This document discusses using event-driven architectures and serverless computing with AWS services. It begins with defining event-driven architectures and how serverless architectures relate to them. It then outlines several AWS services like EventBridge, Step Functions, SQS, SNS, and Lambda that are well-suited for building event-driven applications. The document demonstrates using S3, DynamoDB, API Gateway and other services to build a serverless hotel data ingestion and shopping platform that scales independently for static and dynamic data. It shows how to upload, store, and stream hotel data and expose APIs using serverless AWS services in an event-driven manner.
This document provides an overview of the Azure Batch Service, including its core features, architecture, and monitoring capabilities. It discusses how Azure Batch allows uploading batch jobs to the cloud to be executed and managed, covering concepts like job scheduling, resource management, and process monitoring. The document also demonstrates Azure Batch usage through the Azure portal and Batch Explorer tool and reviews quotas and limits for Batch accounts, pools, jobs, and other resources.
In this session, we will take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.
At the end of this session, you will have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.
When it comes to microservice architecture, sometimes all you wanted is to perform cross cutting concerns ( logging, authentication , caching, CORS, Routing, load balancing , exception handling , tracing, resiliency etc..) and also there might be a scenario where you wanted to perform certain manipulations on your request payload before hitting into your actual handler. And this should not be a repetitive code in each of the services , so all you might need is a single place to orchestrate all these concerns and that is where Middleware comes into the picture. In the demo I will be covering how to orchestrate these cross cutting concerns by using Azure functions as a Serverless model.
In this talk, we will start with some introduction to Azure Functions, its triggers and bindings. Later we will build a serverless solution to solve a problem statement by using different triggers and bindings of Azure Functions.
Language to be used: C# and IDE - Visual Studio 2019 Community Edition"
In this workshop, you will understand how Azure DevOps Services helps you scale DevOps adoption strategies in enterprise. We will explore various feature and services that can enable you to implement various DevOps practices starting from planning, version control, CI & CD , Dependency Management and Test planning.
In this session, we will understand how to create your first pipeline and build an environment to restore dependencies and how to run tests in Azure DevOps followed by building an image and pushing it to container registry.
In this session, we will discuss a use case where we need to quickly develop web and mobile front end applications which are using several different frameworks, hosting options, and complex integrations between systems under the hood. Let’s see how we can leverage serverless technologies (Azure Functions and logic apps) and Low Code/No code platform to achieve the goal. During the session we will go though the code followed by a demonstration.
CREATING REAL TIME DASHBOARD WITH BLAZOR, AZURE FUNCTION COSMOS DB AN AZURE S...CodeOps Technologies LLP
In this talk people will get to know how we can use change feed feature of Cosmos DB and use azure functions and signal or service to develop a real time dashboard system
Imagine a scenario, where you can launch a video call or chat with an advisor, agent, or clinician in just one-click. We will explore application patterns that will enable you to write event-driven, resilient and highly scalable applications with Functions that too with power of engaging communication experience at scale. During the session, we will go through the use case along with code walkthrough and demonstration.
We will walk through the exploration, training and serving of a machine learning model by leveraging Kubeflow's main components. We will use Jupyter notebooks on the cluster to train the model and then introduce Kubeflow Pipelines to chain all the steps together, to automate the entire process.
It is difficult to deploy interloop Kubernetes development in current state. Know these open-source projects that can save us from the burden of various tools and help in deploying microservices on Kubernetes cluster without saving secrets in a file.
Cognitive Services: Building Smart Apps with Speech, NLP & VisionNick Landry
Your computer can recognize your voice and detect words in a speech dictation, but can it truly understand the meaning of what you are saying? Can it analyze your intent and respond accordingly? You don’t need a PhD in artificial intelligence to integrate speech and natural language understanding in your projects. Microsoft Cognitive Services (aka “Project Oxford”) is a portfolio of cloud-based REST APIs and SDKs powered by Machine Learning which enable developers to write applications which understand the content within the rapidly growing set of multimedia data. Cognitive Services API services will help you understand and interact with audio, text, image, and video. In this session, we’ll start with an overview of available services for speech recognition and speech synthesis. Then we’ll explore through live demos how to leverage the Language Understanding Intelligent Service which lets you determine intent, detect entities in user speech and improve language understanding models to more efficiently work with user data. Lastly, we’ll leverage Computer Vision APIs to detect human faces, analyze the content of images, and perform Optical Character Recognition (OCR) to detect and analyze words within a photo. Come learn how your apps can tap into the same active learning services behind the brain of Cortana, and get started writing smart applications that can understand what your users are saying.
Gracias a los Cognitive Services ahora podemos añadir inteligencia a nuestras apps de una manera sencilla. La combinación de estos servicios abren un mundo nuevo de posibilidades, por lo que durante esta charla veremos una breve introducción a los distintos servicios para pasar directamente a verlos en acción en aplicaciones y situaciones reales. Se trata de una charla introductoria en la que haremos demos y veremos cómo podemos utilizar estos servicios en nuestro código.
The document discusses Azure Cognitive Services and how it can be used to build intelligent applications. It provides an overview of the various cognitive services available like computer vision, text analytics, speech, and language services. It also discusses how these services can be used to add capabilities like search, language processing, decision making to applications. Additionally, it mentions the option to customize services by training models using Azure Custom Vision to build bespoke AI applications.
How to remove channel barriers and focus on customer experienceEpiserver
The infinite rise of marketing channels makes creating great customer experience increasingly difficult to attain. This session will show you how to break down the multichannel barriers and focus on what really matters (and converts) by serving personalised and relevant content to your customers in real time.
The document lists various cognitive services and APIs provided by Microsoft including services for language, speech, search, vision, translation, and more. It also provides suggestions for using these cognitive services such as detecting plants to provide care instructions, transcribing the visible world to help the visually impaired, creating a receipt tracking app, and generating article summaries through entity extraction. The document encourages adding additional ideas for using Microsoft's cognitive services.
Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Azure Cognitive Services are APIs, SDKs, and services available to help developers build intelligent applications without having direct AI or data science skills or knowledge.
Building real time image classifiers for mobile apps with azure custom visionLuis Beltran
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 .portal. The output model will be exported to both Tensorflow and CoreML to integrate it into an Android and iOS mobile applications, respectively.
This is my presentation for Global Azure Verona 2021, where I talked about Azure Functions and how this technology can be used to process messages that come from WhatsApp in a chatbot environment.
This document discusses using event-driven architectures and serverless computing with AWS services. It begins with defining event-driven architectures and how serverless architectures relate to them. It then outlines several AWS services like EventBridge, Step Functions, SQS, SNS, and Lambda that are well-suited for building event-driven applications. The document demonstrates using S3, DynamoDB, API Gateway and other services to build a serverless hotel data ingestion and shopping platform that scales independently for static and dynamic data. It shows how to upload, store, and stream hotel data and expose APIs using serverless AWS services in an event-driven manner.
This document provides an overview of the Azure Batch Service, including its core features, architecture, and monitoring capabilities. It discusses how Azure Batch allows uploading batch jobs to the cloud to be executed and managed, covering concepts like job scheduling, resource management, and process monitoring. The document also demonstrates Azure Batch usage through the Azure portal and Batch Explorer tool and reviews quotas and limits for Batch accounts, pools, jobs, and other resources.
In this session, we will take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.
At the end of this session, you will have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.
When it comes to microservice architecture, sometimes all you wanted is to perform cross cutting concerns ( logging, authentication , caching, CORS, Routing, load balancing , exception handling , tracing, resiliency etc..) and also there might be a scenario where you wanted to perform certain manipulations on your request payload before hitting into your actual handler. And this should not be a repetitive code in each of the services , so all you might need is a single place to orchestrate all these concerns and that is where Middleware comes into the picture. In the demo I will be covering how to orchestrate these cross cutting concerns by using Azure functions as a Serverless model.
In this talk, we will start with some introduction to Azure Functions, its triggers and bindings. Later we will build a serverless solution to solve a problem statement by using different triggers and bindings of Azure Functions.
Language to be used: C# and IDE - Visual Studio 2019 Community Edition"
In this workshop, you will understand how Azure DevOps Services helps you scale DevOps adoption strategies in enterprise. We will explore various feature and services that can enable you to implement various DevOps practices starting from planning, version control, CI & CD , Dependency Management and Test planning.
In this session, we will understand how to create your first pipeline and build an environment to restore dependencies and how to run tests in Azure DevOps followed by building an image and pushing it to container registry.
In this session, we will discuss a use case where we need to quickly develop web and mobile front end applications which are using several different frameworks, hosting options, and complex integrations between systems under the hood. Let’s see how we can leverage serverless technologies (Azure Functions and logic apps) and Low Code/No code platform to achieve the goal. During the session we will go though the code followed by a demonstration.
CREATING REAL TIME DASHBOARD WITH BLAZOR, AZURE FUNCTION COSMOS DB AN AZURE S...CodeOps Technologies LLP
In this talk people will get to know how we can use change feed feature of Cosmos DB and use azure functions and signal or service to develop a real time dashboard system
Imagine a scenario, where you can launch a video call or chat with an advisor, agent, or clinician in just one-click. We will explore application patterns that will enable you to write event-driven, resilient and highly scalable applications with Functions that too with power of engaging communication experience at scale. During the session, we will go through the use case along with code walkthrough and demonstration.
We will walk through the exploration, training and serving of a machine learning model by leveraging Kubeflow's main components. We will use Jupyter notebooks on the cluster to train the model and then introduce Kubeflow Pipelines to chain all the steps together, to automate the entire process.
It is difficult to deploy interloop Kubernetes development in current state. Know these open-source projects that can save us from the burden of various tools and help in deploying microservices on Kubernetes cluster without saving secrets in a file.
Must Know Azure Kubernetes Best Practices And Features For Better Resiliency ...CodeOps Technologies LLP
Running day-1 Ops on your Kubernetes is somewhat easy, but it is quite daunting to manage day two challenges. Learn about AKS best practices for your cloud-native applications so that you can avoid blow up your workloads.
Prometheus is a popular open source metric monitoring solution and Azure Monitor provides a seamless onboarding experience to collect Prometheus metrics. Learn how to configure scraping of Prometheus metrics with Azure Monitor for containers running in AKS cluster.
What if you could combine Trello, GitLab, JIRA, Calendar, Slack, Confluence, and more - all together into one solution?
Yes, we are talking about Space - the latest tool from JetBrains famous for its developer productivity-enhancing tools (esp. IntelliJ IDEA).
Here we have explained about JetBrains' space and its functionalities.
This document provides an overview of functional programming concepts in Java 8 including lambdas and streams. It introduces lambda functions as anonymous functions without a name. Lambdas allow internal iteration over collections using forEach instead of external iteration with for loops. Method references provide a shorthand for lambda functions by "routing" function parameters. Streams in Java 8 enhance the library and allow processing data pipelines in a functional way.
This talk will serve as a practical introduction to Distributed Tracing. We will see how we can make best use of open source distributed tracing platforms like Hypertrace with Azure and find the root cause of problems and predict issues in our critical business applications beforehand.
This talk serves as a practical introduction to Distributed Tracing. We will see how we can make best use of open source distributed tracing platforms like Hypertrace with Azure and find the root cause of problems and predict issues in our critical business applications beforehand.
Presentation part of Open Source Days on 30 Oct - ossdays.konfhub.com
The role of the lexical analyzer
Specification of tokens
Finite state machines
From a regular expressions to an NFA
Convert NFA to DFA
Transforming grammars and regular expressions
Transforming automata to grammars
Language for specifying lexical analyzers
ELectronics Boards & Product Testing_Shiju.pdfShiju Jacob
This presentation provides a high level insight about DFT analysis and test coverage calculation, finalizing test strategy, and types of tests at different levels of the product.
Dust Suppressants: A Sustainable Approach to Dust Pollution ControlJanapriya Roy
This journal explores the growing field of bio-organic dust suppressants as a sustainable solution to dust pollution. It reviews the working principles of dust suppression, key performance indicators, and the latest research on using natural materials like polysaccharides, lignin, proteins, and agricultural wastes. It also highlights current challenges and future directions to enhance the durability, cost-effectiveness, and environmental safety of bio-based dust control technologies. A valuable reference for researchers, environmental engineers, and industry professionals seeking eco-friendly dust management solutions.
Dust pollution, whether natural or anthropogenic, poses significant threats to both the environment and public health. Effective dust suppression technologies are essential in mitigating airborne particulate matter, especially in industrial, mining, and construction settings. Recently, bio-organic materials have emerged as promising raw materials for eco-friendly dust suppressants. This paper explores the working principles of dust suppressants, key performance evaluation indices, and the current progress in developing bio-based dust control agents using polysaccharides, lignin, proteins, microorganisms, and agricultural or forestry waste. It also discusses existing challenges and future research directions for enhancing the efficiency and applicability of bio-organic dust suppressants.
Working Principles of Dust Suppressants
Dust suppressants operate primarily through three interrelated mechanisms: wetting, coagulation, and consolidation.
Wetting: This mechanism involves the application of dust suppressants to reduce the surface tension of water, allowing it to penetrate and coat dust particles. This increases the weight and cohesion of the particles, causing them to settle quickly. Surfactants and hygroscopic agents are commonly used in this approach.
Coagulation: Dust particles are brought together to form larger aggregates through electrostatic interactions or binding agents, which helps in accelerating their settling.
Consolidation: A more long-term effect where the suppressant forms a crust or mesh-like structure over the dust-prone surface, physically stabilizing it and preventing re-entrainment of particles by wind or vehicle movement.
Bio-Organic Materials in Dust Suppressants
The shift toward natural, renewable, and biodegradable components has led to extensive research on the use of various bio-organic substances, including:
Polysaccharides: Starch, cellulose derivatives, and chitosan can form gels or films that stabilize surfaces.
Lignin: A byproduct of the paper industry, lignin offers strong binding capacity and is naturally water-resistant.
Proteins: Derived from agricultural waste, proteins like casein and soy protein can enhance binding and wetting.
Microorganisms: Certain bacteria and fungi can produce biofilms or exopolysaccharides that trap dust particles.
Agricultural and Forestry Wastes: Residues su
This paper proposes a shoulder inverse kinematics (IK) technique. Shoulder complex is comprised of the sternum, clavicle, ribs, scapula, humerus, and four joints.
π0.5: a Vision-Language-Action Model with Open-World GeneralizationNABLAS株式会社
今回の資料「Transfusion / π0 / π0.5」は、画像・言語・アクションを統合するロボット基盤モデルについて紹介しています。
拡散×自己回帰を融合したTransformerをベースに、π0.5ではオープンワールドでの推論・計画も可能に。
This presentation introduces robot foundation models that integrate vision, language, and action.
Built on a Transformer combining diffusion and autoregression, π0.5 enables reasoning and planning in open-world settings.
The Fluke 925 is a vane anemometer, a handheld device designed to measure wind speed, air flow (volume), and temperature. It features a separate sensor and display unit, allowing greater flexibility and ease of use in tight or hard-to-reach spaces. The Fluke 925 is particularly suitable for HVAC (heating, ventilation, and air conditioning) maintenance in both residential and commercial buildings, offering a durable and cost-effective solution for routine airflow diagnostics.
Passenger car unit (PCU) of a vehicle type depends on vehicular characteristics, stream characteristics, roadway characteristics, environmental factors, climate conditions and control conditions. Keeping in view various factors affecting PCU, a model was developed taking a volume to capacity ratio and percentage share of particular vehicle type as independent parameters. A microscopic traffic simulation model VISSIM has been used in present study for generating traffic flow data which some time very difficult to obtain from field survey. A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for prediction of PCUs of different vehicle types. From the results observed that ANFIS model estimates were closer to the corresponding simulated PCU values compared to MLR and ANN models. It is concluded that the ANFIS model showed greater potential in predicting PCUs from v/c ratio and proportional share for all type of vehicles whereas MLR and ANN models did not perform well.
2. What is Azure Cognitive Services?
• Cognitive Services bring AI within reach of every developer—
without requiring machine-learning expertise. All it takes is an
API call to embed the ability to see, hear, speak, search,
understand and accelerate decision-making into your apps.
3. Use the Custom Vision API to analyze
images for content, generate captions,
and more ..
4. Use the Text
Analytics API
to identify
sentiment
expressed in
text (e.g.,
Twitter feeds)
Language DetectionTopic Detection
Key Phrase ExtractionSentiment Analysis