Come puoi gestire i difetti? Se sei in una fabbrica, la produzione può produrre oggetti con difetti. Oppure i valori dei sensori possono dirti nel tempo che alcuni valori non sono "normali". Cosa puoi fare come sviluppatore (non come Data Scientist) con .NET o Azure per rilevare queste anomalie? Vediamo come in questa sessione.
Good observability is essential for modern software. It gives us confidence that our systems are working properly. And it also allows us to debug issues efficiently. In this talk, we’ll explore everything you need to know to start applying good observability to your projects. And we’ll see the most common pitfalls you need to be aware of. We will start with the tools and basic concepts in monitoring. And we’ll go over the 3 most common mistakes people make with it. Then we’ll see how to have automatic alerts to detect issues. And, we’ll touch on the principles for setting up good alerts. As a final step, we’ll see how to build our logging system and how to apply it in the most efficient way to debug issues easily.
CMPE 297 Lecture: Building Infrastructure Clouds with OpenStackJoe Arnold
Lecture for the San Jose State masters program on cloud computing. Topic focuses on using OpenStack to deploy infrastructure clouds with commodity hardware and open source software. Covers virtualization, networking, storage, deployment and operations.
Applying principles and practices towards IaC automation. Presented at ThoughtWorks Gurgaon GeekNight Feb 2018
DEMO: https://github.com/hrmeetsingh/terraform-aws-vpc
Generic Math, funzionalità ora schedulata per .NET 7, e Azure IoT PnP mi hanno risvegliato un argomento che nel mio passato mi hanno portato a fare due/tre viaggi, grazie all'Università di Trieste, a Cambridge (2006/2007 circa) e a Seattle (2010, quando ho parlato pubblicamente per la prima volta di Azure :) e che mi ha fatto conoscere il mito Don Box!), a parlare di codice in .NET che aveva a che fare con la matematica e con la fisica: le unità di misura e le matrici. L'avvento dei Notebook nel mondo .NET e un vecchio sogno legato alla libreria ANTLR (e tutti i miei esercizi di Code Generation) mi portano a mettere in ordine 'sto minestrone di idee...o almeno ci provo (non so se sta tutto in piedi).
Born in a research lab, raised in the enterprise and now a fully open source, cross-platform language for the Cloud and Web, F# has been a key part of the transformation of programming since the early 2000s. Programming now regularly incorporates both functional and object programming techniques, with compositionality, succinctness, methodology and delivery to match.
I’ll share the story of this journey, and why F# continues to challenge the status quo with the simplicity and combining power of its design elements. I’ll discuss the philosophy underlying the F# language design and how we can look at a variety of seemingly unsolvable technical conflicts in different ways, with the aim making progress and improving society as a whole. Of interest to anyone who cares about programming, open and welcoming to all, come along and join in the fun.
Good observability is essential for modern software. It gives us confidence that our systems are working properly. And it also allows us to debug issues efficiently. In this talk, we’ll explore everything you need to know to start applying good observability to your projects. And we’ll see the most common pitfalls you need to be aware of. We will start with the tools and basic concepts in monitoring. And we’ll go over the 3 most common mistakes people make with it. Then we’ll see how to have automatic alerts to detect issues. And, we’ll touch on the principles for setting up good alerts. As a final step, we’ll see how to build our logging system and how to apply it in the most efficient way to debug issues easily.
CMPE 297 Lecture: Building Infrastructure Clouds with OpenStackJoe Arnold
Lecture for the San Jose State masters program on cloud computing. Topic focuses on using OpenStack to deploy infrastructure clouds with commodity hardware and open source software. Covers virtualization, networking, storage, deployment and operations.
Applying principles and practices towards IaC automation. Presented at ThoughtWorks Gurgaon GeekNight Feb 2018
DEMO: https://github.com/hrmeetsingh/terraform-aws-vpc
Generic Math, funzionalità ora schedulata per .NET 7, e Azure IoT PnP mi hanno risvegliato un argomento che nel mio passato mi hanno portato a fare due/tre viaggi, grazie all'Università di Trieste, a Cambridge (2006/2007 circa) e a Seattle (2010, quando ho parlato pubblicamente per la prima volta di Azure :) e che mi ha fatto conoscere il mito Don Box!), a parlare di codice in .NET che aveva a che fare con la matematica e con la fisica: le unità di misura e le matrici. L'avvento dei Notebook nel mondo .NET e un vecchio sogno legato alla libreria ANTLR (e tutti i miei esercizi di Code Generation) mi portano a mettere in ordine 'sto minestrone di idee...o almeno ci provo (non so se sta tutto in piedi).
Born in a research lab, raised in the enterprise and now a fully open source, cross-platform language for the Cloud and Web, F# has been a key part of the transformation of programming since the early 2000s. Programming now regularly incorporates both functional and object programming techniques, with compositionality, succinctness, methodology and delivery to match.
I’ll share the story of this journey, and why F# continues to challenge the status quo with the simplicity and combining power of its design elements. I’ll discuss the philosophy underlying the F# language design and how we can look at a variety of seemingly unsolvable technical conflicts in different ways, with the aim making progress and improving society as a whole. Of interest to anyone who cares about programming, open and welcoming to all, come along and join in the fun.
OS for AI: Elastic Microservices & the Next Gen of MLNordic APIs
AI has been a hot topic lately, with advances being made constantly in what is possible, there has not been as much discussion of the infrastructure and scaling challenges that come with it. How do you support dozens of different languages and frameworks, and make them interoperate invisibly? How do you scale to run abstract code from thousands of different developers, simultaneously and elastically, while maintaining less than 15ms of overhead?
At Algorithmia, we’ve built, deployed, and scaled thousands of algorithms and machine learning models, using every kind of framework (from scikit-learn to tensorflow). We’ve seen many of the challenges faced in this area, and in this talk I’ll share some insights into the problems you’re likely to face, and how to approach solving them.
In brief, we’ll examine the need for, and implementations of, a complete “Operating System for AI” – a common interface for different algorithms to be used and combined, and a general architecture for serverless machine learning which is discoverable, versioned, scalable and sharable.
Advanced Model Inferencing leveraging Kubeflow Serving, KNative and IstioAnimesh Singh
Model Inferencing use cases are becoming a requirement for models moving into the next phase of production deployments. More and more users are now encountering use cases around canary deployments, scale-to-zero or serverless characteristics. And then there are also advanced use cases coming around model explainability, including A/B tests, ensemble models, multi-armed bandits, etc.
In this talk, the speakers are going to detail how to handle these use cases using Kubeflow Serving and the native Kubernetes stack which is Istio and Knative. Knative and Istio help with autoscaling, scale-to-zero, canary deployments to be implemented, and scenarios where traffic is optimized to the best performing models. This can be combined with KNative eventing, Istio observability stack, KFServing Transformer to handle pre/post-processing and payload logging which consequentially can enable drift and outlier detection to be deployed. We will demonstrate where currently KFServing is, and where it's heading towards.
Testing Java Microservices: From Development to ProductionDaniel Bryant
Testing microservices is challenging. Dividing a system into components (à la microservices) naturally creates inter-component dependencies, and each service has its own performance and fault-tolerance characteristics that need to be validated during development, the QA process, and continually in production. Attend this meetup to learn about the theory, techniques, and practices needed to overcome this challenge. You will:
• Get an introduction to the challenges of testing distributed microservice systems
• Learn how to isolate tests within a complex microservice ecosystem
• Hear about several tools for automating vulnerability and security scanning for code, dependencies, and deployment artifacts
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Akash Tandon
ML solutions in production start from data ingestion and extend upto the actual deployment step. We want this workflow to be scalable, portable and simple. Containers and kubernetes are great at the former two but not the latter if you aren't a devops practitioner. We'll explore how you can leverage the Kubeflow project to deploy best-of-breed open-source systems for ML to diverse infrastructures.
Node.js Native AddOns from zero to hero - Nicola Del Gobbo - Codemotion Rome ...Codemotion
This talk is about creating Node.js interfaces for native libraries written in C or C++. It starts with various situations in which you need to build native addons and the common problems in doing that. I'll discuss the reference provided by the new N-API (Node-API) that helps mantainers to support a wide variety of Node.js releases without needing recompilation or abstraction layers. With all these tools and knowledge I'll show you how to build some addons from scratch and how to convert existing addons using the new N-API. The last part is related to future developments about addons.
Utilizing messaging systems grants us the capability to decouple services from each other: downtime of a service consuming messages does not impact the functionality of the service sending messages and vice-versa.
In this talk we will discuss how to setup and use messaging systems. As practical examples, we use AMQP backed by ArtemisMQ, as well as kafka to send and receive messages, automatically as well as programmatically.
A central focus of Quarkus is developer joy and productivity. As part of this focus, Quarkus provides a powerful development mode which, among other things, allows true hot compilation of code.
In this talk we will take a look at this feature in detail. Aside from allowing hot reloading locally, we can also configure our application to allow hot reloading remotely. For example, the application could be running in a docker container or on a kubernetes-/openshift-cluster. This improves Dev/Prod-parity and thus enhances the development lifecycle.
Building advanced Chats Bots and Voice Interactive Assistants - Stève Sfartz ...Codemotion
If it takes minutes to code a simple bot, building professional bots represents quite a challenge. Soon you realize you need serious programming and API architecture experience but also “Bot” specific skills. In this session, we'll first show the code of advanced Chat and Voice interactions, and then explore the challenges faced when building advanced Bots (Context storage, NLP approaches, Bot Metadata, OAuth scopes), and discuss interesting opportunities from latest industry trends (Bot platforms, Serverless, Microservices). This talk is about showing the code and sharing lessons learnt.t
Presented by: Justin Reock
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: In our FluentD vs. Logstash comparison blog we talked about the importance of easily capturing, parsing, and visualizing log data at enterprise scale. We looked at the approaches FluentD and Logstash take to accomplish these tasks and defined particular areas of complexity and challenge that users face. Participants in this demo-driven webinar will watch as a system is configured for log analysis using both approaches, highlighting the strengths and weaknesses of each technology in the process.
Companion Blog: https://www.openlogic.com/blog/fluentd-vs-logstash
This time we'll talk about "Canary Deployment with Traefik". You'll learn what Canary Deployment is and why we should do it in the first place. You'll also have the chance to see a technical live demo.
⚙ Jakub Hajek is going to present a cluster built using K3S (Kubernetes light version), on which he will do Traefik deployment version 2.x and a test application. Then, we will release a newer version and check how we can control the traffic between different versions of the application, deployed in one environment.
In this session we will understand about creating an infrastructure using Terraform. Terraform is an IaC tool that manages infrastructure efficiently.
After this, we will see how we can perform end to end testing on code written with Terraform. So, Terratest is basically a Go Library, which helps to write automated tests for IaC.
How can you handle defects? If you are in a factory, production can produce objects with defects. Or values from sensors can tell you over time that some values are not "normal". What can you do as a developer (not a Data Scientist) with .NET o Azure to detect these anomalies? Let's see how in this session.
OS for AI: Elastic Microservices & the Next Gen of MLNordic APIs
AI has been a hot topic lately, with advances being made constantly in what is possible, there has not been as much discussion of the infrastructure and scaling challenges that come with it. How do you support dozens of different languages and frameworks, and make them interoperate invisibly? How do you scale to run abstract code from thousands of different developers, simultaneously and elastically, while maintaining less than 15ms of overhead?
At Algorithmia, we’ve built, deployed, and scaled thousands of algorithms and machine learning models, using every kind of framework (from scikit-learn to tensorflow). We’ve seen many of the challenges faced in this area, and in this talk I’ll share some insights into the problems you’re likely to face, and how to approach solving them.
In brief, we’ll examine the need for, and implementations of, a complete “Operating System for AI” – a common interface for different algorithms to be used and combined, and a general architecture for serverless machine learning which is discoverable, versioned, scalable and sharable.
Advanced Model Inferencing leveraging Kubeflow Serving, KNative and IstioAnimesh Singh
Model Inferencing use cases are becoming a requirement for models moving into the next phase of production deployments. More and more users are now encountering use cases around canary deployments, scale-to-zero or serverless characteristics. And then there are also advanced use cases coming around model explainability, including A/B tests, ensemble models, multi-armed bandits, etc.
In this talk, the speakers are going to detail how to handle these use cases using Kubeflow Serving and the native Kubernetes stack which is Istio and Knative. Knative and Istio help with autoscaling, scale-to-zero, canary deployments to be implemented, and scenarios where traffic is optimized to the best performing models. This can be combined with KNative eventing, Istio observability stack, KFServing Transformer to handle pre/post-processing and payload logging which consequentially can enable drift and outlier detection to be deployed. We will demonstrate where currently KFServing is, and where it's heading towards.
Testing Java Microservices: From Development to ProductionDaniel Bryant
Testing microservices is challenging. Dividing a system into components (à la microservices) naturally creates inter-component dependencies, and each service has its own performance and fault-tolerance characteristics that need to be validated during development, the QA process, and continually in production. Attend this meetup to learn about the theory, techniques, and practices needed to overcome this challenge. You will:
• Get an introduction to the challenges of testing distributed microservice systems
• Learn how to isolate tests within a complex microservice ecosystem
• Hear about several tools for automating vulnerability and security scanning for code, dependencies, and deployment artifacts
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Akash Tandon
ML solutions in production start from data ingestion and extend upto the actual deployment step. We want this workflow to be scalable, portable and simple. Containers and kubernetes are great at the former two but not the latter if you aren't a devops practitioner. We'll explore how you can leverage the Kubeflow project to deploy best-of-breed open-source systems for ML to diverse infrastructures.
Node.js Native AddOns from zero to hero - Nicola Del Gobbo - Codemotion Rome ...Codemotion
This talk is about creating Node.js interfaces for native libraries written in C or C++. It starts with various situations in which you need to build native addons and the common problems in doing that. I'll discuss the reference provided by the new N-API (Node-API) that helps mantainers to support a wide variety of Node.js releases without needing recompilation or abstraction layers. With all these tools and knowledge I'll show you how to build some addons from scratch and how to convert existing addons using the new N-API. The last part is related to future developments about addons.
Utilizing messaging systems grants us the capability to decouple services from each other: downtime of a service consuming messages does not impact the functionality of the service sending messages and vice-versa.
In this talk we will discuss how to setup and use messaging systems. As practical examples, we use AMQP backed by ArtemisMQ, as well as kafka to send and receive messages, automatically as well as programmatically.
A central focus of Quarkus is developer joy and productivity. As part of this focus, Quarkus provides a powerful development mode which, among other things, allows true hot compilation of code.
In this talk we will take a look at this feature in detail. Aside from allowing hot reloading locally, we can also configure our application to allow hot reloading remotely. For example, the application could be running in a docker container or on a kubernetes-/openshift-cluster. This improves Dev/Prod-parity and thus enhances the development lifecycle.
Building advanced Chats Bots and Voice Interactive Assistants - Stève Sfartz ...Codemotion
If it takes minutes to code a simple bot, building professional bots represents quite a challenge. Soon you realize you need serious programming and API architecture experience but also “Bot” specific skills. In this session, we'll first show the code of advanced Chat and Voice interactions, and then explore the challenges faced when building advanced Bots (Context storage, NLP approaches, Bot Metadata, OAuth scopes), and discuss interesting opportunities from latest industry trends (Bot platforms, Serverless, Microservices). This talk is about showing the code and sharing lessons learnt.t
Presented by: Justin Reock
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: In our FluentD vs. Logstash comparison blog we talked about the importance of easily capturing, parsing, and visualizing log data at enterprise scale. We looked at the approaches FluentD and Logstash take to accomplish these tasks and defined particular areas of complexity and challenge that users face. Participants in this demo-driven webinar will watch as a system is configured for log analysis using both approaches, highlighting the strengths and weaknesses of each technology in the process.
Companion Blog: https://www.openlogic.com/blog/fluentd-vs-logstash
This time we'll talk about "Canary Deployment with Traefik". You'll learn what Canary Deployment is and why we should do it in the first place. You'll also have the chance to see a technical live demo.
⚙ Jakub Hajek is going to present a cluster built using K3S (Kubernetes light version), on which he will do Traefik deployment version 2.x and a test application. Then, we will release a newer version and check how we can control the traffic between different versions of the application, deployed in one environment.
In this session we will understand about creating an infrastructure using Terraform. Terraform is an IaC tool that manages infrastructure efficiently.
After this, we will see how we can perform end to end testing on code written with Terraform. So, Terratest is basically a Go Library, which helps to write automated tests for IaC.
How can you handle defects? If you are in a factory, production can produce objects with defects. Or values from sensors can tell you over time that some values are not "normal". What can you do as a developer (not a Data Scientist) with .NET o Azure to detect these anomalies? Let's see how in this session.
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smartphones. Highlights some frameworks and best practices.
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
Computer Vision Landscape : Present and FutureSanghamitra Deb
Millions of people all around the world Learn with Chegg. Education at Chegg is powered by the depth and diversity of the content that we have. A huge part of our content is in form of images. These images could be uploaded by students or by content creators. Images contain text that is extracted using a transcription service. Very often uploaded images are noisy. This leads to irrelevant characters or words in the transcribed text. Using object detection techniques we develop a service that extracts the relevant parts of the image and uses a transcription service to get clean text. In the first part of the presentation, I will talk about building an object detection model using YOLO for cropping and masking images to obtain a cleaner text from transcription. YOLO is a deep learning object detection and recognition modeling framework that is able to produce highly accurate results with low latency. In the next part of my presentation, I will talk about the building the Computer Vision landscape at Chegg. Starting from images on academic materials that are composed of elements such as text, equations, diagrams we create a pipeline for extracting these image elements. Using state of the art deep learning techniques we create embeddings for these elements to enhance downstream machine learning models such as content quality and similarity.
Automatic License Plate Recognition using OpenCVEditor IJCATR
Automatic License Plate Recognition system is a real time embedded system which automatically recognizes the license plate of vehicles. There are many applications ranging from complex security systems to common areas and from parking admission to urban traffic control. Automatic license plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and speed. Most of the ALPR systems are built using proprietary tools like Matlab. This paper presents an alternative method of implementing ALPR systems using Free Software including Python and the Open Computer Vision Library.
Automatic License Plate Recognition using OpenCV Editor IJCATR
Automatic License Plate Recognition system is a real time embedded system which automatically recognizes the license plate of vehicles. There are many applications ranging from complex security systems to common areas and from parking admission to urban traffic control. Automatic license plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and speed. Most of the ALPR systems are built using proprietary tools like Matlab. This paper presents an alternative method of implementing ALPR systems using Free Software including Python and the Open Computer Vision Library.
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
Leading water utility company in USA was facing a challenge to improve pipeline inspection process to reduce human errors and manual inspection time.Pipeline Anomaly Detection automates the process of identification of defects in pipeline videos, by a camera which notes the observations and lastly it generates the report.
Normalmente parliamo e presentiamo Azure IoT (Central) con un taglio un po' da "maker". In questa sessione, invece, vediamo di parlare allo SCADA engineer. Come si configura Azure IoT Central per il mondo industriale? Dov'è OPC/UA? Cosa c'entra IoT Plug & Play in tutto questo? E Azure IoT Central...quali vantaggi ci da? Cerchiamo di rispondere a queste e ad altre domande in questa sessione...
Allo sviluppatore Azure piacciono i servizi PaaS perchè sono "pronti all'uso". Ma quando proponiamo le nostre soluzioni alle aziende, ci scontriamo con l'IT che apprezza gli elementi infrastrutturali, IaaS. Perchè non (ri)scoprirli aggiungendo anche un pizzico di Hybrid che con il recente Azure Kubernetes Services Edge Essentials si può anche usare in un hardware che si può tenere anche in casa? Quindi scopriremo in questa sessione, tra gli altri, le VNET, le VPN S2S, Azure Arc, i Private Endpoints, e AKS EE.
Static abstract members nelle interfacce di C# 11 e dintorni di .NET 7.pptxMarco Parenzan
Did interfaces in C# need evolution? Maybe yes. Are they violating some fundamental principles? We see. Are we asking for some hoops? Let's see all this by telling a story (of code, of course)
Azure Synapse Analytics for your IoT SolutionsMarco Parenzan
Let's find out in this session how Azure Synapse Analytics, with its SQL Serverless Pool, ADX, Data Factory, Notebooks, Spark can be useful for managing data analysis in an IoT solution.
Power BI Streaming Data Flow e Azure IoT Central Marco Parenzan
Dal 2015 gli utilizzatori di Power BI hanno potuto analizzare dati in real-time grazie all'integrazione con altri prodotti e servizi Microsoft. Con streaming dataflow, si porterà l'analisi in tempo reale completamente all'interno di Power BI, rimuovendo la maggior parte delle restrizioni che avevamo, integrando al contempo funzionalità di analisi chiave come la preparazione dei dati in streaming e nessuna creazione di codice. Per vederlo in funzione, studieremo un caso specifico di streaming come l'IoT con Azure IoT Central.
Power BI Streaming Data Flow e Azure IoT CentralMarco Parenzan
Dal 2015 gli utilizzatori di Power BI hanno potuto analizzare dati in real-time grazie all'integrazione con altri prodotti e servizi Microsoft. Con streaming dataflow, si porterà l'analisi in tempo reale completamente all'interno di Power BI, rimuovendo la maggior parte delle restrizioni che avevamo, integrando al contempo funzionalità di analisi chiave come la preparazione dei dati in streaming e nessuna creazione di codice. Per vederlo in funzione, studieremo un caso specifico di streaming come l'IoT con Azure IoT Central.
Power BI Streaming Data Flow e Azure IoT CentralMarco Parenzan
Since 2015, Power BI users have been able to analyze data in real-time thanks to the integration with other Microsoft products and services. With streaming dataflow, you'll bring real-time analytics completely within Power BI, removing most of the restrictions we had, while integrating key analytics features like streaming data preparation and no coding. To see it in action, we will study a specific case of streaming such as IoT with Azure IoT Central.
What are the actors? What are they used for? And how can we develop them? And how are they published and used on Azure? Let's see how it's done in this session
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.NET is better every year for a developer who still dreams of developing a video game. Without pretensions and without talking about Unity or any other framework, just "barebones" .NET code, we will try to write a game (or parts of it) in the 80's style (because I was a kid in those years). In Christmas style.
Building IoT infrastructure on edge with .net, Raspberry PI and ESP32 to conn...Marco Parenzan
IoT scenarios necessarily pass through the Edge component and the Raspberry PI is a great way to explore this world. If we need to receive IoT events from sensors, how do I implement an MQTT endpoint? Kafka is a clever way to do this. And how do I process the data? Kafka? Spark? Rabbit ?. How do we write custom code for these environments? .NET, now in version 6 is another clever way to do it! And maybe, we can also communicate with Azure. We'll see in this session if we can make it all work!
Quali vantaggi ci da Azure? Dal punto di vista dello sviluppo software, uno di questi è certamente la varietà dei servizi di gestione dei dati. Questo ci permette di cominciare a non essere SQL centrici ma utilizzare il servizio giusto per il problema giusto fino ad applicare una strategia di Polyglot Persistence (e vedremo cosa significa) nel rispetto di una corretta gestione delle risorse IT e delle pratiche di DevOps.
C'è ancora diffidenza nei confronti dell'Internet of Things e il costo delle soluzioni custom non aiuta. Azure IoT Central è un servizio SaaS personalizzabile che rende accessibile a costi sostenibili. Vediamo quali sonole peculiarità di questo servizio.
It happens that we have to develop several services and deploy them in Azure. They are small, repetitive but different, often not very different. Why not use code generation techniques to simplify the development and implementation of these services? Let's see with .NET comes to meet us and helps us to deploy in Azure.
Running Kafka and Spark on Raspberry PI with Azure and some .net magicMarco Parenzan
IoT scenarios necessarily pass through the Edge component and the Raspberry PI is a great way to explore this world. If we need to receive IoT events from sensors, how do I implement an MQTT endpoint? Kafka is a clever way to do this. And how do I process the data in Kafka? Spark is another clever way of doing this. How do we write custom code for these environments? .NET, now in version 6 is another clever way to do it! And maybe, we also communicate with Azure. We'll see in this session if we can make it all work!
Time Series Anomaly Detection with Azure and .NETTMarco Parenzan
f you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
It happens that we have to develop several services and deploy them in Azure. They are small, repetitive but different, often not very different. Why not use code generation techniques to simplify the development and implementation of these services? Let's see with .NET comes to meet us and helps us to deploy in Azure.
Che cosa è .NET interactive? Cosa ha a che fare con .NET? e a cosa ti serve? E se usi Azure, in cosa ti può servire? Vediamo di fare chiarezza in questa sessione.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
First Steps with Globus Compute Multi-User Endpoints
Anomaly Detection with Azure and .net
1.
2. Senior Solution Architect in beanTech
Microsoft Azure MVP
Community Lead 1nn0va // Pordenone
1nn0va After Hour
weekly, every Thuesday evening 9PM CET
https://bit.ly/1nn0va-video
Linkedin: https://www.linkedin.com/in/marcoparenzan/
Marco Parenzan
nov 20, 2022
3. This work is the producy of
prolonged inexperience
Questo lavoro è frutto di una
lunga inesperienza
Beppe Severgnini, «Un italiano in America»
5. How can we implement processing?
Ingest ProcessValue
Storage
Account
Azure
IoT Hub-Related
Services
Devices
Events
?
6. Anomaly detection is the process of identifying unexpected items or events in data
sets (called OUTLIERS), which differ from the norm.
On Anomalies we take decisions.
On data we take decisions.
Anomaly Detection
12. • In time series data, an anomaly or outlier can be termed as a data point which is
not following the common collective trend or seasonal or cyclic pattern of the
entire data and is significantly distinct from rest of the data. By significant, most
data scientists mean statistical significance, which in order words, signify that
the statistical properties of the data point is not in alignment with the rest of the
series.
• Anomaly detection has two basic assumptions:
• Anomalies only occur very rarely in the data.
• Their features differ from the normal instances significantly.
Anomaly Detection in Time Series
14. • How a computer «view»?
• (Convolutional) Neural NetworksDeep Learning
• Transforming images in (very) big arrays (they are arrays already)
• ArraysVectors
• Apply big matrices to vectors (Linear Algebra)
• Apply big matrices more and more times (hidden layers)
• Extracting Features
• On featured vectorsapply «classic» Machine Learning (regression,
classification, clusterization)
• regardless of what these mean
• It it the topic where there is the majority of supervised data
Vision
15. On images you can
Object
Detection
Identify Objects
into an image
Classify
image
Enrich image with
tags
16. How to build an Object
Detector or Image Classifier
17. Model Class Reference Description
Tiny YOLOv2 Redmon et al. A real-time CNN for object detection that detects 20 different classes. A smaller version of the more complex full YOLOv2 network.
SSD Liu et al. Single Stage Detector: real-time CNN for object detection that detects 80 different classes.
SSD-MobileNetV1 Howard et al. A variant of MobileNet that uses the Single Shot Detector (SSD) model framework. The model detects 80 different object classes and locates up to 10 objects in an image.
Faster-RCNN Ren et al. Increases efficiency from R-CNN by connecting a RPN with a CNN to create a single, unified network for object detection that detects 80 different classes.
Mask-RCNN He et al.
A real-time neural network for object instance segmentation that detects 80 different classes. Extends Faster R-CNN as each of the 300 elected ROIs go through 3 parallel branches of the
network: label prediction, bounding box prediction and mask prediction.
RetinaNet Lin et al.
A real-time dense detector network for object detection that addresses class imbalance through Focal Loss. RetinaNet is able to match the speed of previous one-stage detectors and defines
the state-of-the-art in two-stage detectors (surpassing R-CNN).
YOLO v2-coco Redmon et al.
A CNN model for real-time object detection system that can detect over 9000 object categories. It uses a single network evaluation, enabling it to be more than 1000x faster than R-CNN and
100x faster than Faster R-CNN. This model is trained with COCO dataset and contains 80 classes.
YOLO v3 Redmon et al. A deep CNN model for real-time object detection that detects 80 different classes. A little bigger than YOLOv2 but still very fast. As accurate as SSD but 3 times faster.
Tiny YOLOv3 Redmon et al. A smaller version of YOLOv3 model.
YOLOv4 Bochkovskiy et al.
Optimizes the speed and accuracy of object detection. Two times faster than EfficientDet. It improves YOLOv3's AP and FPS by 10% and 12%, respectively, with mAP50 of 52.32 on the
COCO 2017 dataset and FPS of 41.7 on a Tesla V100.
DUC Wang et al.
Deep CNN based pixel-wise semantic segmentation model with >80% mIOU (mean Intersection Over Union). Trained on cityscapes dataset, which can be effectively implemented in self
driving vehicle systems.
FCN Long et al.
Deep CNN based segmentation model trained end-to-end, pixel-to-pixel that produces efficient inference and learning. Built off of AlexNet, VGG net, GoogLeNet classification methods.
contribute
Object Detection with CNNs
18. Model Class Reference Description
MobileNet Sandler et al.
Light-weight deep neural network best suited for mobile and embedded vision applications.
Top-5 error from paper - ~10%
ResNet He et al.
A CNN model (up to 152 layers). Uses shortcut connections to achieve higher accuracy when classifying images.
Top-5 error from paper - ~3.6%
SqueezeNet Iandola et al.
A light-weight CNN model providing AlexNet level accuracy with 50x fewer parameters.
Top-5 error from paper - ~20%
VGG Simonyan et al.
Deep CNN model(up to 19 layers). Similar to AlexNet but uses multiple smaller kernel-sized filters that provides more accuracy when classifying images.
Top-5 error from paper - ~8%
AlexNet Krizhevsky et al.
A Deep CNN model (up to 8 layers) where the input is an image and the output is a vector of 1000 numbers.
Top-5 error from paper - ~15%
GoogleNet Szegedy et al.
Deep CNN model(up to 22 layers). Comparatively smaller and faster than VGG and more accurate in detailing than AlexNet.
Top-5 error from paper - ~6.7%
CaffeNet Krizhevsky et al. Deep CNN variation of AlexNet for Image Classification in Caffe where the max pooling precedes the local response normalization (LRN) so that the LRN takes less compute and memory.
RCNN_ILSVRC13 Girshick et al. Pure Caffe implementation of R-CNN for image classification. This model uses localization of regions to classify and extract features from images.
DenseNet-121 Huang et al. Model that has every layer connected to every other layer and passes on its own feature providing strong gradient flow and more diversified features.
Inception_V1 Szegedy et al.
This model is same as GoogLeNet, implemented through Caffe2 that has improved utilization of the computing resources inside the network and helps with the vanishing gradient problem.
Top-5 error from paper - ~6.7%
Inception_V2 Szegedy et al.
Deep CNN model for Image Classification as an adaptation to Inception v1 with batch normalization. This model has reduced computational cost and improved image resolution compared to
Inception v1.
Top-5 error from paper ~4.82%
ShuffleNet_V1 Zhang et al.
Extremely computation efficient CNN model that is designed specifically for mobile devices. This model greatly reduces the computational cost and provides a ~13x speedup over AlexNet on ARM-
based mobile devices. Compared to MobileNet, ShuffleNet achieves superior performance by a significant margin due to it's efficient structure.
Top-1 error from paper - ~32.6%
ShuffleNet_V2 Zhang et al.
Extremely computation efficient CNN model that is designed specifically for mobile devices. This network architecture design considers direct metric such as speed, instead of indirect metric like
FLOP.
Top-1 error from paper - ~30.6%
ZFNet-512 Zeiler et al.
Deep CNN model (up to 8 layers) that increased the number of features that the network is capable of detecting that helps to pick image features at a finer level of resolution.
Top-5 error from paper - ~14.3%
EfficientNet-Lite4 Tan et al.
CNN model with an order of magnitude of few computations and parameters, while still acheiving state-of-the-art accuracy and better efficiency than previous ConvNets.
Top-5 error from paper - ~2.9%
Image Classifications with CNNs
21. • “…The most important thing to realize about TensorFlow is that, for the most
part, the core is not written in Python: It's written in a combination of highly-
optimized C++ and CUDA (Nvidia's language for programming GPUs). Much of
that happens, in turn, by using Eigen (a high-performance C++ and CUDA
numerical library) and NVidia's cuDNN (a very optimized DNN library for NVidia
GPUs, for functions such as convolutions). The model for TensorFlow is that the
programmer uses "some language" (most likely Python!) to express the
model…”
• “…PyTorch backend is written in C++ which provides API's to access highly
optimized libraries such as; Tensor libraries for efficient matrix operations, CUDA
libaries to perform GPU operations and Automatic differentiation for gradience
calculations etc…”
The truth
22. • TensorFlow, PyTorch, SciKitLearn are frameworks to model, train ML and DL
models and score data with them
• They have:
• A native C++ core model
• A binding in Python (compose/parametrize the model)
Frameworks
23. • ONNX is a machine learning model representation format that is open source.
• ONNX establishes a standard set of operators - the building blocks of machine
learning and deep learning models - as well as a standard file format, allowing AI
developers to utilise models with a range of frameworks, tools, runtimes, and
compilers
• TensorFlow, PyTorch can export their Neural Network in ONNX
ONNX
24. • ML.NET is first and foremost a framework that you can use to
create your own custom ML models. This custom approach
contrasts with “pre-built AI,” where you use pre-designed
general AI services from the cloud (like many of the offerings
from Azure Cognitive Services). This can work great for many
scenarios, but it might not always fit your specific business
needs due to the nature of the machine learning problem or to
the deployment context (cloud vs. on-premises).
• ML.NET enables developers to use their existing .NET skills to
easily integrate machine learning into almost any .NET
application. This means that if C# (or F# or VB) is your
programming language of choice, you no longer have to learn
a new programming language, like Python or R, in order to
develop your own ML models and infuse custom machine
learning into your .NET apps.
Data Science and AI for the .NET developer
25. • Evolution and generalization of the seminal role of Mathematica
• In web standards way
• Web (HTTP+Markdown)
• Python adoption (ipynb)
• Written in Java
• Python has an interop bridge...not native (if ever important)
Jupyter (just to know)
26. • .NET bindings (C# e F#) to Spark
• Written on the Spark interop layer, designed to provide high performance bindings to multiple
languages
• Re-use knowledge, skills, code you have as a .NET developer
• Compliant with .NET Standard
• You can use .NET for Apache Spark anywhere you write .NET code
• Original project Moebius
• https://github.com/microsoft/Mobius
Data Science with Notebooks and .NET (and Spark)...just
to know
29. • A pre-trained service capable of recognizing
images and objects within images
• A great standard service to use if you need a
generic service that can recognize all kinds of
things, and you do not want to go through the
trouble of creating a custom service
• Less accurate on details and specifics than a custom model
• Various APIs as well as a test-bench-page are available
• https://azure.microsoft.com/en-us/services/cognitive-services/computer-
vision/
Microsoft Computer Vision
30. • Taking things a step further…
• Instead of using a pre-trained model, we can create our own model purpose-built
for a specific example
• Leads to better results in a more specific/narrow scenario
• It is a complete service to manage the lifecycle of a Vision-based solution
Custom Vision API Overview
31. 1. Train a Custom Model based on your own data/images
• Can be done through a UI
• Can also be done through programmatic service calls
2. Publish the new Model as your private/personal service
• Often done as part of Azure Cloud
• Can be deployed as local services
3. Call the service from your application
Custom Vision Steps
35. • In time series data, an anomaly or outlier can be termed as a data point which is
not following the common collective trend or seasonal or cyclic pattern of the
entire data and is significantly distinct from rest of the data. By significant, most
data scientists mean statistical significance, which in order words, signify that
the statistical properties of the data point is not in alignment with the rest of the
series.
• Anomaly detection has two basic assumptions:
• Anomalies only occur very rarely in the data.
• Their features differ from the normal instances significantly.
Anomaly Detection in Time Series
36. • Statistical Profiling Approach
• This can be done by calculating statistical values like mean or median moving average of the
historical data and using a standard deviation to come up with a band of statistical values
which can define the uppermost bound and the lower most bound and anything falling
beyond these ranges can be an anomaly.
• By Predictive Confidence Level Approach
• One way of doing anomaly detection with time series data is by building a predictive model
using the historical data to estimate and get a sense of the overall common trend, seasonal
or cyclic pattern of the time series data.
• Clustering Based Unsupervised Approach
• Unsupervised approaches are extremely useful for anomaly detection as it does not require
any labelled data, mentioning that a particular data point is an anomaly.
How to do Time Series Anomaly Detection?
37. • To monitor the time-series continuously and alert for potential incidents on time
• The algorithm first computes the Fourier Transform of the original data. Then it computes
the spectral residual of the log amplitude of the transformed signal before applying the Inverse
Fourier Transform to map the sequence back from the frequency to the time domain. This
sequence is called the saliency map. The anomaly score is then computed as the relative
difference between the saliency map values and their moving averages. If the score is above a
threshold, the value at a specific timestep is flagged as an outlier.
• There are several parameters for SR algorithm. To obtain a model with good performance, we
suggest to tune windowSize and threshold at first, these are the most important parameters to
SR. Then you could search for an appropriate judgementWindowSize which is no larger than
windowSize. And for the remaining parameters, you could use the default value directly.
• Time-Series Anomaly Detection Service at Microsoft [https://arxiv.org/pdf/1906.03821.pdf]
Spectrum Residual Cnn (SrCnn)
38. • .NET Interactive gives C# and F# kernels to Jupyter
• .NET Interactive gives all tools to create your hosting application independently
from Jupyter
• In Visual Studio Code, you have two different notebooks (looking similar but
developed in parallel by different teams)
• .NET Interactive Notebook (by the .NET Interactive Team) that can run also Python
• Jupyter Notebook (by the Azure Data Studio Team – probably) that can run also C# and F#
• There is a little confusion on that
• .NET Interactive has a strong C#/F# Kernel...
• ...a less mature infrastructure (compared to Jupiter)
.NET Interactive and Jupyter
and Visual Studio Code
41. • Don’t think that Data Scientists are «superhuman»
• No modeling from scratch, but tuning, training and testing existing models
• Python has won (?!??!!) but only because it is a lazy world
• And .net is evolving (consistently)
• Azure (cloud) is on-demand super power (training)
Conclusions