Azure saturday Pordenone 2019 - ML.NET model lifecycle with azure devopsMarco Zamana
This document discusses integrating machine learning model lifecycles into DevOps workflows. It describes how an application lifecycle can evolve to include ML model generation, training, testing, evaluation, and automatic deployment. It provides an example of a simple ML.NET application for binary classification and discusses expanding the pipeline to include model building, testing, and deployment. Finally, it discusses improvements like dataset versioning, using databases for training data, different DevOps scenarios, model versioning, and integrating with Azure ML and MLFlow.
Deep dive into Conversational Ai developmentMarco Parenzan
The document discusses developing conversational agents using Azure services. It covers topics like conversational design, intents, entities, domains, and different channels like text, voice, and virtual reality. It also mentions tools for building conversational agents like LUIS for natural language understanding and Adaptive Cards for cross-platform responses. Finally, it provides an overview of Azure cognitive speech services for capabilities like speech to text, text to speech, and speech translation.
Let’s dive into the world of serverless and give you real world examples of how to get started. We will focus on Azure Functions in Java and discuss how to provision, deploy and test them in a productive environment. In my demos we will see the ease of local development leveraging from the great integration in Visual Studio Code. Finally, let’s ship our samples and scale them in Azure. If you are tired of server maintenance and want to achieve more with your java functions , don’t miss this session.
This document discusses Azure Pipelines and common misconceptions about it. It notes that Azure Pipelines can be used for both cloud and on-premises workloads, not just Microsoft technologies, and that maintaining agents is simplified. The document traces the history of Azure Pipelines and its predecessors. It promotes the benefits of defining pipelines in YAML, including storing them in source control, easy copying between repos, and support in Visual Studio Code. Future improvements may include multi-stage pipelines and releasing directly to environments using YAML.
In this session, we will explore how to deploy .net core web apps in azure Kubernetes service using azure DevOps starter and Azure DevOps.
Presented as part of Cloud Community Days on 19th June - ccdays.konfhub.com
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.
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.
Azure saturday Pordenone 2019 - ML.NET model lifecycle with azure devopsMarco Zamana
This document discusses integrating machine learning model lifecycles into DevOps workflows. It describes how an application lifecycle can evolve to include ML model generation, training, testing, evaluation, and automatic deployment. It provides an example of a simple ML.NET application for binary classification and discusses expanding the pipeline to include model building, testing, and deployment. Finally, it discusses improvements like dataset versioning, using databases for training data, different DevOps scenarios, model versioning, and integrating with Azure ML and MLFlow.
Deep dive into Conversational Ai developmentMarco Parenzan
The document discusses developing conversational agents using Azure services. It covers topics like conversational design, intents, entities, domains, and different channels like text, voice, and virtual reality. It also mentions tools for building conversational agents like LUIS for natural language understanding and Adaptive Cards for cross-platform responses. Finally, it provides an overview of Azure cognitive speech services for capabilities like speech to text, text to speech, and speech translation.
Let’s dive into the world of serverless and give you real world examples of how to get started. We will focus on Azure Functions in Java and discuss how to provision, deploy and test them in a productive environment. In my demos we will see the ease of local development leveraging from the great integration in Visual Studio Code. Finally, let’s ship our samples and scale them in Azure. If you are tired of server maintenance and want to achieve more with your java functions , don’t miss this session.
This document discusses Azure Pipelines and common misconceptions about it. It notes that Azure Pipelines can be used for both cloud and on-premises workloads, not just Microsoft technologies, and that maintaining agents is simplified. The document traces the history of Azure Pipelines and its predecessors. It promotes the benefits of defining pipelines in YAML, including storing them in source control, easy copying between repos, and support in Visual Studio Code. Future improvements may include multi-stage pipelines and releasing directly to environments using YAML.
In this session, we will explore how to deploy .net core web apps in azure Kubernetes service using azure DevOps starter and Azure DevOps.
Presented as part of Cloud Community Days on 19th June - ccdays.konfhub.com
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.
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.
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.
Service Fabric is the foundational technology introduced by Microsoft Azure to empower the large-scale Azure service. In this session, you’ll get an overview of containers like Docker after an overview of Service Fabric, explain the difference between it and Kubernetes as a new way To Orchestrate Microservices. You’ll learn how to develop a Microservices application and how to deploy those services to Service Fabric clusters and the new serverless Service Fabric Mesh service. We’ll dive into the platform and programming model advantages including stateful services and actors for low-latency data processing and more. You will learn: Overview of containers Overview of Service Fabric Difference between Kubernetes and Service Fabric Setup Environment to start developing an application using Microservices with Service Fabric.
Shared as part of Cloud Community Days on 17th June 2020 - ccdays.konfhub.com
Building adaptive user experiences using Contextual Multi-Armed Bandits with...HostedbyConfluent
At Expedia Group, providing a customized experience for travellers is key to unlocking the best possibilities for each individual traveller and each type of trip.Contextual multi-armed bandits provide a natural approach to develop personalization of user experience and improve content relevancy. In this talk,we present the end-to-end scalable system developed to democratize the use of contextual bandits at EG.The architecture comprises of an online inference component as well as a continuous feedback loop that tracks the users’ affinity towards certain content or page layouts. Kafka is the backbone of our system, powering high-performance streaming jobs that provide bandits with real-time feedback signals to learn from over time. We describe our experience using Kafka for the user interactions events and bandit feedback messages at scale. Lastly,we look at how we plan to expand our use of Kafka to build an off policy evaluation framework to evaluate the effectiveness of new algorithms.
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
Applying DevOps to Databricks can be a daunting task. In this talk this will be broken down into bite size chunks. Common DevOps subject areas will be covered, including CI/CD (Continuous Integration/Continuous Deployment), IAC (Infrastructure as Code) and Build Agents.
We will explore how to apply DevOps to Databricks (in Azure), primarily using Azure DevOps tooling. As a lot of Spark/Databricks users are Python users, will will focus on the Databricks Rest API (using Python) to perform our tasks.
The document discusses building Azure Functions, which allow creating "nanoservices" that can scale based on demand. Azure Functions support languages like JavaScript, C#, Python, and PHP and can be triggered by events from Azure, third party services, or on-premise systems. Common scenarios for Azure Functions include timer-based processing, processing events from Azure services or SaaS applications, building serverless web applications, and real-time stream/bot processing. The document also lists templates for Functions including triggers for blob, event hub, generic webhooks, GitHub webhooks, HTTP requests, queues, and timers.
Introduction to Azure Functions.
An event-based serverless compute experience to accelerate your development. Scale based on demand and pay only for the resources you consume.
This document discusses serverless computing and Azure Functions. It asks common questions about managing servers like how often to patch and deploy code. It then introduces Azure Functions as an event-driven serverless computing platform that scales instantly based on demand and only charges for the resources used. Azure Functions allows code to run in response to events from sources like Azure Storage, Service Bus and HTTP requests. The document provides examples of common Azure Function triggers and bindings that integrate with other Azure services and external APIs. It also lists resources for learning more about Azure Functions like documentation, code samples and community support.
Azure Functions allow developers to write code that runs in response to events, enabling event-driven architectures. Functions can be triggered by common data sources and services and support multiple programming languages. Functions provide automatic scaling and only run code when triggered, avoiding the need to manage servers. They integrate with other Azure services and can be developed, tested, and deployed using common tools like Visual Studio.
This document summarizes the design, development, deployment, and monitoring of serverless applications using Azure Functions. It outlines best practices for distributed architecture, cloud DevOps, and using Logic Apps for workflow orchestration. The development process involves using Azure Functions Core Tools and bindings to connect triggers and outputs. Deployment is done through Azure Resource Manager templates. Monitoring is done through Application Insights.
Tasos Moustakis, Infrastructure Technology Solutions Manager at Uni Systems, explains how Microsoft Azure migration runs smoothly through Ansible Automation platform. From Cloud Migration Through Automation: Next Level Flexibility virtual event, hosted on September 30, 2020
Accelerating Deployment With Azure DevOps - Murughan and Leena - CCDays CodeOps Technologies LLP
This talk helps you understand why DevOps and the power of Azure DevOps which helps in automating build & release process faster for multiple languages & framework.
Presented as part of Cloud Community Days on 19th June - ccdays.konfhub.com
Martin Abbott discusses using MPI (Message Passing Interface) for parallel computing on Azure Batch. He explains that MPI allows applications to communicate across multiple VMs and is supported on Linux and Windows VMs. Examples of MPI applications include computational fluid dynamics (OpenFOAM) and fire simulation (FDS). The process involves preparing input files, copying files to storage, creating a pool and job, mounting files, running the parallel application using mpirun, and downloading results. Automation is possible using PowerShell and Azure Functions to trigger jobs from a service bus queue.
Improve monitoring and observability for kubernetes with oss toolsNilesh Gule
Slide deck from the Azure Community Conference (https://azconf.dev/) presented on 29th October 2021. The session covered following topics
- Need for centralized logging
- Using ElasticSearch, Fluentd and Kibana (EFK) with Kubernetes
- Need for monitoring
- Using Prometheus & Grafana for infrastructure, application and third party services
- Integration of application with Sentry for Exception aggregation
Serverless compute with Azure Functions abstracts away infrastructure management and allows developers to focus on writing code for triggered operations. Azure Functions supports bindings to data sources and services that avoid writing boilerplate integration code, and can be deployed and managed via the Azure Functions runtime, CLI tools, templates and samples on GitHub.
This document summarizes Starbucks' use of Azure services to power their Workforce Management solution. Some key points:
- Starbucks leverages App Service on Azure to create a scalable and resilient platform for their Workforce Management solution.
- They are able to leverage existing Spring Boot modules with minor modifications and deploy to Azure, reducing need for rearchitecture.
- Automation and infrastructure as code allows them to shorten infrastructure deployment time from 3 months to 3.5 minutes.
- The managed platform of App Service increases productivity as technical teams no longer have to maintain infrastructure.
UK Azure User Group - Blazor and Azure (Tim Ebenezer)Richard Conway
This document summarizes a presentation on using Blazor and Azure in an enterprise environment. It begins with an introduction to Blazor, explaining that it is a front end framework that can run on the client or server using C# and interacts with Azure services. It then compares Blazor to JavaScript frameworks. An example high level Blazor and Azure architecture is shown. Key considerations for deploying Blazor at scale in an enterprise are discussed, including scaling the SignalR service, handling large file uploads, logging to Application Insights, and page lifecycles. A demonstration of SignalR scaling is provided. Follow up reading resources are listed at the end.
The Road Most Traveled: A Kafka Story | Heikki Nousiainen, AivenHostedbyConfluent
When moving to a cloud native architecture Moogsoft knew they needed more scale than Rabbit could provide. Moogsoft moved into Kafka which is known for quick writing and driving heavy event driven workloads on top of niceties such as replayability. Choosing the tool was easy, finding a vendor that ticked all their boxes was not. They needed to ensure scalability, upgradability, builds via existing IAC pipelines, and observability via existing tools. When Moogsoft found Aiven, they were impressed with their offering and ability to scale on demand. During this presentation we will explore how Moogsoft used Aiven for Kafka to manage and scale their data in the cloud.
Reduce Risk with End to End Monitoring of Middleware-based ApplicationsSL Corporation
Kafka communicates within a larger complex and evolving environment. The current modular approach to the integration means that the structure of the software stack is much more dynamic than in the past and operators no longer have the time to become intimate with how dependent components interact. The number of dependencies combined with lack of familiarity can create significant risks to the business including increased outages and longer time to resolve incidents. Both can result in loss of revenue and customers.
These risks are significantly reduced by applying best-practice monitoring. Monitoring can provide a complete end-to-end view of the touch points within the application flow, so they are presented in comprehensive service-based views. This provides the user with a true single-pane of glass for monitoring and alerting for Kafka and its dependent technologies.
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.
Service Fabric is the foundational technology introduced by Microsoft Azure to empower the large-scale Azure service. In this session, you’ll get an overview of containers like Docker after an overview of Service Fabric, explain the difference between it and Kubernetes as a new way To Orchestrate Microservices. You’ll learn how to develop a Microservices application and how to deploy those services to Service Fabric clusters and the new serverless Service Fabric Mesh service. We’ll dive into the platform and programming model advantages including stateful services and actors for low-latency data processing and more. You will learn: Overview of containers Overview of Service Fabric Difference between Kubernetes and Service Fabric Setup Environment to start developing an application using Microservices with Service Fabric.
Shared as part of Cloud Community Days on 17th June 2020 - ccdays.konfhub.com
Building adaptive user experiences using Contextual Multi-Armed Bandits with...HostedbyConfluent
At Expedia Group, providing a customized experience for travellers is key to unlocking the best possibilities for each individual traveller and each type of trip.Contextual multi-armed bandits provide a natural approach to develop personalization of user experience and improve content relevancy. In this talk,we present the end-to-end scalable system developed to democratize the use of contextual bandits at EG.The architecture comprises of an online inference component as well as a continuous feedback loop that tracks the users’ affinity towards certain content or page layouts. Kafka is the backbone of our system, powering high-performance streaming jobs that provide bandits with real-time feedback signals to learn from over time. We describe our experience using Kafka for the user interactions events and bandit feedback messages at scale. Lastly,we look at how we plan to expand our use of Kafka to build an off policy evaluation framework to evaluate the effectiveness of new algorithms.
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
Applying DevOps to Databricks can be a daunting task. In this talk this will be broken down into bite size chunks. Common DevOps subject areas will be covered, including CI/CD (Continuous Integration/Continuous Deployment), IAC (Infrastructure as Code) and Build Agents.
We will explore how to apply DevOps to Databricks (in Azure), primarily using Azure DevOps tooling. As a lot of Spark/Databricks users are Python users, will will focus on the Databricks Rest API (using Python) to perform our tasks.
The document discusses building Azure Functions, which allow creating "nanoservices" that can scale based on demand. Azure Functions support languages like JavaScript, C#, Python, and PHP and can be triggered by events from Azure, third party services, or on-premise systems. Common scenarios for Azure Functions include timer-based processing, processing events from Azure services or SaaS applications, building serverless web applications, and real-time stream/bot processing. The document also lists templates for Functions including triggers for blob, event hub, generic webhooks, GitHub webhooks, HTTP requests, queues, and timers.
Introduction to Azure Functions.
An event-based serverless compute experience to accelerate your development. Scale based on demand and pay only for the resources you consume.
This document discusses serverless computing and Azure Functions. It asks common questions about managing servers like how often to patch and deploy code. It then introduces Azure Functions as an event-driven serverless computing platform that scales instantly based on demand and only charges for the resources used. Azure Functions allows code to run in response to events from sources like Azure Storage, Service Bus and HTTP requests. The document provides examples of common Azure Function triggers and bindings that integrate with other Azure services and external APIs. It also lists resources for learning more about Azure Functions like documentation, code samples and community support.
Azure Functions allow developers to write code that runs in response to events, enabling event-driven architectures. Functions can be triggered by common data sources and services and support multiple programming languages. Functions provide automatic scaling and only run code when triggered, avoiding the need to manage servers. They integrate with other Azure services and can be developed, tested, and deployed using common tools like Visual Studio.
This document summarizes the design, development, deployment, and monitoring of serverless applications using Azure Functions. It outlines best practices for distributed architecture, cloud DevOps, and using Logic Apps for workflow orchestration. The development process involves using Azure Functions Core Tools and bindings to connect triggers and outputs. Deployment is done through Azure Resource Manager templates. Monitoring is done through Application Insights.
Tasos Moustakis, Infrastructure Technology Solutions Manager at Uni Systems, explains how Microsoft Azure migration runs smoothly through Ansible Automation platform. From Cloud Migration Through Automation: Next Level Flexibility virtual event, hosted on September 30, 2020
Accelerating Deployment With Azure DevOps - Murughan and Leena - CCDays CodeOps Technologies LLP
This talk helps you understand why DevOps and the power of Azure DevOps which helps in automating build & release process faster for multiple languages & framework.
Presented as part of Cloud Community Days on 19th June - ccdays.konfhub.com
Martin Abbott discusses using MPI (Message Passing Interface) for parallel computing on Azure Batch. He explains that MPI allows applications to communicate across multiple VMs and is supported on Linux and Windows VMs. Examples of MPI applications include computational fluid dynamics (OpenFOAM) and fire simulation (FDS). The process involves preparing input files, copying files to storage, creating a pool and job, mounting files, running the parallel application using mpirun, and downloading results. Automation is possible using PowerShell and Azure Functions to trigger jobs from a service bus queue.
Improve monitoring and observability for kubernetes with oss toolsNilesh Gule
Slide deck from the Azure Community Conference (https://azconf.dev/) presented on 29th October 2021. The session covered following topics
- Need for centralized logging
- Using ElasticSearch, Fluentd and Kibana (EFK) with Kubernetes
- Need for monitoring
- Using Prometheus & Grafana for infrastructure, application and third party services
- Integration of application with Sentry for Exception aggregation
Serverless compute with Azure Functions abstracts away infrastructure management and allows developers to focus on writing code for triggered operations. Azure Functions supports bindings to data sources and services that avoid writing boilerplate integration code, and can be deployed and managed via the Azure Functions runtime, CLI tools, templates and samples on GitHub.
This document summarizes Starbucks' use of Azure services to power their Workforce Management solution. Some key points:
- Starbucks leverages App Service on Azure to create a scalable and resilient platform for their Workforce Management solution.
- They are able to leverage existing Spring Boot modules with minor modifications and deploy to Azure, reducing need for rearchitecture.
- Automation and infrastructure as code allows them to shorten infrastructure deployment time from 3 months to 3.5 minutes.
- The managed platform of App Service increases productivity as technical teams no longer have to maintain infrastructure.
UK Azure User Group - Blazor and Azure (Tim Ebenezer)Richard Conway
This document summarizes a presentation on using Blazor and Azure in an enterprise environment. It begins with an introduction to Blazor, explaining that it is a front end framework that can run on the client or server using C# and interacts with Azure services. It then compares Blazor to JavaScript frameworks. An example high level Blazor and Azure architecture is shown. Key considerations for deploying Blazor at scale in an enterprise are discussed, including scaling the SignalR service, handling large file uploads, logging to Application Insights, and page lifecycles. A demonstration of SignalR scaling is provided. Follow up reading resources are listed at the end.
The Road Most Traveled: A Kafka Story | Heikki Nousiainen, AivenHostedbyConfluent
When moving to a cloud native architecture Moogsoft knew they needed more scale than Rabbit could provide. Moogsoft moved into Kafka which is known for quick writing and driving heavy event driven workloads on top of niceties such as replayability. Choosing the tool was easy, finding a vendor that ticked all their boxes was not. They needed to ensure scalability, upgradability, builds via existing IAC pipelines, and observability via existing tools. When Moogsoft found Aiven, they were impressed with their offering and ability to scale on demand. During this presentation we will explore how Moogsoft used Aiven for Kafka to manage and scale their data in the cloud.
Reduce Risk with End to End Monitoring of Middleware-based ApplicationsSL Corporation
Kafka communicates within a larger complex and evolving environment. The current modular approach to the integration means that the structure of the software stack is much more dynamic than in the past and operators no longer have the time to become intimate with how dependent components interact. The number of dependencies combined with lack of familiarity can create significant risks to the business including increased outages and longer time to resolve incidents. Both can result in loss of revenue and customers.
These risks are significantly reduced by applying best-practice monitoring. Monitoring can provide a complete end-to-end view of the touch points within the application flow, so they are presented in comprehensive service-based views. This provides the user with a true single-pane of glass for monitoring and alerting for Kafka and its dependent technologies.
Building Cloud-Native App Series - Part 5 of 11
Microservices Architecture Series
Microservices Architecture,
Monolith Migration Patterns
- Strangler Fig
- Change Data Capture
- Split Table
Infrastructure Design Patterns
- API Gateway
- Service Discovery
- Load Balancer
Cloudify your applications: microservices and beyondUgo Landini
The document discusses moving applications to a microservices architecture using Cloudify and Istio. It begins by describing typical customer landscapes today with complex, heterogeneous environments running across virtual and physical infrastructure. It then introduces Cloudify and Istio as platforms that can help modernize existing applications and develop new ones using microservices. Key capabilities of Cloudify and Istio are described such as container platforms, developer tools, and services for integration, automation, security and management.
Early Draft: Service Mesh allows developers to focus on business logic while the crosscutting network data layer code is handled by the Service Mesh. This is a boon because this code can be tricky to implement and hard to test all of the edge cases. Service Mesh takes this a few steps further than AOP or Servlet Filters or custom language-specific frameworks because it works regardless of the underlying programming language being used which is great for polyglot development shops. Thus standardizing how these layers work, while allowing teams to pick the best tools or languages for the job at hand. Kubernetes and Istio Service Mesh automate best practices for DevSecOps needs like: failover, scale-out, scalability, health checks, circuit breakers, rate limiters, metrics, observability, avoiding cascading failure, disaster recovery, and traffic routing; supporting CI/CD and microservices architecture.
Istio’s ability to automate and maintaining zero trust networks is its most important feature. In the age of high-profile data breaches, security is paramount. Companies want to avoid major brand issues that impact the bottom line and shrink market capitalization in an instant. Istio allows a standard way to do mTLS and auto certificate rotation which helps prevent a breach and limits the blast radius if a breach occurs. Istio also takes the concern of mTLS from microservices deployments and makes it easy to use taking the burden off of application developers.
How to build "AutoScale and AutoHeal" systems using DevOps practices by using modern technologies.
A complete build pipeline and the process of architecting a nearly unbreakable system were part of the presentation.
These slides were presented at 2018 DevOps conference in Singapore. http://claridenglobal.com/conference/devops-sg-2018/
Microservices - Hitchhiker's guide to cloud native applicationsStijn Van Den Enden
Microservices are a true hype these days. Netflix, Amazon, eBay, … are all using microservices, but why? The idea is simple; split your application into multiple services which can evolve autonomously through time. The name suggests to keep these services small. Conceptually this seems not all that different from a classical Service Oriented Architecture (SOA). Nonetheless, microservices do offer a new perspective. A monolithic application is divided into a couple small services which can be independently developed, deployed and scaled. Flexibility is increased, but using this model also has some pitfalls.This session sheds a light on the microservices landscape; the key drivers for using the pattern, tooling to support development and maintenance, and the pros and cons that go with it. We’ll also introduce some key design principles that can be used in creating and modelling these modular enterprise applications.
Characterizing and contrasting kuhn tey-ner awr-kuh-streyt-orsLee Calcote
The document provides an overview and comparison of several container orchestration platforms: Docker Swarm, Kubernetes, and Mesos/Marathon. It characterizes each based on their origins, support levels, scheduling approaches, modularity, updating processes, networking implementations, and abilities to scale and maintain high availability. While each has strengths for certain use cases, no single orchestrator is argued to be universally superior.
This document discusses application-driven infrastructure using Crossplane. It introduces Crossplane as a Kubernetes-native framework that allows platform teams to assemble infrastructure from multiple vendors and expose higher-level APIs. This enables development teams to consume infrastructure services without having to write custom code. Crossplane uses the concepts of providers, managed resources, and composite resources to map external services to Kubernetes and provide opinionated APIs through self-service resources. It aims to simplify infrastructure provisioning and management for both platform and application teams.
The document discusses Istio, an open source service mesh that provides traffic management, resilience, and security for microservices applications. It begins with an overview of microservices and common challenges in managing microservices applications. It then introduces Istio and its components that address these challenges, such as intelligent routing, policy enforcement, and telemetry collection. Specific Istio features like traffic control, splitting, and mirroring are demonstrated. Finally, it provides instructions for getting started with Istio and links for additional information.
Driving Systems Stability & Delivery Agility through DevOps [Decoding DevOps ...InfoSeption
The document discusses how VMware IT drives systems stability and delivery agility through DevOps practices. It summarizes how VMware IT automated instance provisioning to reduce provisioning time from 4-6 weeks to under 22 hours. It also discusses how VMware IT uses a cloud operations management platform for instance monitoring and management to improve operational efficiency. Finally, it outlines how VMware IT leverages a continuous delivery platform and service virtualization to improve application delivery agility.
Amazon EKS 그리고 Service Mesh
Kubernetes는 컨테이너 서비스를 도입하는 기업들에게 가장 있기있는 Orchestration 플랫폼입니다. 이 세션에서는 아마존에서 6월 정식 출시한 managed Kubenetes서비스인 EKS를 소개해드리며, 오픈소스 버전과의 차이점 및 장점 등에 대해 설명하고, 진보한 마이크로 서비스인 Service Mesh를 구현하는 Linkerd 소개 및 데모를 진행하고자 합니다.
KCD Italy 2022 - Application driven infrastructure with Crossplanesparkfabrik
Crossplane allows users to extend their Kubernetes clusters using CRDs. The CRDs map any infrastructure or managed service, ensuring that the creation process for the users is as simple as the Kubernetes resources creation. Using a collection of YAML manifests, the development teams can assemble the needed cloud services for their applications removing this duty from the operation teams: this is "shift left" at its best. All this powerfulness comes with a cost in terms of security, governance, cognitive load and maintenance. In this talk we'll discuss strategies and techniques to better map the complexity of this infrastructure.
HL7 Survival Guide - Chapter 10 – Process and WorkflowCaristix
This guide is for healthcare integration analysts and their managers. In this chapter, learn about mapping out your processes and workflows to understand how your interface can support them.
The DevOps paradigm - the evolution of IT professionals and opensource toolkitMarco Ferrigno
This document discusses the DevOps paradigm and tools. It begins by defining DevOps as focusing on communication and cooperation between development and operations teams. It then discusses concepts like continuous integration, delivery and deployment. It provides examples of tools used in DevOps like Docker, Kubernetes, Ansible, and monitoring tools. It discusses how infrastructure has evolved to be defined through code. Finally, it discusses challenges of security in DevOps and how DevOps works aligns with open source principles like meritocracy, metrics, and continuous improvement.
This document summarizes the DevOps paradigm and tools. It discusses how DevOps aims to improve communication and cooperation between development and operations teams through practices like continuous integration, delivery, and deployment. It then provides an overview of common DevOps tools for containers, cluster management, automation, CI/CD, monitoring, and infrastructure as code. Specific tools mentioned include Docker, Kubernetes, Ansible, Jenkins, and AWS CloudFormation. The document argues that adopting open source principles and emphasizing leadership, culture change, and talent growth are important for successful DevOps implementation.
As more applications are being developed as a set of microservices, containers and platforms such as Kubernetes make many things much easier, but still leave untouched many operational issues such as traffic management and visibility, service authentication, security and policy. Istio, is a new service mesh that attempts to address many of these. We will discuss the architecture of Istio and the benefits it may offer to new microservice-based systems in a multicloud world.
DigitalOcean transitioned from inconsistent deployment tools to using Kubernetes for container orchestration. This improved their ability to deploy new services from hours to minutes. They customized Kubernetes by focusing on stateless services, declarative deployments, and abstracting operational concerns. They created "docc" to simplify Kubernetes usage. It allows describing applications and infrastructure through manifests. Docc helped deploy 50 applications in 6 months and powered an internal hackathon. Lessons included keeping up with Kubernetes' rapid changes and automating cluster management. They will invest in service meshes, network policies, and secure secret storage.
How to scale pods and nodes under heavy load? On k8s / AKS we have few options, like horizontal-pod-autoscaler or cluster autoscaler.
In this talk I show these options through some examples.
The document discusses Docker and Kubernetes tools for Visual Studio code. It provides an overview of Docker, how to build Docker images using Dockerfiles, and how to use the Docker extension in VS Code. It also covers developing applications inside Docker containers using the Remote - Containers extension. Finally, it gives a basic introduction to Kubernetes, including nodes, pods, deployments, and services. The presenter demonstrates creating a Dockerfile and deploying to Kubernetes.
Azure Search is a search-as-a-service cloud solution
that gives developers APIs and tools for adding a rich search experience
over private, heterogenous content in web, mobile, and enterprise applications.
This document provides an overview of Kubernetes and microservices architecture. It discusses the challenges with monolithic applications and benefits of microservices. Key Kubernetes concepts are explained like masters, nodes, objects, pods, services and deployments. Azure Kubernetes Service (AKS) is introduced as a way to simplify deploying and managing Kubernetes clusters on Azure without having to self-host the Kubernetes infrastructure.
This document provides an introduction to searching with Elasticsearch. It demonstrates how to perform basic searches on an indexed Twitter dataset using curl commands. It also summarizes Elasticsearch concepts like inverted indexes, analyzers, tokenization, normalization, and filters. Elasticsearch.NET and NEST clients for .NET Core are briefly compared.
This document provides an overview of Azure Dev Spaces, which allows developers to share an Azure Kubernetes Service (AKS) cluster for building and testing applications. It discusses challenges with manually hosting Kubernetes clusters and benefits of AKS, which simplifies Kubernetes deployment and management. Azure Dev Spaces enables developers to test code end-to-end on an AKS cluster without needing to replicate or simulate dependencies. It also allows easy onboarding of new team members with minimal machine setup required. The document concludes with a demonstration of Azure Dev Spaces.
Azure functions: from a function to a whole application in 60 minutesAlessandro Melchiori
This document discusses Azure Functions and serverless computing. It describes how Azure Functions evolved from WebJobs and provides a lightweight way to run .NET code on Azure without having to manage infrastructure. Functions can be triggered by events and use bindings to integrate with data sources. The document demonstrates how to create Function Apps locally or on Azure using the CLI or portal, and how to configure runtime versions and bindings. It also introduces the Durable Functions extension for orchestrating function workflows and chaining or fan out/fan in functions.
This document provides an overview of Kubernetes and microservices architectures. It discusses the differences between monolithic and microservices applications and the advantages and disadvantages of each. It then introduces Kubernetes, including its origins at Google, components like the master, nodes, and objects. It covers management techniques like imperative commands, imperative object configuration, and declarative object configuration. Finally, it discusses key Kubernetes concepts like pods, services, and deployments. It also compares manually hosting a Kubernetes cluster to using Azure Kubernetes Service.
How to build a monitoring system for docker from scratch and how to use Azure Operations Management Suite (aka OMS) to collect info about docker cluster deployment
This document discusses cooking Akka.Net and Service Fabric together. It provides an overview of Service Fabric architecture including its cluster model and application model. It describes Service Fabric Reliable Actors and Reliable Collections. It demonstrates Service Fabric Reliable Actors, integrating Service Fabric and Akka.Net, and using Service Fabric for persistence with Akka.Net. It also covers upgrading Service Fabric applications.
Azure SQL Database is a relational database-as-a-service hosted in the Azure cloud that reduces costs by eliminating the need to manage virtual machines, operating systems, or database software. It provides automatic backups, high availability through geo-replication, and the ability to scale performance by changing service tiers. Azure Cosmos DB is a globally distributed, multi-model database that supports automatic indexing, multiple data models via different APIs, and configurable consistency levels with strong performance guarantees. Azure Redis Cache uses the open-source Redis data structure store with managed caching instances in Azure for improved application performance.
The document discusses Docker and Azure. It provides an overview of Docker's architecture including registries, images and containers. It describes how Docker can be used to implement microservices with a layered architecture. It then discusses using private registries like Docker Hub or building your own, as well as Azure Container Registry. It demonstrates running dockerized applications on a single VM, cluster with orchestrator, or Azure Container Service. It also demonstrates a CI/CD pipeline and questions are taken at the end.
Introduzione al protocollo websocket e come implementarlo "manualmente" in un'applicazione asp.net
SignalR: architettura di base, come utilizzare la libreria nei nostri progetti e come configurare i "backplane" per scenari di scale-out.
Quick-overview sulla nuova versione di SignalR per dot.net core
This document provides an overview of Azure Service Fabric, including:
1) Service Fabric is a distributed systems platform that makes it easy to package, deploy, and manage scalable and reliable microservices and containers.
2) It allows applications to be composed of small, independent processes called microservices that can communicate with each other.
3) Service Fabric handles deployment, scaling and management of microservice applications and containers, enabling developers to focus on writing code without having to deal with infrastructure details.
This document discusses functional reactive programming (FRP). It defines FRP as a programming paradigm oriented around data flows and propagation of change. Reactive programming describes systems that are responsive, resilient, elastic and message-driven. The document introduces reactive extensions (Rx) as a library for composing asynchronous and event-based programs using observable sequences and LINQ-style query operators. It provides examples of how Rx can be used to map, filter, scan and zip observable sequences.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
2. What does “reliable” mean?
Reliable applications are:
● Resilient and recover gracefully from failures, and they continue to
function with minimal downtime and data loss before full recovery.
● Highly available (HA) and run as designed in a healthy state with no
significant downtime.
3. What does “reliable” mean?
Reliable applications are:
● Resilient and recover gracefully from failures, and they continue to
function with minimal downtime and data loss before full recovery.
● Highly available (HA) and run as designed in a healthy state with no
significant downtime.
4. What does “reliable” mean?
Instead of trying to prevent failures altogether, the goal is
to minimize the effects of a single failing component.
5. With the network of microservices - service-to-service
communication can become challenging
19. Welcome Polly
Polly is a .NET resilience and transient-fault-
handling library that allows developers to
express policies such as Retry, Circuit Breaker,
Timeout, Bulkhead Isolation, and Fallback in a
fluent and thread-safe manner.
22. Pod overview
● Is the basic building block of Kubernetes
● Represents a running process on the
cluster
● Consists of either a single container or a
small number of containers that are
tightly coupled and that share resources
23. Pod overview
● Is the basic building block of Kubernetes
● Represents a running process on the
cluster
● Consists of either a single container or a
small number of containers that are
tightly coupled and that share resources
24. Sidecar pattern
The sidecar pattern consists of a main
application plus a helper container with
a responsibility that is essential to your
application, but is not necessarily part of
the application itself.
The most common sidecar containers
are logging utilities, sync services,
watchers, and monitoring agents.
25. Traefik
An open-source reverse proxy and load balancer for
HTTP and TCP-based applications that is easy, dynamic,
automatic, fast, full-featured, production proven,
provides metrics, and integrates with every major cluster
technology... No wonder it's so popular!
30. Others Traefik middlewares
● RateLimit:
The RateLimit middleware ensures that services will receive a fair number
of requests, and allows you define what is fair
● Retry:
The Retry middleware is in charge of reissuing a request a given number of
times to a backend server if that server does not reply.
31. Cross-cutting concerns
● Logs
● Metrics
○ Datadog
○ InfluxDB
○ Prometheus
○ StatsD
● Tracing: the tracing system allows developers to visualize call flows in their
infrastructure.
○ Zipkin
○ Datadog
○ Instana
○ ...