Introduction to AWS Lambda, serverless architectures, & the new AWS Serverless Application Model.
Source code for demo serverless application available here:
https://github.com/infectedsoundsystem/lambda-refarch-webapp
AWS Lambda and Serverless framework: lessons learned while building a serverl...Luciano Mammino
油
The document discusses lessons learned from building a serverless company. It introduces Planet 9 Energy and their use of AWS Lambda and the Serverless framework. Key topics covered include security, quality assurance, developer experience, costs, and lessons learned. Some challenges discussed are debugging, API Gateway custom domains, and Lambda limitations. The document emphasizes that serverless architectures provide infinite scalability at low cost but also have some limitations that require management.
This document summarizes an AWS User Group Italy meeting presentation about AWS Lambda.
AWS Lambda allows writing code to automatically run in response to events, allowing building applications from small functions that are triggered by changes to data in AWS services. It provides an easy way to run code without having to manage infrastructure. Lambda functions can be triggered by events in services like S3, DynamoDB, and Kinesis. With Lambda, the focus is on writing business logic rather than managing servers. Lambda automatically scales as needed to handle event loads, and customers pay only for the compute time used rather than having to pay for unused capacity. A preview of Lambda is open for people to try it out with support for S3, Kinesis and Dynam
際際滷s for a short presentation I gave on AWS Lambda, which "lets you run code without provisioning or managing servers". Lambda is to running code as Amazon S3 is to storing objects.
Presentation from the developer track at I Love APIs London 2016 featuring Matt McClean, Amazon Web Services.
Developers have been jumping on the microservices bandwagon because of the obvious benefits of faster release cycles and innovation. However, microservices' downside is the increased server costs, operational costs, and performance costs. To reduce this complexity, Amazon Web Services created AWS Lambda - a compute platform that lets you build microservices with no provisioning and servers.
Matt McClean, Solution Architect from AWS, presents how to use AWS Lambda to build your microservices. He covers various architectural patterns and anti-patterns for using AWS Lambda.
This document provides information on serverless computing platforms from Azure, AWS, and Google. It outlines the supported programming languages and runtimes for each platform's functions as well as common event sources that can trigger function execution, such as S3 buckets, queues, and HTTP requests. It also lists serverless database options and notes that serverless computing allows for automatic scaling of resources and reduced management overhead compared to traditional reserved servers.
The document discusses the Serverless Framework, which is a build tool that makes it easy to setup and invoke Lambda functions on AWS. It allows developers to manage multiple Lambda functions across different regions and stages, and share code between functions. The Serverless Framework supports Node.js, Python, Java/Scala and other runtimes. Functions are defined in a serverless.yml file along with triggers like HTTP events, S3 uploads, schedules and more. The framework handles deployments to AWS and other cloud providers.
This document provides an introduction and overview of AWS Lambda. It discusses how Lambda allows executing code without provisioning or managing servers by uploading code and configuring triggers. Code can be written in Node.js, Java, or Python and executed in response to events from AWS services or API calls. Metrics and logs of Lambda function invocations are automatically sent to CloudWatch for monitoring. An example of using Lambda for thumbnail image creation in response to S3 uploads is also provided.
Security Boundaries and Functions of Services for Serverless Architectures on...AWS Germany
油
Vortrag "Security Boundaries and Functions of Services for Serverless Architectures on AWS" von Bertram Dorn beim AWS Serverless Web Day. Alle Videos und Pr辰sentationen finden Sie hier: http://amzn.to/28QIaxM
Connecting the Unconnected: IoT Made SimpleDanilo Poccia
油
Connecting physical devices to the cloud can enhance the user experience. AWS IoT is a new managed service that enables Internet-connected things (sensors, actuators, devices, and applications) to easily and securely interact with each other and the cloud. In this session, we will discuss how constrained devices can send data to the cloud and receive commands back to the device. Devices can securely connect using MQTT, HTTP protocols and developers can leverage several features of AWS IoT such as the Rules Engine and Thing Shadows to quickly and easily build a real connected product. This session will take a practical approach to developing real-world IoT and mobile applications in which the back end is serverless and can scale from one to virtually unlimited users without any infrastructure or servers to manage.
This document discusses AWS Lambda, a platform as a service for event-driven microservices. It provides an introduction and overview of AWS Lambda and how it can be used to process events from services like S3, Kinesis, and DynamoDB using Node.js functions. Examples are given of how AWS Lambda could be used to build a serverless home security system that processes images uploaded to S3.
Big Data: Architectures and ApproachesThoughtworks
油
ThoughtWorkers David Elliman and Ashok Subramanian present how the big data world is moving quickly with predictions of amazing industry growth. For more information on how the 'Internet of Things' is playing an increasingly larger role, read David's blog post or watch the video from the London-based event. http://www.thoughtworks.com/insights/blog/big-data-and-internet-things
Big Data Analytics: Reference Architectures and Case Studies by Serhiy Haziye...SoftServe
油
BI architecture drivers have to change to satisfy new requirements in format, volume, latency, hosting, analysis, reporting, and visualization. In this presentation delivered at the 2014 SATURN conference, SoftServe`s Serhiy and Olha showcased a number of reference architectures that address these challenges and speed up the design and implementation process, making it more predictable and economical:
- Traditional architecture based on an RDMBS data warehouse but modernized with column-based storage to handle a high load and capacity
- NoSQL-based architectures that address Big Data batch and stream-based processing and use popular NoSQL and complex event-processing solutions
- Hybrid architecture that combines traditional and NoSQL approaches to achieve completeness that would not be possible with either alone
The architectures are accompanied by real-life projects and case studies that the presenters have performed for multiple companies, including Fortune 100 and start-ups.
Microservices on AWS using AWS Lambda and Docker ContainersDanilo Poccia
油
Using AWS Lambda and Docker Containers to build a Microservice Architecture on Amazon Web Services.
From the AWS User Group Hungary meeting in Budapest on Friday March 20th, 2015.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
Microservice Architecture on AWS using AWS Lambda and Docker ContainersDanilo Poccia
油
This document summarizes microservice architecture on AWS using AWS Lambda and Docker containers. It discusses why organizations adopt microservices and how to design smaller loosely coupled services. It then covers how to use AWS services like EC2 Container Service, Lambda, ECS, and others to build and deploy microservices. Specific topics covered include distributed systems, security, testing, monitoring, discovery, deployment pipelines and more. Examples of Netflix and Hailo architectures on AWS are also mentioned.
Lambda architecture for real time big dataTrieu Nguyen
油
- The document discusses the Lambda Architecture, a system designed by Nathan Marz for building real-time big data applications. It is based on three principles: human fault-tolerance, data immutability, and recomputation.
- The document provides two case studies of applying Lambda Architecture - at Greengar Studios for API monitoring and statistics, and at eClick for real-time data analytics on streaming user event data.
- Key lessons discussed are keeping solutions simple, asking the right questions to enable deep analytics and profit, using reactive and functional approaches, and turning data into useful insights.
A comprehensive walkthrough of how to manage infrastructure-as-code using Terraform. This presentation includes an introduction to Terraform, a discussion of how to manage Terraform state, how to use Terraform modules, an overview of best practices (e.g. isolation, versioning, loops, if-statements), and a list of gotchas to look out for.
For a written and more in-depth version of this presentation, check out the "Comprehensive Guide to Terraform" blog post series: https://blog.gruntwork.io/a-comprehensive-guide-to-terraform-b3d32832baca
Big Data and Fast Data - Lambda Architecture in ActionGuido Schmutz
油
Big Data (volume) and real-time information processing (velocity) are two important aspects of Big Data systems. At first sight, these two aspects seem to be incompatible. Are traditional software architectures still the right choice? Do we need new, revolutionary architectures to tackle the requirements of Big Data?
This presentation discusses the idea of the so-called lambda architecture for Big Data, which acts on the assumption of a bisection of the data-processing: in a batch-phase a temporally bounded, large dataset is processed either through traditional ETL or MapReduce. In parallel, a real-time, online processing is constantly calculating the values of the new data coming in during the batch phase. The combination of the two results, batch and online processing is giving the constantly up-to-date view.
This talk presents how such an architecture can be implemented using Oracle products such as Oracle NoSQL, Hadoop and Oracle Event Processing as well as some selected products from the Open Source Software community. While this session mostly focuses on the software architecture of BigData and FastData systems, some lessons learned in the implementation of such a system are presented as well.
This talk is a very quick intro to Docker, Terraform, and Amazon's EC2 Container Service (ECS). In just 15 minutes, you'll see how to take two apps (a Rails frontend and a Sinatra backend), package them as Docker containers, run them using Amazon ECS, and to define all of the infrastructure-as-code using Terraform.
Infrastructure as code: running microservices on AWS using Docker, Terraform,...Yevgeniy Brikman
油
This is a talk about managing your software and infrastructure-as-code that walks through a real-world example of deploying microservices on AWS using Docker, Terraform, and ECS.
Getting Started with AWS Lambda & Serverless CloudIan Massingham
油
This document provides an overview of serverless computing using AWS Lambda. It defines serverless computing as running code without servers by paying only for the compute time consumed. AWS Lambda allows triggering functions from events or APIs which makes it easy to build scalable back-ends, perform data processing, and integrate systems. Recent updates include support for Python, scheduled functions, VPC access, and versioning. The document demonstrates using Lambda for building serverless web apps and microservices.
AWS Lambda allows developers to run code without managing servers. It is a serverless computing platform that automatically scales compute capacity based on incoming requests or events. The guide discusses key Lambda concepts like triggers, execution environments, and function versions. It also provides steps to create a sample "Hello World" function using Java, including setting up development environments, deploying the function, and testing. In summary, AWS Lambda is a flexible and cost-effective option for building applications with varying workloads.
This document discusses AWS Lambda and serverless computing. It provides an introduction to AWS Lambda, the benefits of using serverless computing by running code without managing servers, and getting started using Lambda. Example use cases for Lambda include API backends, image compression workflows triggered by S3 uploads, cron jobs managed by CloudWatch events, and data pipelines. The document also covers alternatives to AWS Lambda and provides references for further information.
Speaker spoke about features and benefits of the AWS Lambda service and explained how to increase system performance by using AWS services.
This presentation by Mykhailo Brodskyi (Senior Software Engineer, Consultant, GlobalLogic, Kharkiv), was delivered at GlobalLogic Kharkiv Java Conference 2018 on June 10, 2018.
Webinar: Serverless Architectures with AWS Lambda and MongoDB AtlasMongoDB
油
Its easier than ever to power serverless architectures with our managed MongoDB as a service, MongoDB Atlas. In this session, we will explore the rise of serverless architectures and how theyve rapidly integrated into public and private cloud offerings.
This document provides an introduction and overview of AWS Lambda. It discusses how Lambda allows executing code without provisioning or managing servers by uploading code and configuring triggers. Code can be written in Node.js, Java, or Python and executed in response to events from AWS services or API calls. Metrics and logs of Lambda function invocations are automatically sent to CloudWatch for monitoring. An example of using Lambda for thumbnail image creation in response to S3 uploads is also provided.
Security Boundaries and Functions of Services for Serverless Architectures on...AWS Germany
油
Vortrag "Security Boundaries and Functions of Services for Serverless Architectures on AWS" von Bertram Dorn beim AWS Serverless Web Day. Alle Videos und Pr辰sentationen finden Sie hier: http://amzn.to/28QIaxM
Connecting the Unconnected: IoT Made SimpleDanilo Poccia
油
Connecting physical devices to the cloud can enhance the user experience. AWS IoT is a new managed service that enables Internet-connected things (sensors, actuators, devices, and applications) to easily and securely interact with each other and the cloud. In this session, we will discuss how constrained devices can send data to the cloud and receive commands back to the device. Devices can securely connect using MQTT, HTTP protocols and developers can leverage several features of AWS IoT such as the Rules Engine and Thing Shadows to quickly and easily build a real connected product. This session will take a practical approach to developing real-world IoT and mobile applications in which the back end is serverless and can scale from one to virtually unlimited users without any infrastructure or servers to manage.
This document discusses AWS Lambda, a platform as a service for event-driven microservices. It provides an introduction and overview of AWS Lambda and how it can be used to process events from services like S3, Kinesis, and DynamoDB using Node.js functions. Examples are given of how AWS Lambda could be used to build a serverless home security system that processes images uploaded to S3.
Big Data: Architectures and ApproachesThoughtworks
油
ThoughtWorkers David Elliman and Ashok Subramanian present how the big data world is moving quickly with predictions of amazing industry growth. For more information on how the 'Internet of Things' is playing an increasingly larger role, read David's blog post or watch the video from the London-based event. http://www.thoughtworks.com/insights/blog/big-data-and-internet-things
Big Data Analytics: Reference Architectures and Case Studies by Serhiy Haziye...SoftServe
油
BI architecture drivers have to change to satisfy new requirements in format, volume, latency, hosting, analysis, reporting, and visualization. In this presentation delivered at the 2014 SATURN conference, SoftServe`s Serhiy and Olha showcased a number of reference architectures that address these challenges and speed up the design and implementation process, making it more predictable and economical:
- Traditional architecture based on an RDMBS data warehouse but modernized with column-based storage to handle a high load and capacity
- NoSQL-based architectures that address Big Data batch and stream-based processing and use popular NoSQL and complex event-processing solutions
- Hybrid architecture that combines traditional and NoSQL approaches to achieve completeness that would not be possible with either alone
The architectures are accompanied by real-life projects and case studies that the presenters have performed for multiple companies, including Fortune 100 and start-ups.
Microservices on AWS using AWS Lambda and Docker ContainersDanilo Poccia
油
Using AWS Lambda and Docker Containers to build a Microservice Architecture on Amazon Web Services.
From the AWS User Group Hungary meeting in Budapest on Friday March 20th, 2015.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
Microservice Architecture on AWS using AWS Lambda and Docker ContainersDanilo Poccia
油
This document summarizes microservice architecture on AWS using AWS Lambda and Docker containers. It discusses why organizations adopt microservices and how to design smaller loosely coupled services. It then covers how to use AWS services like EC2 Container Service, Lambda, ECS, and others to build and deploy microservices. Specific topics covered include distributed systems, security, testing, monitoring, discovery, deployment pipelines and more. Examples of Netflix and Hailo architectures on AWS are also mentioned.
Lambda architecture for real time big dataTrieu Nguyen
油
- The document discusses the Lambda Architecture, a system designed by Nathan Marz for building real-time big data applications. It is based on three principles: human fault-tolerance, data immutability, and recomputation.
- The document provides two case studies of applying Lambda Architecture - at Greengar Studios for API monitoring and statistics, and at eClick for real-time data analytics on streaming user event data.
- Key lessons discussed are keeping solutions simple, asking the right questions to enable deep analytics and profit, using reactive and functional approaches, and turning data into useful insights.
A comprehensive walkthrough of how to manage infrastructure-as-code using Terraform. This presentation includes an introduction to Terraform, a discussion of how to manage Terraform state, how to use Terraform modules, an overview of best practices (e.g. isolation, versioning, loops, if-statements), and a list of gotchas to look out for.
For a written and more in-depth version of this presentation, check out the "Comprehensive Guide to Terraform" blog post series: https://blog.gruntwork.io/a-comprehensive-guide-to-terraform-b3d32832baca
Big Data and Fast Data - Lambda Architecture in ActionGuido Schmutz
油
Big Data (volume) and real-time information processing (velocity) are two important aspects of Big Data systems. At first sight, these two aspects seem to be incompatible. Are traditional software architectures still the right choice? Do we need new, revolutionary architectures to tackle the requirements of Big Data?
This presentation discusses the idea of the so-called lambda architecture for Big Data, which acts on the assumption of a bisection of the data-processing: in a batch-phase a temporally bounded, large dataset is processed either through traditional ETL or MapReduce. In parallel, a real-time, online processing is constantly calculating the values of the new data coming in during the batch phase. The combination of the two results, batch and online processing is giving the constantly up-to-date view.
This talk presents how such an architecture can be implemented using Oracle products such as Oracle NoSQL, Hadoop and Oracle Event Processing as well as some selected products from the Open Source Software community. While this session mostly focuses on the software architecture of BigData and FastData systems, some lessons learned in the implementation of such a system are presented as well.
This talk is a very quick intro to Docker, Terraform, and Amazon's EC2 Container Service (ECS). In just 15 minutes, you'll see how to take two apps (a Rails frontend and a Sinatra backend), package them as Docker containers, run them using Amazon ECS, and to define all of the infrastructure-as-code using Terraform.
Infrastructure as code: running microservices on AWS using Docker, Terraform,...Yevgeniy Brikman
油
This is a talk about managing your software and infrastructure-as-code that walks through a real-world example of deploying microservices on AWS using Docker, Terraform, and ECS.
Getting Started with AWS Lambda & Serverless CloudIan Massingham
油
This document provides an overview of serverless computing using AWS Lambda. It defines serverless computing as running code without servers by paying only for the compute time consumed. AWS Lambda allows triggering functions from events or APIs which makes it easy to build scalable back-ends, perform data processing, and integrate systems. Recent updates include support for Python, scheduled functions, VPC access, and versioning. The document demonstrates using Lambda for building serverless web apps and microservices.
AWS Lambda allows developers to run code without managing servers. It is a serverless computing platform that automatically scales compute capacity based on incoming requests or events. The guide discusses key Lambda concepts like triggers, execution environments, and function versions. It also provides steps to create a sample "Hello World" function using Java, including setting up development environments, deploying the function, and testing. In summary, AWS Lambda is a flexible and cost-effective option for building applications with varying workloads.
This document discusses AWS Lambda and serverless computing. It provides an introduction to AWS Lambda, the benefits of using serverless computing by running code without managing servers, and getting started using Lambda. Example use cases for Lambda include API backends, image compression workflows triggered by S3 uploads, cron jobs managed by CloudWatch events, and data pipelines. The document also covers alternatives to AWS Lambda and provides references for further information.
Speaker spoke about features and benefits of the AWS Lambda service and explained how to increase system performance by using AWS services.
This presentation by Mykhailo Brodskyi (Senior Software Engineer, Consultant, GlobalLogic, Kharkiv), was delivered at GlobalLogic Kharkiv Java Conference 2018 on June 10, 2018.
Webinar: Serverless Architectures with AWS Lambda and MongoDB AtlasMongoDB
油
Its easier than ever to power serverless architectures with our managed MongoDB as a service, MongoDB Atlas. In this session, we will explore the rise of serverless architectures and how theyve rapidly integrated into public and private cloud offerings.
Serverless design considerations for Cloud Native workloadsTensult
油
We have built a news website with more than a billion views per month and we are sharing the learnings from that experience covering Serverless architectures, Design considerations, and Gotchas.
Deploying Serverless Cloud Optical Character Recognition in Support of NASA A...Chris Shenton
油
Presentation to the NASA Cloud Community of Interest on how we leveraged AWS Lambda in the GovCloud to do high volume OCR of design documents to improve astronaut safety.
This document contains a summary of best practices for using Lambda and DynamoDB presented by Yan Cui. Some key recommendations include implementing observability from the start by using metrics and alerts to monitor performance, creating separate AWS accounts for each team and environment, loading secrets securely from SSM Parameter Store at runtime, following the principle of least privilege with IAM policies, parallelizing functions where possible, and various DynamoDB optimizations like using DocumentClient and PAY_PER_REQUEST billing. The document emphasizes that best practices depend on individual contexts and situations.
Serverless Optical Character Recognition in support of Astronaut Safety AWS M...Chris Shenton
油
The document summarizes a serverless optical character recognition (OCR) architecture developed to support NASA astronaut safety. It discusses:
1) The problem of life-threatening spacesuit failures that motivated developing OCR to digitize engineering documents.
2) The challenges of scaling OCR to process large document volumes and current reporting processes that could not be changed.
3) The evolution of architectures from on-premise servers that could not scale, to cloud servers with scaling challenges, and finally to a serverless architecture with automatic scaling.
4) Key aspects of the serverless OCR architecture developed using AWS Lambda and S3, including security controls, integration with other systems, and benefits of low
Serverless OCR for NASA EVA: AWS Meetup DC 2017-12-12Chris Shenton
油
NASA's EVA group needs to extract text (OCR) from 100,000 pages per month to improve Astronaut safety. This presentation shows how we used AWS Lambda serverless tech to solve the problem quickly and cost-effectively.
Introduction to AWS lambda & Serverless Application1.pptxMohammed Shefeeq
油
AWS lambda is a serverless computing platform that allows developers to create and deploy applications without provisioning or managing servers.
It was released in November 2014. The service runs code in response to events, such as API calls or page views, and automatically manages the compute resources required by that code, scaling up or down in response to demand.
In this PPT we will cover the basics of AWS lambda and how it can be used for building serverless applications.
We will also take a look at the tools needed for creating and testing AWS lambda-based applications.
AWS SAM is a tool for developing serverless applications on AWS. It helps you create and deploy functions that are triggered by events such as HTTP requests, Amazon S3 bucket events, DynamoDB table events, or other AWS services.
We will also cover Building and deploying a hello world using SAM in this section
This presentation provides a comprehensive overview of the Transactional Outbox Pattern and the Inbox Pattern, two essential techniques for ensuring reliable and consistent communication in distributed systems.
We start by clearly outlining the problem these patterns aim to solvenamely, maintaining data consistency between databases and message brokers in event-driven architectures. From there, we delve into what the Outbox Pattern is, how it works under the hood, and how it guarantees message delivery even in the face of failures.
The presentation then shifts focus to the Inbox Pattern, explaining its role in ensuring idempotency and preventing duplicate processing of messages. Each concept is explained with simple language, diagrams, and a logical flow that builds a solid understanding from the ground up.
Whether youre an engineer building microservices or just exploring distributed system patterns, this talk provides clarity, practical insights, and a helpful demo to see the patterns in action.
Topics Covered:
* Problem Statement
* Transactional Outbox Pattern
* How It Solves the Problem
* Internal Mechanics
* Delivery Guarantees
* Inbox Pattern Explained
* Internal Workflow
* Conclusions & Further Reading
* Demo
Sugarlab AI: How Much Does an XXX AI Porn Generator Cost in 2025Sugarlab AI
油
The cost of an XXX AI porn generator in 2025 varies depending on factors like AI sophistication, subscription plans, and additional expenses. Whether you're looking for a free AI porn video generator or a premium adult AI image generator, pricing ranges from basic tools to enterprise-level solutions. This article breaks down the costs, features, and what to expect from AI-driven adult content platforms.
Getting the Best of TrueDEM April News & Updatespanagenda
油
Webinar Recording: https://www.panagenda.com/webinars/getting-the-best-of-truedem-april-news-updates/
Boost your Microsoft 365 experience with OfficeExpert TrueDEM! Join the April webinar for a deep dive into recent and upcoming features and functionalities of OfficeExpert TrueDEM. Well showcase whats new and use practical application examples and real-life scenarios, to demonstrate how to leverage TrueDEM to optimize your M365 environment, troubleshoot issues, improve user satisfaction and productivity, and ultimately make data-driven business decisions.
These sessions will be led by our team of product management and consultants, who interact with customers daily and possess in-depth product knowledge, providing valuable insights and expert guidance.
What youll take away
- Updates & info about the latest and upcoming features of TrueDEM
- Practical and realistic applications & examples for troubelshooting or improving your Microsoft Teams & M365 environment
- Use cases and examples of how our customers use TrueDEM
Benefits of Moving Ellucian Banner to Oracle CloudAstuteBusiness
油
Discover the advantages of migrating Ellucian Banner to Oracle Cloud Infrastructure, including scalability, security, and cost efficiency for educational institutions.
Mastering Azure Durable Functions - Building Resilient and Scalable WorkflowsCallon Campbell
油
The presentation aims to provide a comprehensive understanding of how Azure Durable Functions can be used to build resilient and scalable workflows in serverless applications. It includes detailed explanations, application patterns, components, and constraints of Durable Functions, along with performance benchmarks and new storage providers.
AI Driven Posture Analysis Fall Detection System for the Elderly.pdfPatrick Ogbuitepu
油
This project introduces an innovative, cost-effective solution for real-time activity monitoring of elderly individuals. By leveraging the MediaPipe pose estimation model, fuzzy logic, and finite state machines, the system can reliably track individuals, recognize static postures (standing, sitting, lying), and detect transitions, particularly focusing on falls. A key achievement is the systems zero false alarm rate, a significant advancement in vision-based fall detection systems. While the system shows promise, it faces limitations in scenarios with severe occlusions or low lighting conditions. To address these challenges, future work
will explore the use of multi-camera setups, interactive calibration modes, and audio feedback to enhance accuracy and user experience. This prototype represents a significant step towards reliable, real-time elder care. By combining advanced AI techniques with practical considerations, this system offers a scalable and effective solution to a pressing societal need.
Scot-Secure is Scotlands largest annual cyber security conference. The event brings together senior InfoSec personnel, IT leaders, academics, security researchers and law enforcement, providing a unique forum for knowledge exchange, discussion and high-level networking.
The programme is focussed on improving awareness and best practice through shared learning: highlighting emerging threats, new research and changing adversarial tactics, and examining practical ways to improve resilience, detection and response.
All-Data, Any-AI Integration: FME & Amazon Bedrock in the Real-WorldSafe Software
油
Join us for an exclusive webinar featuring special guest speakers from Amazon, Amberside Energy, and Avineon-Tensing as we explore the power of Amazon Bedrock and FME in AI-driven geospatial workflows.
Discover how Avineon-Tensing is using AWS Bedrock to support Amberside Energy in automating image classification and streamlining site reporting. By integrating Bedrocks generative AI capabilities with FME, image processing and categorization become faster and more efficient, ensuring accurate and organized filing of site imagery. Learn how this approach reduces manual effort, standardizes reporting, and leverages AWSs secure AI tooling to optimize their workflows.
If youre looking to enhance geospatial workflows with AI, automate image processing, or simply explore the potential of FME and Bedrock, this webinar is for you!
Threat Modeling a Batch Job System - AWS Security Community DayTeri Radichel
油
I've been working on building a batch job framework for a few years now and blogging about it in the process. This presentation explains how and why I started building and writing about this system and the reason it changed from deploying one simple batch job to a much bigger project. I explore a number of recent data breaches, how they occurred, and what may have prevented them along the way. We consider how what make goes into an effective security architecture and well-designed security controls that avoid common pitfalls. There are friend links to many blog posts in the notes of the presentation that bypass the paywall. Topics include security architecture, IAM, encryption (KMS), networking, MFA, source control, separation of duties, supply chain attacks, and more.
SAP Automation with UiPath: Solution Accelerators and Best Practices - Part 6...DianaGray10
油
Join us for a comprehensive webinar on SAP Solution Accelerators and best practices for implementing them using UiPath. This session is designed to help SAP professionals and automation enthusiasts understand how to effectively leverage UiPaths SAP Solution Accelerators to automate standard SAP process quickly. Learn about the benefits, best ways to do it, and real-world success stories to speed up.
Smarter RAG Pipelines: Scaling Search with Milvus and FeastZilliz
油
About this webinar
Learn how Milvus and Feast can be used together to scale vector search and easily declare views for retrieval using open source. Well demonstrate how to integrate Milvus with Feast to build a customized RAG pipeline.
Topics Covered
- Leverage Feast for dynamic metadata and document storage and retrieval, ensuring that the correct data is always available at inference time
- Learn how to integrate Feast with Milvus to support vector-based retrieval in RAG systems
- Use Milvus for fast, high-dimensional similarity search, enhancing the retrieval phase of your RAG model
Migrating to the Isolated worker process in Azure Functions .pptxCallon Campbell
油
"Migrating to the Isolated worker process in Azure Functions" provides an overview of serverless computing and different hosting models, with a focus on migrating to the isolated worker process in Azure Functions. The presentation covers the benefits of the isolated worker process, including fewer conflicts, full control of the process, and dependency injection. It also includes demos and practical steps for migrating existing .NET applications to the isolated worker process. The presentation aims to help developers modernize their applications and prepare for future versions of .NET.
Build Your Uber Clone App with Advanced FeaturesV3cube
油
Build your own ride-hailing business with our powerful Uber clone app, fully equipped with advanced features to give you a competitive edge. Start your own taxi business today!
More Information : https://www.v3cube.com/uber-clone/
Research Data Management (RDM): the management of dat in the research processHeilaPienaar
油
Presented as part of the M.IT degree at the Department of Information Science, University of Pretoria, South Africa. Module: Data management. 2023, 2024.
Automated Engineering of Domain-Specific Metamorphic Testing EnvironmentsPablo G坦mez Abajo
油
Context. Testing is essential to improve the correctness of software systems. Metamorphic testing (MT) is an approach especially suited when the system under test lacks oracles, or they are expensive to compute. However, building an MT environment for a particular domain (e.g., cloud simulation, model transformation, machine learning) requires substantial effort.
Objective. Our goal is to facilitate the construction of MT environments for specific domains.
Method. We propose a model-driven engineering approach to automate the construction of MT environments. Starting from a meta-model capturing the domain concepts, and a description of the domain execution environment, our approach produces an MT environment featuring comprehensive support for the MT process. This includes the definition of domain-specific metamorphic relations, their evaluation, detailed reporting of the testing results, and the automated search-based generation of follow-up test cases.
Results. Our method is supported by an extensible platform for Eclipse, called Gotten. We demonstrate its effectiveness by creating an MT environment for simulation-based testing of data centres and comparing with existing tools; its suitability to conduct MT processes by replicating previous experiments; and its generality by building another MT environment for video streaming APIs.
Conclusion. Gotten is the first platform targeted at reducing the development effort of domain-specific MT environments. The environments created with Gotten facilitate the specification of metamorphic relations, their evaluation, and the generation of new test cases.
3. What is AWS Lambda?
A compute service
No infrastructure resources...
to provision
to set up
to manage
to pay for 24/7 whether in use or not
Scales automatically
Only pay for what is used
4. What is AWS Lambda?
BYOC - Bring Your Own Code
Available runtimes:
Node.js v4.3
Python 2.7
Java 8
C# (.NET Core 1.0.1)
Behind the scenes: executes within Amazon Linux containers
5. Lambda Functions
Confusingly named
Stateless
Store state in DynamoDB, S3, etc...
Can cache in /tmp - unreliably
Will run if...
Configured to act on events (e.g. S3 bucket writes)
Directly invoked via the API
Scheduled to
Configured as an endpoint for API Gateway
This allows its use in serverless
6. Lambda Functions
Can call primitive Linux utilities
Can create threads/processes
These can be frozen
Package function in a zip
Can include libraries
Can include native libraries
Can include native binaries
Max 50 MB compressed/250 MB uncompressed
7. Lambda Function Configuration
Allocate memory
128 MB 1536 MB
Affects CPU resources allocated
Set timeout (up to 300 seconds)
Can VPC-enable with an ENI (so can access private resources)
Can set up dead letter queue (Lambda retries failures twice)
Environment vars, versioning, aliases
8. Lambda Function Execution
From cold:
1. New container is created
2. Lambda function code copied to it
3. Lambda function is initialised
4. Lambda function is executed
5. Container is frozen when function terminates/times out
This also can be referred to as warmed/hot - yay, confusion!
Steps #1 -> #3 can take time, ~1 to 9 seconds
Python least delay from cold, Java longest
9. Lambda Function Execution
If warm container is used:
1. Container is thawed
2. Lambda function is executed
3. Container is frozen when function terminates/timeout
Step #1 takes < 100ms
Java has least delay when warm
Note: no guarantees that warm container will be used
11. Lambda in use in production
Web form serverless backend
Virus scanning & resizing uploaded files
Simple automatic image thumbnailer
Connecting AWS alarms to internal monitoring
Dynamically calculating subnet CIDRs for CloudFormation stacks
Processing config changes & alerting on unsafe actions
...and more
12. Benefits of Lambda
Quick to create and deploy
Connective tissue for AWS resources
Reduce server maintenance
Potentially unlimited instant scalability
Smaller attack surface
Cheap! (Generous permanent free tier)
1m requests/month free, $0.0000002 per request after
400,000 GB-seconds compute/month free, $0.00001667 per GB-s after
16. AWS CloudFormation
To provision the AWS resources
Uses JSON/YAML templates
Allows to define infrastructure as code
Transforms AWS Serverless Application Model definitions
17. Amazon S3
Object storage
One bucket stores the Lambda code to deploy via CloudFormation
Another bucket stores the website HTML and assets
This bucket is configured for static website hosting
18. Amazon DynamoDB
NoSQL database PaaS with built-in HA/durability
Two tables, one for votes and one for vote aggregation
DynamoDB Streams enable reading and processing updates to the vote table
19. Amazon Cognito
Service that provides identity management and data sync
Primarily used for mobile applications
Used here to provide an identity pool to allow browser-side code to run as an
unauthenticated user that has IAM permissions to scan the DynamoDB vote
aggregation table directly
20. Amazon API Gateway
Allows to create publicly available REST APIs
Done by creating resources, and methods for these
These manage the requests and responses when invoking our Lambda function
One resource used here for voting via the website
Gives a public HTTPS endpoint for a Lambda function to be triggered via an
Ajax request containing the users vote
21. AWS Lambda
Four Lambda functions used here:
One to register votes received via the website
One that listens to a DynamoDB stream from the votes table, and aggregates the votes when
there are changes
One used as CloudFormation custom resource to create/configure our Cognito Identity Pool
One used as CFN custom resource to automatically configure static files with resource values
22. AWS Serverless Application Model
Used to define serverless applications on AWS
Based on CloudFormation
Defined in CFN templates and deployed as CFN stack
SAM objects simplify definition of common serverless resources
CloudFormation transformation expands these to normal CFN resource
definitions
https://github.com/awslabs/serverless-application-model
28. Over traditional server architectures
Modularity and separation
Isolated deployments and failures
No ongoing server maintenance
Potentially unlimited instant scalability
Smaller attack surface
Cheap!
29. > 90%
Running cost reduction when compared to a traditional HA server architecture.
(Thats before factoring in additional cost savings from reduced man-hours
incurred performing maintenance etc...)
31. Compared to traditional server architectures
Not every web application will be suited for it
Occasional latency spike due to Lambda container creation (additional ~2-3 secs
average for Node.js runtime)
Requires initial learning curve to enable design and development of appropriate
application architectures