These are slides from a talk I gave at UCSB to the Senior Capstone class on 02/10/10 on how I developed the Call of Cthulhu application using Google App Engine.
Tech Talk on Autoscaling in Apache StratosVishanth Bala
This document provides an overview of autoscaling in Apache Stratos. It discusses the autoscaling architecture, which uses a CEP engine to analyze metrics like requests, memory usage, and CPU usage to predict future loads and scale instances accordingly. The autoscaling workflow and lifecycle are also described. Autoscale policies define thresholds that trigger scaling, and rules engines use rules to determine the appropriate scaling action. Dependent and group scaling are also covered, where scaling is triggered based on dependencies between cartridges or to scale an entire group. The presentation concludes with a demo and Q&A.
AWS Webcast - Customizing AWS ops works with chef 11 and Amazon machine imagesAmazon Web Services
AWS OpsWorks lets you model and visualize your application with layers that define how to configure a set of resources that are managed together. Did you also know that you have two ways to customize your instances? In this session we show you how use custom AMIs as the foundation for your instances to improve boot speeds and Chef recipes to dynamically install, configure, and update software
StreamSQL Feature Store (Apache Pulsar Summit)Simba Khadder
Input features are the building blocks for machine learning models. You cannot have a great model without great features. By building on top of Apache Pulsar's infinite retention of events, we built infrastructure to serve features in production and to generate training datasets. It allowed our machine learning teams to change, test, and deploy personalization features at an extraordinary rate to 10s of millions of end-users.
This talk will discuss:
- What event-sourcing is and why it's so powerful for machine learning infrastructure.
- How we built the StreamSQL feature store on top of Pulsar, Flink, and Cassandra.
- How a feature store accelerates ML development.
Amazon EC2 Container Service: Manage Docker-Enabled Apps in EC2Amazon Web Services
Amazon EC2 Container Service (Amazon ECS) is a new AWS service that makes it easy to run and manage Docker-enabled applications across a cluster of Amazon EC2 instances. Amazon ECS lets you define, schedule, and stop sets of containers. You have access to the state of your resources, making it easy to confirm that tasks are running or view the utilization of EC2 instances in your cluster. This session will describe the benefits of containers, introduce ECS, and demonstrate how to use ECS for your applications.
An introductory presentation on Chatbots with Serverless (AWS Lambda). It covers AWS Lez, its terminologies and AWS Lambda in detail. It also showcases on how to connect your Lex bot to Facebook Messenger.
The document contains a 10 question quiz about serverless computing concepts. It begins by explaining that the term "lambda" in serverless functions originates from Alonzo Church's "lambda calculus" in 1936. Each question is then presented along with the corresponding answer and brief explanation. The questions cover topics like principles for creating lambda functions, suitable and unsuitable use cases for serverless, where to store credentials, characteristics of serverless functions, the "freeze/thaw cycle", and approaches to avoid for state management.
(DEV302) Hosting ASP.Net 5 Apps in AWS with Docker & AWS CodeDeployAmazon Web Services
The .NET Platform is undergoing a revolution with a new modularized .NET Framework and CoreCLR, a new cross platform runtime. ASP.NET 5 gives .NET developers the ability to develop and run their applications outside of Windows. In this session we will explore how to develop and deploy ASP.NET 5 applications on Windows with AWS CodeDeploy and Linux with Docker. For Docker we will explore using Docker with both Elastic Beanstalk and EC2 Container Service.
Sascha Möllering gave a presentation on deploying applications to the AWS cloud. He began with an overview of AWS services like EC2, S3, RDS and explained how to initially create a simple cloud service with one instance each for a web application and database. He then described how to improve the architecture by separating components, adding redundancy and elasticity using services like ELB, autoscaling and read replicas. Sascha demonstrated deploying a sample application built with JHipster and Docker to AWS Elastic Beanstalk, which handles running the containers and mapping environment variables for the database connection.
This document summarizes an event-driven architecture presentation using Java. It discusses using Apache Kafka/Amazon Kinesis for messaging, Docker for containerization, Vert.x for reactive applications, Apache Camel/AWS Lambda for integration, and Google Protocol Buffers for data serialization. It covers infrastructure components, software frameworks, local and AWS deployment, and integration testing between Kinesis and Kafka. The presentation provides resources for code samples and Docker images discussed.
Heterogeneous Workflows With Spark At NetflixJen Aman
This document discusses Netflix's use of the Meson workflow system to manage heterogeneous machine learning workflows at scale on their Spark clusters. Meson is a general purpose workflow orchestration framework that delegates execution to resource managers like Mesos. It is optimized for machine learning pipelines and supports standard and custom step types, parameter passing between steps, and multi-tenancy of large Spark clusters running hundreds of concurrent jobs.
This document discusses serverless architectures using AWS Lambda. It provides an overview of serverless computing and AWS Lambda, outlines some common use cases and challenges at OpsGenie, and describes their serverless technology stack. Some key points include:
- AWS Lambda allows running code without managing servers and only paying for the compute time used
- OpsGenie uses AWS Lambda along with other serverless AWS services like DynamoDB, S3, and API Gateway for various use cases including reporting, indexing data to Elasticsearch, and a service management pilot
- Challenges of using serverless include Java cold starts, proper monitoring without agents, and deployment processes
(APP309) Running and Monitoring Docker Containers at Scale | AWS re:Invent 2014Amazon Web Services
If you have tried Docker but are unsure about how to run it at scale, you will benefit from this session. Like virtualization before, containerization (à; la Docker) is increasing the elastic nature of cloud infrastructure by an order of magnitude. But maybe you still have questions: How many containers can you run on a given Amazon EC2 instance type? Which metric should you look at to measure contention? How do you manage fleets of containers at scale?
Datadog is a monitoring service for IT, operations, and development teams who write and run applications at scale. In this session, the cofounder of Datadog presents the challenges and benefits of running containers at scale and how to use quantitative performance patterns to monitor your infrastructure at this magnitude and complexity. Sponsored by Datadog.
Eric Holmes from Remind discussed building an internal Platform as a Service (PaaS) called Empire using Docker and Amazon EC2 Container Service (ECS). Remind started on Heroku but encountered issues with scaling and visibility. Empire provides a management layer on top of ECS for deploying and scaling microservices. It implements a subset of the Heroku API and provides a single binary and CLI. Empire is running 15 of Remind's production services on ECS with improved performance over Heroku. A demo was shown of deploying a sample app with Empire.
Getting Started with AWS provides an overview of fundamental AWS services and steps to get started using AWS. It covers creating an AWS account, SSH keys for access, security groups for firewall rules, launching EC2 virtual machines, connecting to instances, taking EBS volume snapshots for backups, monitoring with CloudWatch alarms, and using S3 storage. The presentation aims to give attendees a hands-on introduction to common AWS services needed for basic deployment and management of cloud resources.
Give your little scripts big wings: Using cron in the cloud with Amazon Simp...Amazon Web Services
Most developers write them and every company has them – a vast library of small and large scripts that are designed to run on a scheduled basis. These background angels help keep the lights on and the doors open. They’ve been built up over time and are forgotten little heroes that are only remembered when the machines they live on fail. They are scattered throughout a company’s IT infrastructure and do important things.
In this session, we will explain how to use Ruby on Simple Workflow to quickly build a system that schedules scripts, runs them on time, retries them if they fail, and stores the history of their execution. You will walk away from this session with an understanding of how Simple Workflow brings resiliency, concurrency, and tracking to your applications.
This document provides an overview of Linux containers, Docker, and Kubernetes. It discusses how Linux containers have limitations that Docker aimed to address by providing a platform for managing containers. However, standalone Docker has issues at scale, which Kubernetes was created to solve by offering clustering and orchestration of Docker containers across multiple hosts. Key Kubernetes concepts are explained such as pods, labels, services, and deployments. The document concludes with a reference to a Kubernetes demo.
Automating Application over OpenStack using WorkflowsYaron Parasol
This document discusses automating DevOps processes through orchestration and workflows. It introduces Petsy, a pet art company that needs to automate deployments. Common DevOps workflows like deployment, infrastructure upgrades, and scaling are described. The document then introduces the Cloudify project which uses TOSCA-inspired building blocks like nodes, relationships, and workflows to automate application topology deployment and management across clouds. A live demo of automatically deploying a Mezzanine application is shown. The document concludes by discussing how Cloudify integrates with the OpenStack ecosystem through the Solum project.
The document discusses the TaskFlow framework, which provides state management for workflows. It allows workflows to be paused, resumed, and recovered from failures. Key concepts discussed include tasks, flows, jobs, engines, and persistence. Tasks represent individual operations, flows compose tasks, and jobs are initial task/flow sets. Engines control execution and support different implementations. Persistence tracks progress to enable recovery. Patterns like linear, unordered, and graph impose ordering on tasks. The document provides examples and motivates state management for reliable, consistent workflows.
OpenStack Atlanta Summit - Build an OpenStack Cluster Before Lunch, Scale Glo...Michael Fork
(1) The document discusses how to build an OpenStack cluster using SoftLayer infrastructure before lunch and scale it globally by supper.
(2) It provides step-by-step instructions on setting up a three-node OpenStack cluster on SoftLayer and pricing out the necessary components.
(3) The document also describes one person's experience building an OpenStack cluster across two regions with shared Keystone and Horizon services, noting it took around 9 hours once neutron issues were resolved.
This document provides an overview of OpenStack Networking (Neutron) and the different networking plugins and configurations available in Neutron. It discusses the Nova network manager, the Neutron OpenvSwitch plugin configured for VLAN and GRE tunneling modes, Neutron security groups, and Neutron's software defined networking capabilities. Diagrams and examples of packet flows are provided to illustrate how networks are logically and physically implemented using the different Neutron plugins.
OpenStack networking can use either VLAN tagging or GRE tunneling to provide logical isolation between tenant networks. With VLAN, packets are tagged with a VLAN ID at the compute and network nodes to associate them with a particular tenant network. With GRE, packets are encapsulated with a GRE header that includes a tunnel ID to associate them with a tenant network. Security groups are applied using iptables rules to filter traffic between VMs in different networks.
Horizon is a dashboard for managing OpenStack services powered by Django. It includes key packages like openstack_dashboard, horizon, and openstack_auth. The horizon package defines common components like tables, workflows, and forms. The openstack_dashboard package contains the actual Django project including dashboards and panels. Developers can create new dashboards and panels by using the provided commands and adding the appropriate configuration files.
OpenStack Horizon: Controlling the Cloud using DjangoDavid Lapsley
OpenStack is an open source cloud computing project that is implemented predominantly in Python. OpenStack’s goal is to provide the "ubiquitous open source cloud computing platform for public and private clouds”. The OpenStack project was launched by Rackspace and NASA in July 2010. Since then the project has gained considerable momentum with over 200 companies joining the project, and the launch of commercial services and products that use OpenStack.
The OpenStack Horizon project provides a web-based User Interface to OpenStack services. It is constructed in two parts: (1) a core set of libraries for implementing a Dashboard; (2) a reference dashboard implementation that uses the core set of libraries. Customization is a core part of the Horizon Framework. The framework enables developers to construct their own dashboards, panel groups and panels, and enables them to assemble them together via a common navigation/presentation framework.
In this presentation, we will provide a brief introduction to OpenStack and Horizon. Then we will dive into the details of Horizon. We will review Horizon’s overall architecture and how it integrates with other OpenStack services. We will look at some of Horizon’s interesting features and describe how to get started developing with Horizon. Finally, we will discuss some of the current challenges facing Horizon and some future directions.
The document discusses OpenStack Neutron and Software Defined Networks (SDN). It begins with an agenda for a demonstration of Neutron including creating networks, spawning VMs, testing connectivity, and creating load balancers. It then provides an overview of Neutron components and architecture, including the modular layer 2 plugin. It demonstrates Neutron APIs and network namespaces. It introduces SDN concepts like the control plane and network virtualization. Finally, it discusses how Neutron enforces SDN through plugins like PLUMgrid that implement the functionality on software edges in compute nodes.
What is NFV? How does it relate to SDN, what does it mean for the telecommunications industry, and why should anyone outside of that industry care?
Presentation delivered at CloudOpen Europe, Düsseldorf, October 2014
This document provides an overview and agenda for a presentation on OpenStack networking. It begins with an overview of OpenStack architecture and services like Compute, Networking, Identity and Image services. It then discusses basic network components like controllers, compute nodes and networking plugins. Next, it covers networking process flows and dives deeper into the Neutron networking plugin, including the Modular Layer 2 plugin framework and drivers like Open vSwitch. It concludes with a planned demonstration of networking functionality in an OpenStack lab environment.
Deploying & Scaling OpenShift on OpenStack using Heat - OpenStack Seattle Mee...OpenShift Origin
This document provides an overview and agenda for deploying OpenShift on OpenStack. It begins with a brief introduction to Platform as a Service (PaaS) and OpenShift. It then discusses the various flavors of OpenShift including the open source Origin project, public cloud service, and on-premise private cloud software. The remainder of the document focuses on deploying OpenShift on OpenStack using Heat templates, including an overview of Heat and its orchestration capabilities, the OpenShift architecture, and a demonstration of deploying OpenShift Enterprise templates with Heat.
This was a tutorial which Mark McClain and I led at ONUG, Spring 2015. It was well received and serves as a walk through of OpenStack Neutron and it's features and usage.
This document provides an introduction to OpenFlow, SDN, and NFV. It describes the need for new networking paradigms and outlines some of the key problems with traditional networking approaches. OpenFlow is presented as providing open interfaces and programmability to network nodes. SDN is defined as separating the control logic from the forwarding plane and enabling programmable automation through open APIs. NFV aims to virtualize network functions to improve flexibility, reduce costs, and accelerate service deployment using standard IT virtualization technologies.
Introduction to Software Defined Networking (SDN)rjain51
Class lecture by Prof. Raj Jain on Introduction to . The talk covers Origins of SDN, What is SDN?, Original Definition of SDN, What = Why We need SDN?, SDN Definition, XMPP, XMPP in Data Centers, Path Computation Element, PCE, Forwarding and Control Element, Sample ForCES Exchanges, Application Layer Traffic Optimization, ALTO, ALTO Extension, Current SDN Debate: What vs. How?, SDN Controller Functions, RESTful APIs, OSGi Framework, Open Daylight SDN Controller, OpenDaylight Tools, Affinity Metadata Service, SDN Related Organizations and Projects, SDN Web Sites, Hierarchy of Operations, Introduction to, Origins of SDN, What is SDN?, Original Definition of SDN, What = Why We need SDN?, SDN Definition, XMPP, XMPP in Data Centers, Path Computation Element, PCE, Forwarding and Control Element, Sample ForCES Exchanges, Application Layer Traffic Optimization, ALTO, ALTO Extension, Current SDN Debate: What vs. How?, SDN Controller Functions, RESTful APIs, OSGi Framework, Open Daylight SDN Controller, OpenDaylight Tools, Affinity Metadata Service, SDN Related Organizations and Projects, SDN Web Sites. Video recording available in YouTube.
This document provides an introduction and overview of Google App Engine and developing applications with Python on the platform. It discusses what App Engine is, who uses it, how much it costs, recommended development tools and frameworks, and some of the key services provided like the datastore, blobstore, task queues, and URL fetch. It also notes some limitations of App Engine and alternatives to running your own version of the platform.
Having Fun Building Web Applications (Day 1 Slides)Clarence Ngoh
This document outlines the agenda and content for Day 1 of a web applications course. The instructor will cover building the front end of web applications using HTML, CSS, JavaScript, jQuery and templating. Students will practice implementing common front end patterns and components. They will scaffold and style a matchmaking application, adding user profiles, events and APIs. The goal is to build out the front end interface without a backend, focusing on design, interactivity and data loading.
Introduction to Google App Engine with PythonBrian Lyttle
Google App Engine is a cloud development platform that allows users to build and host web applications on Google's infrastructure. It provides automatic scaling for applications and manages all server maintenance. Development is done locally in Python and code is pushed to the cloud. The platform provides data storage, user authentication, URL fetching, task queues, and other services via APIs. While initially limited to Python and Java, it now supports other languages as well. Usage is free for small applications under a monthly quota, and priced based on usage for larger applications.
This document provides an overview of Google App Engine, including what cloud computing is, the different types of cloud computing models, how App Engine provides a scalable infrastructure, the programming languages and frameworks supported, how data is stored and accessed via the datastore, services available on App Engine like caching, task queues, and mail, and tips for testing and deploying App Engine applications.
The document provides an overview of Google App Engine, a platform for developing and hosting web applications on Google's infrastructure. It discusses the different language runtimes, services, and development tools available on App Engine and highlights some example applications that have been built on the platform. The document also shares experiences from Latin American users and details some new features recently added to App Engine like cursors, task queues, and cron jobs.
- The document discusses Google's Prediction API which allows users to build machine learning models and make predictions by uploading training data, training models on that data, and then making predictions on new data.
- It provides an example of using the Prediction API to automatically categorize and respond to customer emails by language by training on tagged emails and predicting the language of new emails.
- The process involves uploading training data, training a model on that data, and then making predictions on new data using the trained model to receive a predicted language label.
Jeff Scudder, Eric Bidelman
The number of APIs made available for Google products has exploded from a handful to a slew! Get
the big picture on what is possible with the APIs for everything from YouTube, to Spreadsheets, to
Search, to Translate. We'll go over a few tools to help you get started and the things these APIs share
in common. After this session picking up new Google APIs will be a snap.
The document discusses the Play framework, an agile web development framework created by Guillaume Bort in 2007. It provides an overview of Play's main concepts including its stateless MVC architecture, ability to fix bugs and reload code without restarting, efficient templating, and support for test-driven development. The document also covers getting started with Play and using modules to add additional functionality.
Serverless ML Workshop with Hopsworks at PyData SeattleJim Dowling
1. The document discusses building a minimal viable prediction service (MVP) to predict air quality using only Python and free serverless services in 90 minutes.
2. It describes creating feature, training, and inference pipelines to build an air quality prediction service using Hopsworks, Modal, and Streamlit/Gradio.
3. The pipelines would extract features from weather and air quality data, train a model, and deploy an inference pipeline to make predictions on new data.
This document discusses cloud computing and Google App Engine. It provides an overview of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) and examples of each from Google. Google App Engine is described as a platform built on Google's infrastructure that allows hosting web applications and provides APIs, runtimes including Python, Java, and Go, and other features like caching and email sending. Limitations of App Engine are also listed such as not having native threads or sockets.
AngularJS with TypeScript and Windows Azure Mobile ServicesRainer Stropek
In the coming two weeks I will do a series of talks at various conferences in Austria and Germany. I will speak about AngularJS, TypeScript, and Windows Azure Mobile Services. In this blog post I publish the slides and the sample code.
Porting and Maintaining your C++ Game on Android without losing your mindBeMyApp
Presentation from David Wingrove & Katie Merrill from Golden Hammer Software http://www.goldenhammersoftware.com/
From the Barcelona Android User Group meetup: http://www.meetup.com/Barcelona-Android-User-Group/events/166734982/
Google App Engine is a PaaS that allows developers to build and host web applications in the Google cloud. The document summarizes a workshop on using the Java runtime environment on GAE. It discusses the SDKs, deploying and managing apps on GAE, data storage using the datastore, and limitations like the 30-second request limit. The biggest benefits of GAE are scalability and low startup costs, while the hardest limit is the 30-second request processing time.
Having Fun Building Web Applications (Day 2 slides)Clarence Ngoh
This document summarizes Day 2 of a Meteor development workshop. It introduces Meteor, a platform that allows building full-stack web apps using JavaScript on both the client and server. Key topics covered include installing Meteor, creating a new project, adding packages, using templates with reactive data binding, setting up user accounts, interacting with the MongoDB database using Collections, and deploying a Meteor app. Exercises guide applying these concepts by building out an app structure, adding features like user profiles and likes/views counting, and deploying the final project.
This document provides an overview of Google Cloud Platform (GCP) services. It discusses computing services like App Engine and Compute Engine for hosting applications. It covers storage options like Cloud Storage, Cloud Datastore and Cloud SQL. It also mentions big data services like BigQuery and machine learning services like Prediction API. The document provides brief descriptions of each service and highlights their key features. It includes code samples for using Prediction API to train a model and make predictions on new data.
What's new in android jakarta gdg (2015-08-26)Google
This document summarizes the key updates and features in Android M (Marshmallow), Google Play Services 7.8, and Android tools and libraries. Some highlights include runtime permissions in Android M, auto app backup, power saving optimizations, new APIs in Google Play Services like Nearby Messages and Face API, and support libraries for navigation, snackbars, tabs and more. The document provides code samples and best practices for using the new features.
Developing Java Web Applications In Google App EngineTahir Akram
The document provides an overview of developing Java-based web applications using Google App Engine. It discusses the key features and services of GAE including the runtime environment, datastore, memcache, mail, task queues, images, cron jobs, and user authentication. It also covers limitations, demo examples, and resources for learning more.
JS Fest 2019/Autumn. Александр Товмач. JAMstackJSFestUA
Вы уже слышали о JAMstack, который пришел на смену SSR и SPA? Подход, который оптимизирует веб приложения так, что они ограничены только скоростью вашего интернет соединения. Никаких просадок при рендере на клиенте, никаких падений серверов от нагрузки, только SEO-friendly приложения без проблем с масштабируемостью.
You may know Google for search, YouTube, Android, Chrome, and Gmail, but that's only as an end-user of OUR apps. Did you know you can also integrate Google technologies into YOUR apps? We have many APIs and open source libraries that help you do that! If you have tried and found it challenging, didn't find not enough examples, run into roadblocks, got confused, or just curious about what Google APIs can offer, join us to resolve any blockers. Code samples will be in Python and/or Node.js/JavaScript. This session focuses on showing you how to access Google Cloud APIs from one of Google Cloud's compute platforms, whether serverless or otherwise.
Similar to Designing the Call of Cthulhu app with Google App Engine (20)
論文紹介:A Systematic Survey of Prompt Engineering on Vision-Language Foundation ...Toru Tamaki
Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023
https://arxiv.org/abs/2307.12980
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
How Social Media Hackers Help You to See Your Wife's Message.pdfHackersList
In the modern digital era, social media platforms have become integral to our daily lives. These platforms, including Facebook, Instagram, WhatsApp, and Snapchat, offer countless ways to connect, share, and communicate.
Support en anglais diffusé lors de l'événement 100% IA organisé dans les locaux parisiens d'Iguane Solutions, le mardi 2 juillet 2024 :
- Présentation de notre plateforme IA plug and play : ses fonctionnalités avancées, telles que son interface utilisateur intuitive, son copilot puissant et des outils de monitoring performants.
- REX client : Cyril Janssens, CTO d’ easybourse, partage son expérience d’utilisation de notre plateforme IA plug & play.
UiPath Community Day Kraków: Devs4Devs ConferenceUiPathCommunity
We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner!
We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
09:40 From bottlenecks to breakthroughs: Citizen Development in action (25')
Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
10:05 Next-level bots: API integration in UiPath Studio (30')
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
12:20 🍕 Lunch Break (1hr)
13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30')
Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
13:50 Communications Mining - focus on AI capabilities (30')
Thomasz Wierzbicki, Business Analyst @Office Samurai
14:20 Polish MVP panel: Insights on MVP award achievements and career profiling
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
Mitigating the Impact of State Management in Cloud Stream Processing SystemsScyllaDB
Stream processing is a crucial component of modern data infrastructure, but constructing an efficient and scalable stream processing system can be challenging. Decoupling compute and storage architecture has emerged as an effective solution to these challenges, but it can introduce high latency issues, especially when dealing with complex continuous queries that necessitate managing extra-large internal states.
In this talk, we focus on addressing the high latency issues associated with S3 storage in stream processing systems that employ a decoupled compute and storage architecture. We delve into the root causes of latency in this context and explore various techniques to minimize the impact of S3 latency on stream processing performance. Our proposed approach is to implement a tiered storage mechanism that leverages a blend of high-performance and low-cost storage tiers to reduce data movement between the compute and storage layers while maintaining efficient processing.
Throughout the talk, we will present experimental results that demonstrate the effectiveness of our approach in mitigating the impact of S3 latency on stream processing. By the end of the talk, attendees will have gained insights into how to optimize their stream processing systems for reduced latency and improved cost-efficiency.
Measuring the Impact of Network Latency at TwitterScyllaDB
Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-InTrustArc
Six months into 2024, and it is clear the privacy ecosystem takes no days off!! Regulators continue to implement and enforce new regulations, businesses strive to meet requirements, and technology advances like AI have privacy professionals scratching their heads about managing risk.
What can we learn about the first six months of data privacy trends and events in 2024? How should this inform your privacy program management for the rest of the year?
Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year.
This webinar will review:
- Key changes to privacy regulations in 2024
- Key themes in privacy and data governance in 2024
- How to maximize your privacy program in the second half of 2024
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-In
Designing the Call of Cthulhu app with Google App Engine
1. Designing the Call of Cthulhu app with Google App Engine Presented by Chris Bunch at UCSB CS 189A / 172 : Capstone Project / Software Engineering February 10, 2010 http://cs.ucsb.edu/~cgb
2. Motivation I am what you would call a “nerd” Plays nerdy board games Would like to upload stats from games and analyze them Could do other cool things as well
4. Arkham Horror Lots of variables that can be analyzed Co-op game: all v. the board Mix and match expansions Many different winning / losing conditions But fundamentally boils down to: Save the world from total destruction
5. App Requirements Must be able to upload game data Should be able to predict a win or loss given some preliminary info Can experiment with prediction algorithms Should be able to summarize game data
6. App Requirements Allows users to log in to upload their data Allows users to share data with each other Handles users in a sane fashion Harder than it sounds Display a top score list for users to view Game provides scoring metric
7. Enter the Cloud Why go the cloud route? Simple answer: You likely don’t have your own hardware to host this app on, so just use somebody else’s But cloud computing is confusing and not well defined yet, so...
8. Defining Cloud Computing Three layers, each building on the last Infrastructure: Programmer gets a virtual machine and / or reliable storage Most control of environment Must scale manually Amazon EC2, Eucalyptus
9. Defining Cloud Computing Three layers, each building on the last Platform: Programmer gets a scalable API Less control of environment Scaling done automatically Google App Engine, Microsoft Azure
10. Defining Cloud Computing Three layers, each building on the last Software: Programmer gets to “skin” an existing app No control of environment Scaling done automatically SalesForce, GMail
11. My Choice Clearly I want the platform! Platform: Scalable API = great! Less control of environment = ok Programability depends on APIs offered Have personal exp. w/ Google App Engine
12. What is Google App Engine? Scalable web framework first offered in 2008 Users program apps in: Python Java Unofficially: JRuby, Groovy, JavaScript
13. Sandboxed Environment Requests limited to 30 seconds Only white-listed libraries can be used Anything non-trivial is quota-ed No reading / writing to the filesystem Web communication over HTTP(S) only
14. Datastore API A giant hash table Primitives: Get, put, delete - same as regular HT Query, count - new operations Transactions supported (row-level only)
15. Datastore API GQL queries offered - subset of SQL Select statement, no JOINs Tip: Queries are the most expensive operation, use sparingly If possible, perform it asynchronously
16. Memcache API Equivalent to memcached Provides access to low latency LRU cache Roughly 100x faster than Datastore But unreliable - remember it’s a cache! Exposes get, set, delete operations Exposes atomic increment / decrement
17. URL Fetch API Equivalent to curl Used to grab web content from other sites Only allows for HTTP(S) data to be read Can also be done asynchronously Typically grabs images or mashup data
18. Mail API Equivalent to sendmail Can send or receive mail asynchronously Can also add attachments Not all types allowed - no .doc, .xls, or .ppt in Python SDK
19. XMPP API Equivalent to jabber Send and receive instant messages to appname.appspotchat.com App can’t join group chat yet Use as a notifier or alternate UI to app
20. Images API Equivalent to python-imaging Exposes image rotation, flipping, cropping, and histogram Image is stored internally as a blob, then retrieved and manipulated
21. Users API Equivalent to Google Accounts Provides for simple authentication Allows app to force login or admin on certain pages Also exposes URL to optionally login to
22. Blobstore API Allows for large file uploads (<50 MB) Users can upload videos, datasets, or large music files for later retrieval Upload must be done via form post
23. Cron API Equivalent to cron User specifies how often a web page should be accessed (e.g., daily, every Monday) Task is then run in the background Useful for running expensive data analysis
24. Task Queue API Spawns a new thread in the background But still must visit your app Configuration file specifies queues Useful for short to medium length tasks
25. To summarize: Many, many APIs available for what we need to do! Use Datastore to store / retrieve user data Use Users to authenticate users Gets around a lot of boilerplate authentication code
26. To summarize: Use Mail to e-mail users if their score is beat Use XMPP to allow users to add new data Use Cron to run compute-intensive code in the background
27. To summarize: Think about paid functionality: Maybe use Blobstore to allow users to upload videos explaining gameplay Handle greater traffic if site becomes popular
28. Let’s Get to Business Data Modeling Upload Stats Page Game Stats Page Game Prediction Page
29. Data Modeling class Game(db.Model): player = db.UserProperty() goo = db.StringProperty() comments = dbStringProperty(multiline=True) date = db.DateTimeProperty(auto_now_add=True)
31. Uploading Stats class AddGames(webapp.RequestHandler): def get(self): user = users.get_current_user() if user: # means they’re logged in # write html form for getting data else: self.redirect(users.create_login_url(self.request.uri))
32. Uploading Stats def post(self): new_goo = self.request.get(‘goo’) # validate goo, make sure data type is right game = Game() game.goo = new_goo game.put()
33. Game Stats games = db.GqlQuery (“SELECT * FROM Game ORDER BY date desc”) for game in games: # find the goo w/ highest player win percentage # can also just display the info here
34. Game Prediction def get(self): # user specifies the game they will play def post(self): # calculate how likely they are to win # right now we only care about the goo
36. Thankfully Most requirements have very little state shared between URLs (all saved in DB) Thus each class is one of two forms: get / post: Grab data from user and do something with it get: Just display or synthesize some data
37. Rendering Web Pages Two main options available: self.response.out.write(“Hello World!”) self.response.out.write(template.render(‘index.html’, {‘my_title: ‘baz’, ‘data’: some_variable})) Use the second for any non-trivial app
38. Rendering Web Pages Now in your index.html: <title> {{ my_title }} </title> <div> {{ data }} </div>
39. Tips for Your Programs Separate out your code into files as needed app.yaml specifies this: handlers: - url: /one script: one.py - url: /two/(.*)/(.*) script: /two//.py
40. Tips for Your Programs Make pages with sensitive data ‘secure’ All access done over HTTPS Optional - HTTP and HTTPS work Never - HTTPS redirects to HTTP Always - HTTP redirects to HTTPS
41. Tips for Your Programs Specify static files in app.yaml - url: /images static_dir: static/images Can also tag ‘secure’ if desired
42. Tips for Your Programs Be creative! The webapp framework supports: get, post, head, options, put, delete, trace Map user pages to ~name?
43. Summary Google App Engine is more than flexible enough to handle your web app desires And it’s only getting better! More than enough possibilities to create a web app: you just need a good idea!