This is a presentation given by Google Developer Advocate Chris Schalk at Spring One 2GX on Oct 21st, 2010. It introduces Google Storage for Developers, Prediction API, and BigQuery.
Introduction to Google's Cloud TechnologiesChris Schalk
An overview of the different Cloud technologies available from Google including App Engine, Google Storage, Google Prediction API, and BigQuery.
This presentation was given to the San Diego GTUG on Aug 26th, 2011.
Building Apps on Google Cloud TechnologiesChris Schalk
This is a presentation on how to use the different Google Cloud technologies to build applications.
It was delivered in Mexico City at the "EstoEsGoogle" aka Devfest Mexico event on Aug 9th, 2011 in Mexico City by Google Developer Advocate Chris Schalk.
Boosting Documents in Solr by Recency, Popularity, and User PreferencesLucidworks (Archived)
Presentation on how to and access to source code for boosting and/or filtering documents by recency, popularity, and personal preferences. My solution improves upon the common "recip" based solution for boosting by document age.
Boosting Documents in Solr by Recency, Popularity and Personal Preferences - ...lucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/revolution/2011
Attendees with come away from this presentation with a good understanding and access to source
code for boosting and/or filtering documents by recency, popularity, and personal preferences. My
solution improves upon the common “recipe” based solution for boosting by document age. The
framework also supports boosting documents by a popularity score, which is calculated and
managed outside the index. I will present a few different ways to calculate popularity in a scalable
manner. Lastly, my solution supports the concept of a personal document collection, where each
user is only interested in a subset of the total number of documents in the index.
Implementing and Visualizing Clickstream data with MongoDBMongoDB
Having recently implemented a new framework for the real-time collection, aggregation and visualization of web and mobile generated Clickstream traffic (realizing daily click-stream volumes of 1M+ events), this walkthrough is about the motivations, throughout-process and key decisions made, as well as an in depth look at the implementation of how to buildout a data-collection, analytics and visualization framework using MongoDB. Technologies covered in this presentation (as well as MongoDB) are Java, Spring, Django and Pymongo.
Exploring .NET memory management - A trip down memory lane - Copenhagen .NET ...Maarten Balliauw
The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!
MongoDB .local London 2019: Fast Machine Learning Development with MongoDBLisa Roth, PMP
Today an increasingly large number of products use machine learning and AI to deliver a great personalized user experience, and workplace software is no exception. Spoke goes beyond traditional ticketing with their friendly, AI-powered chatbot that gives workplace teams hours of time back as it automatically responds to questions on Slack, email, SMS, and web. Learn how Spoke uses MongoDB to do dynamic model training in real time from user interaction data and serves thousands of models, with multiple customized models per client.
Introduction to Google's Cloud TechnologiesChris Schalk
An overview of the different Cloud technologies available from Google including App Engine, Google Storage, Google Prediction API, and BigQuery.
This presentation was given to the San Diego GTUG on Aug 26th, 2011.
Building Apps on Google Cloud TechnologiesChris Schalk
This is a presentation on how to use the different Google Cloud technologies to build applications.
It was delivered in Mexico City at the "EstoEsGoogle" aka Devfest Mexico event on Aug 9th, 2011 in Mexico City by Google Developer Advocate Chris Schalk.
Boosting Documents in Solr by Recency, Popularity, and User PreferencesLucidworks (Archived)
Presentation on how to and access to source code for boosting and/or filtering documents by recency, popularity, and personal preferences. My solution improves upon the common "recip" based solution for boosting by document age.
Boosting Documents in Solr by Recency, Popularity and Personal Preferences - ...lucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/revolution/2011
Attendees with come away from this presentation with a good understanding and access to source
code for boosting and/or filtering documents by recency, popularity, and personal preferences. My
solution improves upon the common “recipe” based solution for boosting by document age. The
framework also supports boosting documents by a popularity score, which is calculated and
managed outside the index. I will present a few different ways to calculate popularity in a scalable
manner. Lastly, my solution supports the concept of a personal document collection, where each
user is only interested in a subset of the total number of documents in the index.
Implementing and Visualizing Clickstream data with MongoDBMongoDB
Having recently implemented a new framework for the real-time collection, aggregation and visualization of web and mobile generated Clickstream traffic (realizing daily click-stream volumes of 1M+ events), this walkthrough is about the motivations, throughout-process and key decisions made, as well as an in depth look at the implementation of how to buildout a data-collection, analytics and visualization framework using MongoDB. Technologies covered in this presentation (as well as MongoDB) are Java, Spring, Django and Pymongo.
Exploring .NET memory management - A trip down memory lane - Copenhagen .NET ...Maarten Balliauw
The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!
MongoDB .local London 2019: Fast Machine Learning Development with MongoDBLisa Roth, PMP
Today an increasingly large number of products use machine learning and AI to deliver a great personalized user experience, and workplace software is no exception. Spoke goes beyond traditional ticketing with their friendly, AI-powered chatbot that gives workplace teams hours of time back as it automatically responds to questions on Slack, email, SMS, and web. Learn how Spoke uses MongoDB to do dynamic model training in real time from user interaction data and serves thousands of models, with multiple customized models per client.
Complex realtime event analytics using BigQuery @Crunch WarmupMárton Kodok
Complex event analytics solutions require massive architecture, and Know-How to build a fast real-time computing system. Google BigQuery solves this problem by enabling super-fast, SQL-like queries against append-only tables, using the processing power of Google’s infrastructure.In this presentation we will see how Bigquery solves our ultimate goal: Store everything accessible by SQL immediately at petabyte-scale. We will discuss some common use cases: funnels, user retention, affiliate metrics.
Big Data Analytics 2: Leveraging Customer Behavior to Enhance Relevancy in Pe...MongoDB
This session covers how to capture and analyize customer behavior to create more relevent contexts for customers. We will cover how to use your current BI features, and more importantly, how newer technologies approach the challenge. You will walk away with a good idea on how to build and drive even more contextually relevant experiences to customers for even more successful engagements.
Intro to machine learning for web folks @ BlendWebMixLouis Dorard
Get a business understanding of ML by going through key concepts and concrete use cases that illustrate its possibilities for web-based companies.
In this presentation I introduce new technology that makes ML more accessible, and I explain in simple terms the limitations to what can be achieved. Finally, I discuss pragmatic considerations of real-world applications and I give a sneak peak at the Machine Learning Canvas — a framework for describing a predictive system that uses ML to provide value to its end user.
--
L'utilisation du Machine Learning s'est fortement développée ces dernières années, jusqu'à être présent aujourd'hui dans environ la moitié des applications que nous utilisons sur smartphone. Même s'ils n'ont pas connaissance du Machine Learning (ML), les utilisateurs d'applications mobile et web sont devenus demandeurs de fonctionnalités prédictives que le ML rend possibles. Par ailleurs, dans le cadre de l'entreprise, le ML représente un avantage compétitif important qui permet de valoriser ses data en les couplant à une intelligence machine.
Auparavant réservée aux grosses entreprises, cette technologie se démocratise grâce aux nouveaux outils de ML-as-a-Service et aux APIs de prediction. Afin d'en tirer profit, nous verrons ensemble les clés de compréhension du fonctionnement du machine learning, qui sous-tendent ses possibilités et ses limites. Nous verrons également comment amorcer son utilisation dans votre propre projet, au travers du Machine Learning Canvas qui permet de décrire un système où le ML est au cœur de la création de valeur.
iBOA: The Incremental Bayesian Optimization AlgorithmMartin Pelikan
This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.
Using Machine Learning in Networks Intrusion Detection SystemsOmar Shaya
The internet and different computing devices from desktop computers to smartphones have raised many security and privacy concerns, and the need to automate systems that detect attacks on these networks has emerged in order to be able to protect these networks with scale. And while traditional intrusion detection methods may be able to detect previously known attacks, the issue of dealing with new unknown attacks arises and that brings machine learning as a strong candidate to solve these challenges.
In this report, we investigate the use of machine learning in detecting network attacks, intrusion detection, by looking at work that has been done in this field. Particularly we look at the work that has been done by Pasocal et al.
A review of machine learning based anomaly detectionMohamed Elfadly
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. These nonconforming patterns are often referred to as anomalies, outliers, discordant observations, exceptions, aberrations, surprises, peculiarities, or contaminants in different application domains.
The AML group carries out both theoretical and experimental work on developing and applying new machine learning techniques for solving various application problems.
[CB16] Method of detecting vulnerability in WebApps using Machine Learning by...CODE BLUE
In Japan, information security engineers are lacking. So, I am focused on artificial intelligence (AI) technology to solve the lack of human resources. And, I have developed the AI to detect vulnerabilities on web apps called SAIVS (Spider Artificial Intelligence Vulnerabilities Scanner). The goal of SAIVS is to obtain ability of equal or higher than vulnerability diagnosis members. Currently, SAIVS is prototype.
But, it is possible to detect vulnerabilities on web apps like a human.
1. It can crawl web apps.
SAIVS can crawl web apps that include dynamic pages such as "login," "create account".
For example, SAIVS recognizes the type of the page. If it crawls the login page without having a login credential, it creates login credential in the create account page. After it login with the created login credentials, it crawls the rest of the pages.
2. It can detect vulnerabilities.
SAIVS can detect vulnerabilities efficiently by observing the behavior of web apps.
For example, in the case of a reflected XSS, SAIVS recognizes the echoed back place of input value, and it can insert the exploitable tags and scripts that to match the HTML and JavaScript syntax. In addition, if the input value has been sanitized, SAIVS selects test pattern to avoid the sanitization, and it can inspect the XSS again.
I achieve these actions by simulate the thinking pattern of vulnerability diagnosis members using multiple machine learning algorithms.
My presentation will explain how this ability was made possible by the machine learning algorithms and show a demo (detecting reflected XSS).
--- Isao Takaesu
Web security engineer at Mitsui Bussan Secure Directions, Inc. CISSP.
I have worked on the detection of vulnerabilities on the web applications (web applications diagnosis) for seven years. In these days, I have been hoping to detect more vulnerabilities, but I feel the limitation of human resources. So, I am focused on the machine learning for web applications diagnosis, and have tried to develop the AI called SAIVS. In future, I really want SAIVS to take over my tasks of web applications diagnosis. Furthermore, SAIVS has been introduced at Black Hat Asia 2016 Arsenal at Singapore, and was well received.
Complex realtime event analytics using BigQuery @Crunch WarmupMárton Kodok
Complex event analytics solutions require massive architecture, and Know-How to build a fast real-time computing system. Google BigQuery solves this problem by enabling super-fast, SQL-like queries against append-only tables, using the processing power of Google’s infrastructure.In this presentation we will see how Bigquery solves our ultimate goal: Store everything accessible by SQL immediately at petabyte-scale. We will discuss some common use cases: funnels, user retention, affiliate metrics.
Big Data Analytics 2: Leveraging Customer Behavior to Enhance Relevancy in Pe...MongoDB
This session covers how to capture and analyize customer behavior to create more relevent contexts for customers. We will cover how to use your current BI features, and more importantly, how newer technologies approach the challenge. You will walk away with a good idea on how to build and drive even more contextually relevant experiences to customers for even more successful engagements.
Intro to machine learning for web folks @ BlendWebMixLouis Dorard
Get a business understanding of ML by going through key concepts and concrete use cases that illustrate its possibilities for web-based companies.
In this presentation I introduce new technology that makes ML more accessible, and I explain in simple terms the limitations to what can be achieved. Finally, I discuss pragmatic considerations of real-world applications and I give a sneak peak at the Machine Learning Canvas — a framework for describing a predictive system that uses ML to provide value to its end user.
--
L'utilisation du Machine Learning s'est fortement développée ces dernières années, jusqu'à être présent aujourd'hui dans environ la moitié des applications que nous utilisons sur smartphone. Même s'ils n'ont pas connaissance du Machine Learning (ML), les utilisateurs d'applications mobile et web sont devenus demandeurs de fonctionnalités prédictives que le ML rend possibles. Par ailleurs, dans le cadre de l'entreprise, le ML représente un avantage compétitif important qui permet de valoriser ses data en les couplant à une intelligence machine.
Auparavant réservée aux grosses entreprises, cette technologie se démocratise grâce aux nouveaux outils de ML-as-a-Service et aux APIs de prediction. Afin d'en tirer profit, nous verrons ensemble les clés de compréhension du fonctionnement du machine learning, qui sous-tendent ses possibilités et ses limites. Nous verrons également comment amorcer son utilisation dans votre propre projet, au travers du Machine Learning Canvas qui permet de décrire un système où le ML est au cœur de la création de valeur.
iBOA: The Incremental Bayesian Optimization AlgorithmMartin Pelikan
This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.
Using Machine Learning in Networks Intrusion Detection SystemsOmar Shaya
The internet and different computing devices from desktop computers to smartphones have raised many security and privacy concerns, and the need to automate systems that detect attacks on these networks has emerged in order to be able to protect these networks with scale. And while traditional intrusion detection methods may be able to detect previously known attacks, the issue of dealing with new unknown attacks arises and that brings machine learning as a strong candidate to solve these challenges.
In this report, we investigate the use of machine learning in detecting network attacks, intrusion detection, by looking at work that has been done in this field. Particularly we look at the work that has been done by Pasocal et al.
A review of machine learning based anomaly detectionMohamed Elfadly
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. These nonconforming patterns are often referred to as anomalies, outliers, discordant observations, exceptions, aberrations, surprises, peculiarities, or contaminants in different application domains.
The AML group carries out both theoretical and experimental work on developing and applying new machine learning techniques for solving various application problems.
[CB16] Method of detecting vulnerability in WebApps using Machine Learning by...CODE BLUE
In Japan, information security engineers are lacking. So, I am focused on artificial intelligence (AI) technology to solve the lack of human resources. And, I have developed the AI to detect vulnerabilities on web apps called SAIVS (Spider Artificial Intelligence Vulnerabilities Scanner). The goal of SAIVS is to obtain ability of equal or higher than vulnerability diagnosis members. Currently, SAIVS is prototype.
But, it is possible to detect vulnerabilities on web apps like a human.
1. It can crawl web apps.
SAIVS can crawl web apps that include dynamic pages such as "login," "create account".
For example, SAIVS recognizes the type of the page. If it crawls the login page without having a login credential, it creates login credential in the create account page. After it login with the created login credentials, it crawls the rest of the pages.
2. It can detect vulnerabilities.
SAIVS can detect vulnerabilities efficiently by observing the behavior of web apps.
For example, in the case of a reflected XSS, SAIVS recognizes the echoed back place of input value, and it can insert the exploitable tags and scripts that to match the HTML and JavaScript syntax. In addition, if the input value has been sanitized, SAIVS selects test pattern to avoid the sanitization, and it can inspect the XSS again.
I achieve these actions by simulate the thinking pattern of vulnerability diagnosis members using multiple machine learning algorithms.
My presentation will explain how this ability was made possible by the machine learning algorithms and show a demo (detecting reflected XSS).
--- Isao Takaesu
Web security engineer at Mitsui Bussan Secure Directions, Inc. CISSP.
I have worked on the detection of vulnerabilities on the web applications (web applications diagnosis) for seven years. In these days, I have been hoping to detect more vulnerabilities, but I feel the limitation of human resources. So, I am focused on the machine learning for web applications diagnosis, and have tried to develop the AI called SAIVS. In future, I really want SAIVS to take over my tasks of web applications diagnosis. Furthermore, SAIVS has been introduced at Black Hat Asia 2016 Arsenal at Singapore, and was well received.
[Tutorial] building machine learning models for predictive maintenance applic...PAPIs.io
This talk introduces the landscape and challenges of predictive maintenance applications in the industry, illustrates how to formulate (data labeling and feature engineering) the problem with three machine learning models (regression, binary classification, multi-class classification) using a publicly available aircraft engine run-to-failure data set, and showcases how the models can be conveniently trained and compared with different algorithms in Azure ML.
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQueryChris Schalk
This is an introductory presentation given at DevFest Madrid 2010 by Google Developer Advocate Chris Schalk. It introduces new Google cloud technologies: Google Storage, Google Prediction API and BigQuery.
An overview of the different Google Cloud Technologies. Includes coverage of Google App Engine, Google Storage, Google Prediction Api, and BigQuery.
This presentation was given to the San Diego GTUG on Aug 26th, 2011.
Quick Intro to Google Cloud TechnologiesChris Schalk
This is the "Lightning Presentation" given at DreamForce 2011 on Google's Cloud Technologies. It covers, App Engine, Google Storage and BigQuery. #df11
Building Integrated Applications on Google's Cloud TechnologiesChris Schalk
This is the presentation "Building Integrated Applications on Google's Cloud Technologies" that was given at GDD 2011 #gdd11 in Sao Paulo and Buenos Aires by Google Developer Advocate Chris Schalk @cschalk.
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.
GDD Brazil 2010 - Google Storage, Bigquery and Prediction APIsPatrick Chanezon
Google is expanding our storage products by introducing Google Storage for Developers. It offers a RESTful API for storing and accessing data at Google. Developers can take advantage of the performance and reliability of Google's storage infrastructure, as well as the advanced security and sharing capabilities. We will demonstrate key functionality of the product as well as customer use cases. Google relies heavily on data analysis and has developed many tools to understand large datasets. Two of these tools are now available on a limited sign-up basis to developers: (1) BigQuery: interactive analysis of very large data sets and (2) Prediction API: make informed predictions from your data. We will demonstrate their use and give instructions on how to get access.
Image archive, analysis & report generation with Google Cloudwesley chun
Google Cloud provides a diverse array of services to realize the ambition of solving real business problems, like constrained resources. An image archive & analysis plus report generation use-case can be realized with just Google Workspace & GCP APIs. The principle of mixing-and-matching Google technologies is applicable to many other challenges faced by you, your organization, or your customers. These slides are from a half- to 1-hour presentation about this case study.
Part 1: App Engine for Business によって、Google のアプリケーションを支えているのと同じスケーラブルなシステムを使ってエンタープライズアプリケーションを作成する事ができます。このセッションではエンタープライズの要求に答えるために用意されている API, 分かりやすい課金体系, SLA とサポートについて紹介します。 Part 2: Google がリリースしようとしている新しい Cloud サービス群の紹介をします。1） Google Storage for Developers は Google のインフラストラクチャ上にデータを保存,アクセスするための RESTful なサービスです。2） BigQuery は大規模なデータセットに対してインタラクティブな分析を行う Web サービスです。3） Prediction API はデータから機械学習により予測を行うための API です。
This is our contributions to the Data Science projects, as developed in our startup. These are part of partner trainings and in-house design and development and testing of the course material and concepts in Data Science and Engineering. It covers Data ingestion, data wrangling, feature engineering, data analysis, data storage, data extraction, querying data, formatting and visualizing data for various dashboards.Data is prepared for accurate ML model predictions and Generative AI apps
This is our project work at our startup for Data Science. This is part of our internal training and focused on data management for AI, ML and Generative AI apps
Powerful Google developer tools for immediate impact! (2023-24 A)wesley chun
This is one of two 45-60-min presentations to students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many other cloud services? In this comprehensive yet still high-level overview of Google Cloud tools & APIs with the purpose of inspiring you as to what's possible. The session introduces Google's machine learning & other APIs, tools that have an immediate impact on projects, alleviating the need to think about computing infrastructure as well as dispensing with the need to have machine learning expertise. We'll wrap up w/online resources like videos & hands-on tutorials to get you started! The main takeaways are where to run your code, store your data, and analyze your data, all in the cloud!
The other version of this talk ("B") focuses more on serverless platforms.
Easy path to machine learning (2023-2024)wesley chun
1-hr tech talk introducing Machine Learning and the GCP ML APIs and other Google Cloud developer tools to a technical audience:
Easier onramp to getting into AI/ML by using GCP AI/ML APIs (Vision, Video Intelligence, Natural Language, Speech-to-Text, Text-to-Speech, Translation) backed by single-task pre-trained models found in Vertex AI, AutoML for finetuning those pre-trained models, and other "friends of AI/ML" Google dev tools & platforms that can help: BigQuery (data warehouse & analysis), Cloud SQL+AlloyDB & Firestore (SQL & NoSQL databases), serverless platforms (App Engine, Cloud Functions, Cloud Run), and introducing the Gemini API (from both Google AI and GCP Vertex AI)
Google Cloud for Data Crunchers - Strata Conf 2011Patrick Chanezon
http://strataconf.com/strata2011/public/schedule/detail/16242
Talk at Strata 2011 with Ryan Boyd and Kirrily Roberts
Google is a Data business: over the past few years, many of the tools Google created to store, query, analyze, visualize its data, have been exposed to developers as services.
This talk will give you an overview of Google services for Data Crunchers:
Google Storage for developers
BigQuery, fast interactive queries on Terabytes of data
Machine Learning API: Machine Learning made easy
Google App Engine, exposing Data APIs is a very common use case for App Engine
Visualization API: many cool visualization components
Similar to Introduction to Google Cloud platform technologies (20)
An overview and update presentation on Google App Engine given by Google Developer Advocate Christian Schalk at the 2011 DevFest Singapore and Jakarta events. Developer Advocate Wesley Chun also participated in the Q&A.
How to build Kick Ass Games in the CloudChris Schalk
This is a presentation given by Googlers Chris Schalk and Johan Euphrosine (Proppy) at GDD Sydney 2011 on how to build multi-platform video games using PlayN.
Building Kick Ass Video Games for the CloudChris Schalk
This is a presentation that covers how to use PlayN to build kick ass games for the cloud. It was delivered at GDC Online 2011, by Google Developer Advocate, Chris Schalk
GDD 2011 - How to build kick ass video games for the cloudChris Schalk
This is the Google Developer Day 2011 "How to build kick ass games in the cloud" presentation that was given in Sao Paulo and Buenos Aires. (The title is in Spanish since it was last given in Argentina - the content is the same as Sao Paulo though)
It was given by Google Developer Advocate, Chris Schalk in Sept 2011. @cschalk
A review and update presentation on Google App Engine's latest features up through version 1.5.3 and including new experimental features. This presentation was given to the San Diego GTUG on Aug 26, 2011.
This is a presentation on Google App Engine's Latest Features.
It was delivered in Mexico City at the "EstoEsGoogle" aka Devfest Mexico event on Aug 9th, 2011 in Mexico City by Google Developer Advocate Chris Schalk.
Building Multi-platform Video Games for the CloudChris Schalk
This is a presentation on how to build multi-platform (HTML5, Flash, Java/Android) video games using an open source technology known as Forplay (PlayN).
This presentation was delivered in Mexico City on Aug 9th, 2011 at the "EstoEsGoogle" aka Devfest Mexico event on Aug 9th in Mexico City by Google Developer Advocate Chris Schalk
Building Enterprise Applications on Google Cloud Platform Cloud Computing Exp...Chris Schalk
This is a presentation given by Google Developer Advocate Chris Schalk at Cloud Expo in NYC on June 8th 2011 on building enterprise applications with Google's Cloud Platform.
This is an introduction presentation on App Engine for Business given by Chris Schalk, Google Developer Advocate on Oct 26, 2010 at the PayPal Innovate conference.
What's new in App Engine and intro to App Engine for BusinessChris Schalk
This is a presentation given by Devfest Madrid 2010 by Google Developer Advocate Chris Schalk on "What's new in Google App Engine and Intro to App Engine for Business"
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
How world-class product teams are winning in the AI era by CEO and Founder, P...
Introduction to Google Cloud platform technologies
1. Chicago, October 19 - 22, 2010
How to analyze your data and take
advantage of machine learning in
your application
Chris Schalk - Google
2. SpringOne 2GX 2009. All rights
reserved. Do not distribute without
Google’s new Cloud Technologies
Topics covered
• Google Storage for Developers
• Prediction API (machine learning)
• BigQuery
4. What Is Google Storage?
• Store your data in Google's cloud
o any format, any amount, any time
• You control access to your data
o private, shared, or public
• Access via Google APIs or 3rd party tools/libraries
5. Sample Use Cases
Static content hosting
e.g. static html, images, music, video
Backup and recovery
e.g. personal data, business records
Sharing
e.g. share data with your customers
Data storage for applications
e.g. used as storage backend for Android, AppEngine, Cloud based apps
Storage for Computation
e.g. BigQuery, Prediction API
6. Google Storage Benefits
High Performance and Scalability
Backed by Google infrastructure
Strong Security and Privacy
Control access to your data
Easy to Use
Get started fast with Google & 3rd party tools
7. Google Storage Technical Details
• RESTful API
o Verbs: GET, PUT, POST, HEAD, DELETE
o Resources: identified by URI
o Compatible with S3
• Buckets
o Flat containers
• Objects
o Any type
o Size: 100 GB / object
• Access Control for Google Accounts
o For individuals and groups
• Two Ways to Authenticate Requests
o Sign request using access keys
o Web browser login
8. Performance and Scalability
• Objects of any type and 100 GB / Object
• Unlimited numbers of objects, 1000s of buckets
• All data replicated to multiple US data centers
• Leveraging Google's worldwide network for data delivery
• Only you can use bucket names with your domain names
• Read-your-writes data consistency
• Range Get
9. Security and Privacy Features
• Key-based authentication
• Authenticated downloads from a web browser
• Sharing with individuals
• Group sharing via Google Groups
• Access control for buckets and objects
• Set Read/Write/List permissions
14. Google Storage - Pricing
o Storage
$0.17/GB/Month
o Network
Upload - $0.10/GB
Download
$0.15/GB Americas / EMEA
$0.30/GB APAC
o Requests
PUT, POST, LIST - $0.01 / 1000 Requests
GET, HEAD - $0.01 / 10000 Requests
15. Google Storage - Availability
• Limited preview in US currently
o 100GB free storage and network from Google per
account
o Sign up for waitlist at http://code.google.com/apis/
storage/
• Note: Non US preview available on case-by-case basis
16. Google Storage Summary
• Store any kind of data using Google's cloud infrastructure
• Easy to Use APIs
• Many available tools and libraries
o gsutil, GS Manager
o 3rd party:
Boto, CloudBerry, CyberDuck, JetS3t, and more
18. Introducing the Google Prediction API
• Google's sophisticated machine learning technology
• Available as an on-demand RESTful HTTP web service
19. How does it work?
"english" The quick brown fox jumped over the
lazy dog.
"english" To err is human, but to really foul things
up you need a computer.
"spanish" No hay mal que por bien no venga.
"spanish" La tercera es la vencida.
? To be or not to be, that is the question.
? La fe mueve montañas.
The Prediction API
finds relevant
features in the
sample data during
training.
The Prediction API
later searches for
those features
during prediction.
20. A virtually endless number of applications...
Customer
Sentiment
Transaction
Risk
Species
Identification
Message
Routing
Legal Docket
Classification
Suspicious
Activity
Work Roster
Assignment
Recommend
Products
Political
Bias
Uplift
Marketing
Email
Filtering
Diagnostics
Inappropriate
Content
Career
Counselling
Churn
Prediction
... and many more ...
21. A Prediction API Example
Automatically categorize and respond to emails by language
• Customer: ACME Corp, a multinational organization
• Goal: Respond to customer emails in their language
• Data: Many emails, tagged with their languages
• Outcome: Predict language and respond accordingly
22. Using the Prediction API
1. Upload
2. Train
Upload your training data to
Google Storage
Build a model from your data
Make new predictions3. Predict
A simple three step process...
23. Step 1: Upload
Upload your training data to Google Storage
• Training data: outputs and input features
• Data format: comma separated value format (CSV)
"english","To err is human, but to really ..."
"spanish","No hay mal que por bien no venga."
...
Upload to Google Storage
gsutil cp ${data} gs://yourbucket/${data}
24. Step 2: Train
Create a new model by training on data
To train a model:
POST prediction/v1.1/training?data=mybucket%2Fmydata
Training runs asynchronously. To see if it has finished:
GET prediction/v1.1/training/mybucket%2Fmydata
{"data":{
"data":"mybucket/mydata",
"modelinfo":"estimated accuracy: 0.xx"}}}
25. Step 3: Predict
Apply the trained model to make predictions on new data
POST prediction/v1.1/query/mybucket%2Fmydata/predict
{ "data":{
"input": { "text" : [
"J'aime X! C'est le meilleur" ]}}}
26. Step 3: Predict
Apply the trained model to make predictions on new data
POST prediction/v1.1/query/mybucket%2Fmydata/predict
{ "data":{
"input": { "text" : [
"J'aime X! C'est le meilleur" ]}}}
{ data : {
"kind" : "prediction#output",
"outputLabel":"French",
"outputMulti" :[
{"label":"French", "score": x.xx}
{"label":"English", "score": x.xx}
{"label":"Spanish", "score": x.xx}]}}
27. Step 3: Predict
Apply the trained model to make predictions on new data
import httplib
header = {"Content-Type" : "application/json"}
#...put new data in JSON format in params variable
conn = httplib.HTTPConnection("www.googleapis.com")conn.request("POST",
"/prediction/v1.1/query/mybucket%2Fmydata/predict”, params, header)
print conn.getresponse()
An example using Python
28. Prediction API Capabilities
Data
• Input Features: numeric or unstructured text
• Output: up to hundreds of discrete categories
Training
• Many machine learning techniques
• Automatically selected
• Performed asynchronously
Access from many platforms:
• Web app from Google App Engine
• Apps Script (e.g. from Google Spreadsheet)
• Desktop app
29. Prediction API v1.1 - new features
• Updated Syntax
• Multi-category prediction
o Tag entry with multiple labels
• Continuous Output
o Finer grained prediction rankings based on multiple labels
• Mixed Inputs
o Both numeric and text inputs are now supported
Can combine continuous output with mixed inputs
32. Introducing Google BigQuery
• Google's large data adhoc analysis technology
o Analyze massive amounts of data in seconds
• Simple SQL-like query language
• Flexible access
o REST APIs, JSON-RPC, Google Apps Script
34. Many Use Cases ...
Spam
Trends
Detection
Web Dashboards Network
Optimization
Interactive Tools
35. Key Capabilities of BigQuery
• Scalable: Billions of rows
• Fast: Response in seconds
• Simple: Queries in SQL
• Web Service
o REST
o JSON-RPC
o Google App Scripts
36. Using BigQuery
1. Upload
2. Import
Upload your raw data to
Google Storage
Import raw data into BigQuery table
Perform SQL queries on table3. Query
Another simple three step process...
37. Writing Queries
Compact subset of SQL
o SELECT ... FROM ...
WHERE ...
GROUP BY ... ORDER BY ...
LIMIT ...;
Common functions
o Math, String, Time, ...
Statistical approximations
o TOP
o COUNT DISTINCT
38. BigQuery via REST
GET /bigquery/v1/tables/{table name}
GET /bigquery/v1/query?q={query}
Sample JSON Reply:
{
"results": {
"fields": { [
{"id":"COUNT(*)","type":"uint64"}, ... ]
},
"rows": [
{"f":[{"v":"2949"}, ...]},
{"f":[{"v":"5387"}, ...]}, ... ]
}
}
Also supports JSON-RPC
39. Security and Privacy
Standard Google Authentication
• Client Login
• OAuth
• AuthSub
HTTPS support
• protects your credentials
• protects your data
Relies on Google Storage to manage access
40. Large Data Analysis Example
Wikimedia Revision history data from: http://download.wikimedia.org/enwiki/latest/
enwiki-latest-pages-meta-history.xml.7z
Wikimedia Revision History
44. Recap
• Google Storage
o High speed data storage on Google Cloud
• Prediction API
o Google's machine learning technology able to
predict outcomes based on sample data
• BigQuery
o Interactive analysis of very large data sets
o Simple SQL query language access
45. Further info available at:
• Google Storage for Developers
o http://code.google.com/apis/storage
• Prediction API
o http://code.google.com/apis/predict
• BigQuery
o http://code.google.com/apis/bigquery
46. SpringOne 2GX 2010. All rights reserved. Do not distribute without permission.
Demo
47. SpringOne 2GX 2010. All rights reserved. Do not distribute without permission.
Q&A
48. SpringOne 2GX 2010. All rights reserved. Do not distribute without permission.
Thank You!
Chris Schalk
Google Developer
Advocate
http://twitter.com/cschalk
