Machine Learning Wars: Deep Learning vs Gradient Boosting MachinesSefik Ilkin Serengil
This document compares and contrasts gradient boosting machines (GBM) and deep learning. It notes that while deep learning can perform complex tasks like facial recognition, its decisions are not understandable by humans. GBMs like gradient boosting decision trees address this issue by providing interpretable models with decision rules that can be traced back. The document also discusses popular GBM frameworks like XGBoost and LightGBM and notes that while GBMs are widely used in data science and won many Kaggle competitions, some experts still prefer neural networks.
Incremental Delivery: Benefits of Vertical SplittingLukas Klose
WHY is splitting stories properly so important? - This presentation uses an entertaining analogy to illustrate how incremental delivery can be used to help tackle complexity and manage/mitigate various types of risk like technical risk, business risk, delivery risk, etc.
BIG2016- Lessons Learned from building real-life user-focused Big Data systemsXavier Amatriain
1) More data is not always better than better models. Sometimes, better modeling techniques are needed rather than just collecting more data.
2) Ensembles of different models generally perform better than any single model and are commonly used in practice. Feature engineering to create new inputs for ensembles can improve their effectiveness.
3) Implicit signals from user behavior usually provide more useful information than explicit feedback, but both should be used to best represent users' long-term goals.
Estimations, Expectations, and Evolution During a Project's Journey from RFP ...Rick Manelius
This talk was presented at Drupalcon Nashville on April 12th, 2018. See talk overview and recording at this URL. https://events.drupal.org/nashville2018/sessions/estimates-expectations-and-evolution-during-projects-journey-rfp-release
This document provides an outline and notes for a workshop on running lean startups with agile practices. The workshop covers why lean startups need agile methods, includes an agile lego workshop simulation, and discusses practical agile management practices and technical practices for startups such as daily standups, user stories, hypotheses, planning poker, iterations, and feedback tools. The workshop aims to help startups build products more quickly through short development cycles and customer feedback.
This document discusses the current limitations of machine learning and managing expectations. It covers three key areas:
1) Current state-of-the-art limitations such as an inability to build generalized models for both images and text or conversational agents that pass the Turing test.
2) Expectation mismatch between what products teams expect ML to be able to do and its actual capabilities, like generating catchy titles.
3) Technical difficulties in maintaining ML systems over time like concept drift, training-serving skew, and unexpected data distributions causing false positives that require additional data and retraining. Check data science competitions to understand current ML capabilities and manage expectations.
Machine Learning Wars: Deep Learning vs Gradient Boosting MachinesSefik Ilkin Serengil
This document compares and contrasts gradient boosting machines (GBM) and deep learning. It notes that while deep learning can perform complex tasks like facial recognition, its decisions are not understandable by humans. GBMs like gradient boosting decision trees address this issue by providing interpretable models with decision rules that can be traced back. The document also discusses popular GBM frameworks like XGBoost and LightGBM and notes that while GBMs are widely used in data science and won many Kaggle competitions, some experts still prefer neural networks.
Incremental Delivery: Benefits of Vertical SplittingLukas Klose
WHY is splitting stories properly so important? - This presentation uses an entertaining analogy to illustrate how incremental delivery can be used to help tackle complexity and manage/mitigate various types of risk like technical risk, business risk, delivery risk, etc.
BIG2016- Lessons Learned from building real-life user-focused Big Data systemsXavier Amatriain
1) More data is not always better than better models. Sometimes, better modeling techniques are needed rather than just collecting more data.
2) Ensembles of different models generally perform better than any single model and are commonly used in practice. Feature engineering to create new inputs for ensembles can improve their effectiveness.
3) Implicit signals from user behavior usually provide more useful information than explicit feedback, but both should be used to best represent users' long-term goals.
Estimations, Expectations, and Evolution During a Project's Journey from RFP ...Rick Manelius
This talk was presented at Drupalcon Nashville on April 12th, 2018. See talk overview and recording at this URL. https://events.drupal.org/nashville2018/sessions/estimates-expectations-and-evolution-during-projects-journey-rfp-release
This document provides an outline and notes for a workshop on running lean startups with agile practices. The workshop covers why lean startups need agile methods, includes an agile lego workshop simulation, and discusses practical agile management practices and technical practices for startups such as daily standups, user stories, hypotheses, planning poker, iterations, and feedback tools. The workshop aims to help startups build products more quickly through short development cycles and customer feedback.
This document discusses the current limitations of machine learning and managing expectations. It covers three key areas:
1) Current state-of-the-art limitations such as an inability to build generalized models for both images and text or conversational agents that pass the Turing test.
2) Expectation mismatch between what products teams expect ML to be able to do and its actual capabilities, like generating catchy titles.
3) Technical difficulties in maintaining ML systems over time like concept drift, training-serving skew, and unexpected data distributions causing false positives that require additional data and retraining. Check data science competitions to understand current ML capabilities and manage expectations.
This document provides an overview of trunk-based development. It begins with defining continuous integration, delivery, deployment, and release. It then discusses branching strategies like GitFlow and the problems with pull requests. Trunk-based development is presented as pushing code directly to the main branch, along with practices like pairing, feature flags, and continuous delivery pipelines. The document stresses trunk-based development is about more than just the branch - it requires risk management, delivering value, and technical excellence. It provides advice on when not to use it and how to start implementing it.
The Fountain Project Model is proposed as a hybrid between Waterfall and Agile methods that aims to bridge their benefits and limitations. It involves an initial deployment on day two to provide early value to customers. Further development is done in iterations where customers specify the next most important features. This allows customers to receive working software early while developers receive feedback to guide development. The model is disguised when presented to managers as staged checkpoints with milestones to appear similar to Waterfall and ensure continued funding, while leveraging Agile practices internally. The goal is to enable project success by satisfying all stakeholders.
Book: Software Architecture and Decision-MakingSrinath Perera
Uncertainty is the leading cause of mistakes made by practicing software architects. The primary goal of architecture is to handle uncertainty arising from user cases as well as architectural techniques. The book discusses how to make architectural decisions and manage uncertainty. From the book, You will learn common problems while designing a system, a default solution for each, more complex alternatives, and 5Q & 7P (Five Questions and Seven Principles) that help you choose.
Book, https://amzn.to/3v1MfZX
Blog: http://tinyurl.com/swdmblog
Six min video - https://youtu.be/jtnuHvPWlYU
Practical DevSecOps: Fundamentals of Successful ProgramsMatt Tesauro
From ONUG Fall 2022:
"Shift Left'' and automation have turned from ideals to meaningless buzzwords. Instead of riding the hype train, let's get real and cover practical and real-world examples taken from actual product security successes. Not every business is the same, neither will their DevSecOps program.
In this talk, I'll cover the fundamentals of common to successful DevSecOps programs as well as a grab bag of useful techniques to consider. These are lessons learned doing AppSec at a wide variety of companies including Rackspace, Pearson, a fortune 500 financial, Duo Security and Cognizant Healthcare. Bruce Lee said "Research your own experience. Absorb what is useful, reject what is useless, add what is essentially your own". The goal of this talk is to provide you with enough examples to build your own pragmatic and practical DevSecOps program or maybe absorb a new technique or two into your existing program.
This document provides tips for winning data science competitions by summarizing a presentation about strategies and techniques. It discusses the structure of competitions, sources of competitive advantage like feature engineering and the right tools, and validation approaches. It also summarizes three case studies where the speaker applied these lessons, including encoding categorical variables and building diverse blended models. The key lessons are to focus on proper validation, leverage domain knowledge through features, and apply what is learned to real-world problems.
Taking the plunge: Why you should use new technology on client projectsTommy Ferry
The official release of Gutenberg has fundamentally altered how users can interact with the content editor. For developers, it’s a huge risk to dive into this new and unfamiliar territory on a client project. Many agencies and freelancers are therefore reluctant to develop custom blocks for their clients. However, finding the “perfect” time to implement a new technology is impossible.
This talk will explore a custom block project that was launched before Gutenberg came to core, demonstrating why you shouldn’t be afraid to jump in, the tangible benefits for you and your clients, and some challenges you might face along the way.
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
10 more lessons learned from building Machine Learning systemsXavier Amatriain
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. The outputs of machine learning models will often become inputs to other models, so models need to be designed with this in mind to avoid issues like feedback loops.
10 more lessons learned from building Machine Learning systems - MLConfXavier Amatriain
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. It is important to focus on feature engineering to create features that are reusable, transformable, interpretable, and reliable. The outputs of models may become inputs to other models, so care must be taken to avoid feedback loops and ensure proper data dependencies.
Influx/Days 2017 San Francisco | Dan VanderkamInfluxData
THE DYGRAPHS CHARTING LIBRARY
dygraphs is an open source JavaScript charting library which has been in development since 2006. Its combination of performance and interactivity make it an appealing visualization for dashboards. This talk will walk through how to add dygraphs to your project and how it can be used to facilitate interactive data exploration. Along the way, we’ll touch on some of the trials and tribulations of maintaining open source projects over long periods of time.
Running a small, high tech consulting firm - lessons learnedPere Ferrera Bertran
In this talk I describe my experience as CTO of Big Data consulting firm Datasalt from 2011 to 2016, the main use cases done for companies and the lessons learned from such a experience.
Ux best practices for non designers by Chimdindu Aneke Chimdindu Aneke
The document provides best practices for non-designers to improve user experience (UX). It discusses focusing on the user, validating designs upfront to help users, using different payment options, including text labels with icons for navigation, handling errors gracefully, starting with an end goal in mind when researching users, optimizing performance through progressive enhancement and lazy loading, and being responsive to different devices.
Dealing with Contributor Overload - Linux Conf AU Jan 2018Holden Karau
The document summarizes ways to deal with contributor overload in open source projects. As projects grow larger, it becomes more difficult to manage an increasing number of code contributions and questions. The document suggests establishing community structures, using tools to automate tasks, adding more committers to help with code reviews, setting clear expectations for contributors, and accepting that it's not possible to address everything perfectly. It emphasizes that feeling overwhelmed is normal and it's okay to not fix everything.
Scrum is an agile framework for managing product development. It involves fixed length sprints where a self-organizing team works to complete user stories from a prioritized backlog. Ceremonies like daily stand-ups, planning poker, and retrospectives help track progress and improve the process iteratively. Metrics like velocity and burn charts provide transparency. Tools like Jira help automate workflows. The goal is rapid, flexible delivery of valuable working software.
Scrum is an agile framework for managing product development. Key roles include the Product Owner, who represents stakeholders and priorities work. The Development Team works in iterations (sprints) to implement user stories, while the Scrum Master helps remove impediments. Ceremonies like planning poker, daily standups, and retrospectives provide transparency and opportunities to inspect and adapt the process sprint-over-sprint. Metrics like velocity and burndown charts are used for planning and monitoring progress. Tools like Jira help automate workflows and provide visibility.
How to Sunset Your Product by TaskTop Technologies Product ManagerProduct School
When people talk about Product Management, the vast majority of the time, it's all focused on how to bring new products to market. It's about how to prioritize new features. It's about the flashy and new. Trevor shared that if that's all you know about Product Management, you're only getting half the story. Yes, building new products is important and exciting, there's no denying that. However, it is just as important knowing how to sunset a product and how to clear out the old-growth to make room for the new. Trevor discussed how killing a product can result in more profits, but may actually result in more top line revenue.
Devops, the future is here it's not evenly distributed yetKris Buytaert
Devops, the future is here, but it is not evenly distributed. The document discusses the history and principles of devops. It summarizes that traditionally, development and operations teams had different goals and worked in silos, but devops aims to break down these barriers by advocating for automated testing, continuous integration, infrastructure as code, and cross-functional teams to improve collaboration and speed of delivery. Achieving devops culture and practices can help organizations release changes more quickly, reliably and with higher quality.
Lucy Bushby, digital partner and Christian Shannon, senior
designer, Reason Digital
Visit the CharityComms website to view slides from past events, see what events we have coming up and to check out what else we do: www.charitycomms.org.uk
Scaling Recommendations at Quora (RecSys talk 9/16/2016)Nikhil Dandekar
Talk about scaling Quora's recommendations and ML systems given at the ACM RecSys conference at Boston during the Large Scale Recommendation Systems (LSRS) workshop.
Embracing Culture, Sharing, and Systems from Employee 1.
Reference Article: https://rickmanelius.com/article/employee-1-and-beyond-system-set-checklist
Presented at the Boulder DevOps Presentation Meetup on 11/2019
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
This document provides an overview of trunk-based development. It begins with defining continuous integration, delivery, deployment, and release. It then discusses branching strategies like GitFlow and the problems with pull requests. Trunk-based development is presented as pushing code directly to the main branch, along with practices like pairing, feature flags, and continuous delivery pipelines. The document stresses trunk-based development is about more than just the branch - it requires risk management, delivering value, and technical excellence. It provides advice on when not to use it and how to start implementing it.
The Fountain Project Model is proposed as a hybrid between Waterfall and Agile methods that aims to bridge their benefits and limitations. It involves an initial deployment on day two to provide early value to customers. Further development is done in iterations where customers specify the next most important features. This allows customers to receive working software early while developers receive feedback to guide development. The model is disguised when presented to managers as staged checkpoints with milestones to appear similar to Waterfall and ensure continued funding, while leveraging Agile practices internally. The goal is to enable project success by satisfying all stakeholders.
Book: Software Architecture and Decision-MakingSrinath Perera
Uncertainty is the leading cause of mistakes made by practicing software architects. The primary goal of architecture is to handle uncertainty arising from user cases as well as architectural techniques. The book discusses how to make architectural decisions and manage uncertainty. From the book, You will learn common problems while designing a system, a default solution for each, more complex alternatives, and 5Q & 7P (Five Questions and Seven Principles) that help you choose.
Book, https://amzn.to/3v1MfZX
Blog: http://tinyurl.com/swdmblog
Six min video - https://youtu.be/jtnuHvPWlYU
Practical DevSecOps: Fundamentals of Successful ProgramsMatt Tesauro
From ONUG Fall 2022:
"Shift Left'' and automation have turned from ideals to meaningless buzzwords. Instead of riding the hype train, let's get real and cover practical and real-world examples taken from actual product security successes. Not every business is the same, neither will their DevSecOps program.
In this talk, I'll cover the fundamentals of common to successful DevSecOps programs as well as a grab bag of useful techniques to consider. These are lessons learned doing AppSec at a wide variety of companies including Rackspace, Pearson, a fortune 500 financial, Duo Security and Cognizant Healthcare. Bruce Lee said "Research your own experience. Absorb what is useful, reject what is useless, add what is essentially your own". The goal of this talk is to provide you with enough examples to build your own pragmatic and practical DevSecOps program or maybe absorb a new technique or two into your existing program.
This document provides tips for winning data science competitions by summarizing a presentation about strategies and techniques. It discusses the structure of competitions, sources of competitive advantage like feature engineering and the right tools, and validation approaches. It also summarizes three case studies where the speaker applied these lessons, including encoding categorical variables and building diverse blended models. The key lessons are to focus on proper validation, leverage domain knowledge through features, and apply what is learned to real-world problems.
Taking the plunge: Why you should use new technology on client projectsTommy Ferry
The official release of Gutenberg has fundamentally altered how users can interact with the content editor. For developers, it’s a huge risk to dive into this new and unfamiliar territory on a client project. Many agencies and freelancers are therefore reluctant to develop custom blocks for their clients. However, finding the “perfect” time to implement a new technology is impossible.
This talk will explore a custom block project that was launched before Gutenberg came to core, demonstrating why you shouldn’t be afraid to jump in, the tangible benefits for you and your clients, and some challenges you might face along the way.
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
10 more lessons learned from building Machine Learning systemsXavier Amatriain
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. The outputs of machine learning models will often become inputs to other models, so models need to be designed with this in mind to avoid issues like feedback loops.
10 more lessons learned from building Machine Learning systems - MLConfXavier Amatriain
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. It is important to focus on feature engineering to create features that are reusable, transformable, interpretable, and reliable. The outputs of models may become inputs to other models, so care must be taken to avoid feedback loops and ensure proper data dependencies.
Influx/Days 2017 San Francisco | Dan VanderkamInfluxData
THE DYGRAPHS CHARTING LIBRARY
dygraphs is an open source JavaScript charting library which has been in development since 2006. Its combination of performance and interactivity make it an appealing visualization for dashboards. This talk will walk through how to add dygraphs to your project and how it can be used to facilitate interactive data exploration. Along the way, we’ll touch on some of the trials and tribulations of maintaining open source projects over long periods of time.
Running a small, high tech consulting firm - lessons learnedPere Ferrera Bertran
In this talk I describe my experience as CTO of Big Data consulting firm Datasalt from 2011 to 2016, the main use cases done for companies and the lessons learned from such a experience.
Ux best practices for non designers by Chimdindu Aneke Chimdindu Aneke
The document provides best practices for non-designers to improve user experience (UX). It discusses focusing on the user, validating designs upfront to help users, using different payment options, including text labels with icons for navigation, handling errors gracefully, starting with an end goal in mind when researching users, optimizing performance through progressive enhancement and lazy loading, and being responsive to different devices.
Dealing with Contributor Overload - Linux Conf AU Jan 2018Holden Karau
The document summarizes ways to deal with contributor overload in open source projects. As projects grow larger, it becomes more difficult to manage an increasing number of code contributions and questions. The document suggests establishing community structures, using tools to automate tasks, adding more committers to help with code reviews, setting clear expectations for contributors, and accepting that it's not possible to address everything perfectly. It emphasizes that feeling overwhelmed is normal and it's okay to not fix everything.
Scrum is an agile framework for managing product development. It involves fixed length sprints where a self-organizing team works to complete user stories from a prioritized backlog. Ceremonies like daily stand-ups, planning poker, and retrospectives help track progress and improve the process iteratively. Metrics like velocity and burn charts provide transparency. Tools like Jira help automate workflows. The goal is rapid, flexible delivery of valuable working software.
Scrum is an agile framework for managing product development. Key roles include the Product Owner, who represents stakeholders and priorities work. The Development Team works in iterations (sprints) to implement user stories, while the Scrum Master helps remove impediments. Ceremonies like planning poker, daily standups, and retrospectives provide transparency and opportunities to inspect and adapt the process sprint-over-sprint. Metrics like velocity and burndown charts are used for planning and monitoring progress. Tools like Jira help automate workflows and provide visibility.
How to Sunset Your Product by TaskTop Technologies Product ManagerProduct School
When people talk about Product Management, the vast majority of the time, it's all focused on how to bring new products to market. It's about how to prioritize new features. It's about the flashy and new. Trevor shared that if that's all you know about Product Management, you're only getting half the story. Yes, building new products is important and exciting, there's no denying that. However, it is just as important knowing how to sunset a product and how to clear out the old-growth to make room for the new. Trevor discussed how killing a product can result in more profits, but may actually result in more top line revenue.
Devops, the future is here it's not evenly distributed yetKris Buytaert
Devops, the future is here, but it is not evenly distributed. The document discusses the history and principles of devops. It summarizes that traditionally, development and operations teams had different goals and worked in silos, but devops aims to break down these barriers by advocating for automated testing, continuous integration, infrastructure as code, and cross-functional teams to improve collaboration and speed of delivery. Achieving devops culture and practices can help organizations release changes more quickly, reliably and with higher quality.
Lucy Bushby, digital partner and Christian Shannon, senior
designer, Reason Digital
Visit the CharityComms website to view slides from past events, see what events we have coming up and to check out what else we do: www.charitycomms.org.uk
Scaling Recommendations at Quora (RecSys talk 9/16/2016)Nikhil Dandekar
Talk about scaling Quora's recommendations and ML systems given at the ACM RecSys conference at Boston during the Large Scale Recommendation Systems (LSRS) workshop.
Embracing Culture, Sharing, and Systems from Employee 1.
Reference Article: https://rickmanelius.com/article/employee-1-and-beyond-system-set-checklist
Presented at the Boulder DevOps Presentation Meetup on 11/2019
Similar to NEW version: https://www.slideshare.net/LukasKlose/incremental-delivery-benefits-of-vertical-splitting (20)
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
SMS API Integration in Saudi Arabia| Best SMS API ServiceYara Milbes
Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Revolutionizing Visual Effects Mastering AI Face Swaps.pdfUndress Baby
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
3. If you were to Build a Road from your Village “A” to
Village “B” Through a Forest...
...how would you break this task
into multiple (like 5) steps…
(Really, how would you do it...)
Village “B”
Village “A”
4. Not a good idea...
● Don’t know what is there until I
survey the landscape
● Might not have enough money to
complete the job
● Project might get cut
prematurely
● Might need to be able to
demonstrate/verify benefits of
getting to village B to get more
funding
8. User: explorer
● No infrastructure
● Barely enough precedence to
do it once more
● Validate assumptions
● Get to know people in village B
Step 1 - Blaze a Trail
9. ● Reduce Business Risk
○ Validate business assumptions
○ eg “Do I like the people in ‘village B’”
Benefits of Splitting
10. Step 2 - Harden the Path
User: foot passenger
● Can’t drive, but can
walk with ease
● Validate more
assumptions
11. Benefits of Splitting - Reduce Delivery Risk
○ walk before you run
○ predictability (eg 4 out of 5 done vs. 1 out
of 2)
○ interdependencies and – with it - risks
○ smaller changes -> less to go wrong
12. Step 3 - Construct a Road
User: off road vehicles
● Expand the usefulness of the path
13. Step 4 - Harden the Road
User: common vehicles
● No edge cases
14. Benefits of Splitting
● Reduce Technical Risk
○ defer commitment
○ validate tech assumptions (eg. can we build a highway on that route)
15. Step 5 - Build a Highway
User: everyone
● Make solution scalable
20. When a Story is Split Well
● It is valuable
○ meets DoD, incl QA
○ Can produce feedback
● It is shippable/complete
○ no need to do further work to ship
○ could be feature toggled
● Integrated
○ Requires x-Functional Collaboration
○ No surprises later
○ Encourages team work
21. However
● It could be a fragment of a bigger feature
○ Shippable ≠ shipped
○ Eg. Jigsaw puzzle
○ Not useful without other pieces of the puzzle
● PO might pay a penalty for splitting to gain
incremental benefit
○ eg 1 big story = $100k, 5 small stories = $110k
○ decision is up to the PO
22. Techniques
● Split with the team because it’s a technical conversation, and all can learn
● Use acceptance criteria
● Use happy path (subset of use cases)
● Use subset of users
● Use assumptions
● Use constraints
● Use ...
23. If you really can’t figure out how to go smaller
vertically
.... build the road to the
cottage on the way (like
half way)
Travel the second half
of the way another day
Arrive at a destination
(however small the
increment) with every
story
Cottage Village “B”
Village “A”
24. SAMPLE STORY
As a customer on PLP I want only products available to me to show so I don’t
waste time looking at out of stock products
AC
MUST BE ABLE TO SEE THE PRODUCT SOMEWHERE ELSE
THE PRODUCTS OUT OF STOCK MUST BE ONLINE
IF PRODUCT IS IN STOCK IN A CLOSE STORE, THAT IS CONSIDERED AVAILABLE