- The document discusses Netflix's approach to using data and algorithms to provide personalized recommendations to users. It summarizes Netflix's transition from simple ranking algorithms to personalized recommendations based on user behavior data.
- Netflix runs hundreds of A/B tests on algorithms and designs simultaneously to evaluate how changes impact user engagement and retention. Both online and offline testing is used to evaluate recommendations before and after deployment.
- A variety of algorithms are used for recommendations, including matrix factorization, restricted Boltzmann machines, and learning to rank approaches. Feature engineering and algorithm development are ongoing areas of research at Netflix to improve diversity, novelty, and accuracy of recommendations.
Big & Personal: the data and the models behind Netflix recommendations by Xa...BigMine
Since the Netflix $1 million Prize, announced in 2006, our company has been known for having personalization at the core of our product. Even at that point in time, the dataset that we released was considered “large”, and we stirred innovation in the (Big) Data Mining research field. Our current product offering is now focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search.
In this talk I will discuss the different approaches we follow to deal with these large streams of data in order to extract information for personalizing our service. I will describe some of the machine learning models used, as well as the architectures that allow us to combine complex offline batch processes with real-time data streams.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Big & Personal: the data and the models behind Netflix recommendations by Xa...BigMine
Since the Netflix $1 million Prize, announced in 2006, our company has been known for having personalization at the core of our product. Even at that point in time, the dataset that we released was considered “large”, and we stirred innovation in the (Big) Data Mining research field. Our current product offering is now focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search.
In this talk I will discuss the different approaches we follow to deal with these large streams of data in order to extract information for personalizing our service. I will describe some of the machine learning models used, as well as the architectures that allow us to combine complex offline batch processes with real-time data streams.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Product Recommendations Enhanced with Reviewsmaranlar
Tutorial presented by Muthusamy Chelliah (Flipkart, India) and Sudeshna Sarkar (IIT Kharagpur, India) at ACM RecSys 2017 https://recsys.acm.org/recsys17/tutorials/#content-tab-1-3-tab
E-commerce websites commonly deploy recommender systems that make use of user activity (e.g., ratings, views, and purchases) or content (product descriptions). These recommender systems can benefit enormously by also exploiting the information contained in customer reviews. Reviews capture the experience of multiple customers with diverse preferences, often on the fine-grained level of specific features of products. Reviews can also identify consumers’ preferences for product features and provide helpful explanations. The usefulness of reviews is evidenced by the prevalence of their use by customers to support shopping decisions online. With the appropriate techniques, recommender systems can benefit directly from user reviews.
This tutorial will present a range of techniques that allow recommender systems in e-commerce websites to take full advantage of reviews. Topics covered include text mining methods for feature-specific sentiment analysis of products, topic models and distributed representations that bridge the vocabulary gap between user reviews and product descriptions, and recommender algorithms that use review information to address the cold-start problem.
The tutorial sessions will be interspersed with examples from an online marketplace (i.e., Flipkart) and experience with using data mining and Natural Language Processing techniques (e.g., matrix factorization, LDA, word embeddings) from Web-scale systems.
Building Large-scale Real-world Recommender Systems - Recsys2012 tutorialXavier Amatriain
There is more to recommendation algorithms than rating prediction. And, there is more to recommender systems than algorithms. In this tutorial, given at the 2012 ACM Recommender Systems Conference in Dublin, I review things such as different interaction and user feedback mechanisms, offline experimentation and AB testing, or software architectures for Recommender Systems.
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
Déjà Vu: The Importance of Time and Causality in Recommender SystemsJustin Basilico
Talk at RecSys 2017 in Como, Italy on 2017-08-29.
Abstract:
Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the important challenges of handling time in recommendation algorithms at Netflix. We will focus on challenges related to how our users, items, and systems all change over time. We will then discuss some strategies for tackling these challenges, which revolves around proper treatment of causality in our systems.
Deep Learning For Practitioners, lecture 2: Selecting the right applications...ananth
In this presentation we articulate when deep learning techniques yield best results from a practitioner's view point. Do we apply deep learning techniques for every machine learning problem? What characteristics of an application lends itself suitable for deep learning? Does more data automatically imply better results regardless of the algorithm or model? Does "automated feature learning" obviate the need for data preprocessing and feature design?
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Product Recommendations Enhanced with Reviewsmaranlar
Tutorial presented by Muthusamy Chelliah (Flipkart, India) and Sudeshna Sarkar (IIT Kharagpur, India) at ACM RecSys 2017 https://recsys.acm.org/recsys17/tutorials/#content-tab-1-3-tab
E-commerce websites commonly deploy recommender systems that make use of user activity (e.g., ratings, views, and purchases) or content (product descriptions). These recommender systems can benefit enormously by also exploiting the information contained in customer reviews. Reviews capture the experience of multiple customers with diverse preferences, often on the fine-grained level of specific features of products. Reviews can also identify consumers’ preferences for product features and provide helpful explanations. The usefulness of reviews is evidenced by the prevalence of their use by customers to support shopping decisions online. With the appropriate techniques, recommender systems can benefit directly from user reviews.
This tutorial will present a range of techniques that allow recommender systems in e-commerce websites to take full advantage of reviews. Topics covered include text mining methods for feature-specific sentiment analysis of products, topic models and distributed representations that bridge the vocabulary gap between user reviews and product descriptions, and recommender algorithms that use review information to address the cold-start problem.
The tutorial sessions will be interspersed with examples from an online marketplace (i.e., Flipkart) and experience with using data mining and Natural Language Processing techniques (e.g., matrix factorization, LDA, word embeddings) from Web-scale systems.
Building Large-scale Real-world Recommender Systems - Recsys2012 tutorialXavier Amatriain
There is more to recommendation algorithms than rating prediction. And, there is more to recommender systems than algorithms. In this tutorial, given at the 2012 ACM Recommender Systems Conference in Dublin, I review things such as different interaction and user feedback mechanisms, offline experimentation and AB testing, or software architectures for Recommender Systems.
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
Déjà Vu: The Importance of Time and Causality in Recommender SystemsJustin Basilico
Talk at RecSys 2017 in Como, Italy on 2017-08-29.
Abstract:
Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the important challenges of handling time in recommendation algorithms at Netflix. We will focus on challenges related to how our users, items, and systems all change over time. We will then discuss some strategies for tackling these challenges, which revolves around proper treatment of causality in our systems.
Deep Learning For Practitioners, lecture 2: Selecting the right applications...ananth
In this presentation we articulate when deep learning techniques yield best results from a practitioner's view point. Do we apply deep learning techniques for every machine learning problem? What characteristics of an application lends itself suitable for deep learning? Does more data automatically imply better results regardless of the algorithm or model? Does "automated feature learning" obviate the need for data preprocessing and feature design?
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Ludwig is a new type of branding agency built on a worldwide growing community of independent creative and strategic experts. We collaborate, define, create and execute global and local branding campaigns.
Learn more about our expertise and new business model. www.weareludwig.com
Microsoft Office 2007 PL. Rady i wskazówki. Rozwiązania w biznesieWydawnictwo Helion
Poznaj pełnię możliwości pakietu Office 2007
* Jak kontrolować wygląd oraz treść dokumentów i arkuszy?
* W jaki sposób tworzyć rozbudowane bazy danych?
* Jak zarządzać terminami i kontaktami?
Pakiet biurowy Microsoft Office 2007 to użyteczne narzędzie. Ogromna większość jego użytkowników wykorzystuje jednak bardzo niewielką część oferowanych możliwości. Szacuje się, że ponad 90% możliwości Office’a pozostaje nieznane. Co jest tego powodem? Owe ukryte możliwości, pomimo swojej przydatności, rzadko są opisywane podręcznikach, a w dokumentacji programu pojawiają się w formie często trudnej do zrozumienia. Prawie wszyscy koncentrują się na podstawowych zastosowaniach Worda, Excela, PowerPointa i Accessa, zupełnie pomijając te elementy, które mogą znacznie przyspieszyć i usprawnić pracę z pakietem Office.
Książka "Microsoft Office 2007 PL. Rady i wskazówki. Rozwiązania w biznesie“ jest inna. Autor, bazując na ponad 20-letnim doświadczeniu w pracy z MS Office, zebrał w niej setki wskazówek i praktycznych rozwiązań pozwalających na wyciśnięcie maksimum możliwości z Microsoft Office 2007 za pomocą mało znanych lub dopiero odkrytych ciekawych sztuczek i technik. Czytając tę książkę, poznasz niezwykle wygodne sposoby formatowania tekstu i kontrolowania wyglądu dokumentów, usprawniania obliczeń w Excelu, tworzenia wspaniałych prezentacji w PowerPoint i zarządzania danymi w Accessie, odkryjesz nowe możliwości Outlooka i nauczysz się korzystać z makropoleceń oraz języka VBA.
* Optymalizacja pracy z tekstem
* Nieznane sposoby formatowania dokumentów
* Zaawansowanie formatowanie strony
* Efektywne funkcje i obliczenia
* Szybka analiza danych i wykresy
* Ciekawe animacje na slajdach w PowerPoint
* Niezwykłe możliwości poczty elektronicznej
* Sprawne zarządzanie danymi
Usprawnij swoją pracę – wyciśnij z MS Office 100% możliwości.
In the world of recommendation systems, there are various theories and algorithms that work together to give the best results. Among these, the core recommendation algorithm is crucial. This paper will provide an introduction to some fundamental algorithms used in recommendation systems. These algorithms are like building blocks that help make recommendations more effective.
Building High Available and Scalable Machine Learning ApplicationsYalçın Yenigün
The slide contains some high level information about some machine learning algorithms, cross validation and feature extraction techniques. It also contains high level techniques about high available and scalable ML products.
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
A 1h webinar on RecSys for the Udacity NanoDegree Program "How to become a Data Scientist" : https://in.udacity.com/course/data-scientist-nanodegree--nd025
[UPDATE] Udacity webinar on Recommendation SystemsAxel de Romblay
A 1h webinar on RecSys for the Udacity NanoDegree Program "How to become a Data Scientist" : https://in.udacity.com/course/data-scientist-nanodegree--nd025.
The link to the ipynb : https://www.kaggle.com/axelderomblay/udacity-workshop-on-recommendation-systems
Predicting user engagement with direct displays (DD) is of paramount importance to commercial search engines, as well as to search performance evaluation. However, understanding within-content engagement on a web page is not a trivial task mainly because of two reasons: (1) engagement is subjective and different users may exhibit different behavioural patterns; (2) existing proxies of user engagement (e.g., clicks, dwell time) suffer from certain caveats, such as the well-known position bias, and are not as effective in discriminating between useful and non-useful components. In this paper, we conduct a crowdsourcing study and examine how users engage with a prominent web search engine component such as the knowledge module (KM) display. To this end, we collect and analyse more than 115k mouse cursor positions from 300 users, who perform a series of search tasks. Furthermore, we engineer a large number of meta-features which we use to predict different proxies of user engagement, including attention and usefulness. In our experiments, we demonstrate that our approach is able to predict more accurately different levels of user engagement and outperform existing baselines.
Movie Recommender System Using Artificial Intelligence Shrutika Oswal
In recent years, a huge amount of information is available on the internet and it is very difficult for the user to collect the relevant information. While purchasing any product also a lot of choices available and the user is confused about what to choose. This will be a time-consuming process as well. The search engine will solve this problem to some extent by but it will fail in giving a personalized recommendation. In this presentation, I will describe the different types and working of the recommender system how they gather the data, build recommender, generate recommendations from it, evaluate the performance and effectiveness of the recommender system. The further part of the presentation will describe how to build a movie recommender system using python.
PyCon Balkans 2018 // Recommender systems - collaborative filtering and dimen...Mladen Jovanovic
Recommender systems are considered as an inevitable part of any system that is offering some kind of products/services to the final user. System complexity can range anywhere from simple webstores displaying few dozen articles to big web applications with complex architecture offering millions of items to as many users.
As the systems grew bigger, rising need of efficiently handling and presenting that amount of data in a meaningful manner emerged. Early steps were focused on good categorization of available items, improving browsing capabilities and providing intelligent search. But the main aspect of user engagement remained the same. User had to take action in order to have the items presented to them. This is where the recommender systems came in, radically changing the way how the end users are experiencing the whole platform. Instead of browsing the platform in order to discover something new and relevant, the items are directly presented to the user based on previous experience, not just from their particular history of actions but from the experience of the whole user community as well.
Over the years, variety of techniques have been developed for building recommender systems, including content-based, collaborative filtering each having their own pros and cons. We’ll cover both of them offering relative insight how to treat some of the challenges involved in dealing with this kind data like matrix sparsity, dimensionality reduction etc.
Data/AI driven product development: from video streaming to telehealthXavier Amatriain
Healthcare is different from any other application domain, or is it not? While it is true that there are specific aspects, such as high stakes decisions and a complex regulatory framework, that make healthcare somewhat different, it is also the case that many of the lessons learned from building data-driven products in other domains translate remarcably well into healthcare. This is particularly so because healthcare is also a user facing domain, where users can be both patients or healthcare professionals. Given that data has shown to improve user experience while ensuring quality and scalability, few would argue that healthcare cannot benefit from being much more data-driven than it has traditionally been.
In this talk, I described how this experience building impactful data and AI solutions into user facing products for decades can be leveraged to revolutionize telehealth. At Curai, we combine approaches such as state-of-the-art large language models with expert systems in areas such as NLP, vision, and automated diagnosis to augment and scale doctors, and to improve user experience and healthcare outcomes. We will see some of those applications while analyzing the role of data and ML algorithms in making them possible.
AI-driven product innovation: from Recommender Systems to COVID-19Xavier Amatriain
AI/Machine Learning has become an integral part of many household tech products, from Netflix to our phones. In this talk I will draw from my experience driving AI teams at some of those companies to showcase how AI can positively impact products as different as Netflix and Curai, an online telehealth service.
With half of the world’s population lacking access to healthcare services, and 30% of the adult population in the US having inadequate health insurance coverage to get even basic access to services, it should have been clear that a pandemic like COVID-19 would strain the global healthcare system way over its maximum capacity. In this context, many are trying to embrace and encourage the use of telehealth as a way to provide safe and convenient access to care. However, telehealth in itself can not scale to cover all our needs unless we improve scalability and efficiency through AI and automation.
In this talk, we will describe how our work on combining latest AI advances with medical experts and online access has the potential to change the landscape in healthcare access and provide 24/7 quality healthcare. Combining areas such as NLP, vision, and automatic diagnosis we can augment and scale doctors. We will describe our work on combining expert systems with deep learning to build state-of-the-art medical diagnostic models that are also able to model the unknowns. We will also show our work on using language models for medical Q&A . More importantly, we will describe how those approaches have been used to address the urgent and immediate needs of the current pandemic.
AI for COVID-19: An online virtual care approachXavier Amatriain
Slides for the talk I gave at the AI and COVID-19 virtual conference at Stanford. Video here: https://hai.stanford.edu/events/covid-19-and-ai-virtual-conference/video-archive
From one to zero: Going smaller as a growth strategyXavier Amatriain
This talk was designed for Engineering managers. Having been at companies of all sizes, I recommend managers who want to grow to go smaller. At the same time I reflect on what are the important things that remain constant regardless the size and context and which ones don't.
Deep learning has accomplished impressive feats in areas such as voice recognition, image processing, and natural language processing. Deep learning enthusiasts have rushed to predict that this family of algorithms is likely to take over most other applications in the near future. This focus on deep architectures seems to have cast a shadow over more “traditional” machine learning and data science approaches, leaving researchers and practitioners alike wondering whether there is any point in investing in feature engineering or simpler models.
In this talk, I will go over what deep learning can and cannot do for you, both now and in the near future. I will also describe how different approaches will continue to be needed, and why their demand will likely grow despite the rise of deep learning. I will support my claims not only by looking at recent publications, but also by using practical examples drawn from my experience at companies at the forefront of machine learning applications, such as Quora.
Lean DevOps - Lessons Learned from Innovation-driven CompaniesXavier Amatriain
Presentation I gave at the IEEE Devops Symposium in the Computer History Museum, Mountain View. I describe the CASSSH model for Devops as well as lessons learned in innovation-driven companies.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
13. Consumer (Data) Science
1.
Start with a hypothesis:
■ Algorithm/feature/design X will increase member engagement
with our service, and ultimately member retention
2.
Design a test
■ Develop a solution or prototype
■ Think about dependent & independent variables, control,
significance…
3.
4.
Execute the test
Let data speak for itself
15. Executing A/B tests
Measure differences in metrics across statistically identical populations
that each experience a different algorithm.
■ Decisions on the product always data-driven
■ Overall Evaluation Criteria (OEC) = member retention
■ Use long-term metrics whenever possible
■ Short-term metrics can be informative and allow faster decisions
■ But, not always aligned with OEC
■ Significance and hypothesis testing (1000s of members and 220 cells)
■ A/B Tests allow testing many (radical) ideas at the same
time (typically 100s of customer A/B tests running)
16. Offline testing
■ Measure model performance, using (IR) metrics
■ Offline performance used as an indication to make
informed decisions on follow-up A/B tests
■ A critical (and mostly unsolved) issue is how offline
metrics can correlate with A/B test results.
■ Extremely important to define offline evaluation
framework that maps to online OEC
■ e.g. How to create training/testing datasets may not be trivial
18. Big Data @Netflix
Time
Impressions
Metadata
Social
■ > 40M subscribers
■ Ratings: ~5M/day
■ Searches: >3M/day
Geo-information
■ Plays: > 50M/day
■ Streamed hours:
○ 5B hours in
Member Behavior Q3 2013
Ratings
Device Info
Demographics
19. Smart Models
■ Regression models (Logistic,
Linear, Elastic nets)
■ SVD & other MF models
■ Factorization Machines
■ Restricted Boltzmann Machines
■ Markov Chains & other graph
models
■ Clustering (from k-means to
HDP)
■ Deep ANN
■ LDA
■ Association Rules
■ GBDT/RF
■ …
20. SVD for Rating Prediction
■ User factor vectors
■ Baseline (bias)
and item-factors vectors
(user & item deviation
from average)
■ Predict rating as
■ SVD++ (Koren et. Al) asymmetric variation w.
implicit feedback
■ Where
■
are three item factor vectors
■ Users are not parametrized, but rather represented by:
■ R(u): items rated by user u & N(u): items for which the user
has given implicit preference (e.g. rated/not rated)
21. Restricted Boltzmann Machines
■ Restrict the connectivity in ANN to
make learning easier.
■
Only one layer of hidden units.
■
■
Although multiple layers are possible
No connections between hidden
units.
■ Hidden units are independent given
the visible states..
■ RBMs can be stacked to form Deep
Belief Networks (DBN) – 4th generation
of ANNs
22. Ranking
■ Ranking = Scoring + Sorting + Filtering
bags of movies for presentation to a user
■ Key algorithm, sorts titles in most contexts
■ Goal: Find the best possible ordering of a
set of videos for a user within a specific
context in real-time
■ Objective: maximize consumption &
“enjoyment”
■ Factors
■
■
■
■
■
■
Accuracy
Novelty
Diversity
Freshness
Scalability
…
23. Example: Two features, linear model
2
3
4
5
Popularity
Linear Model:
frank(u,v) = w1 p(v) + w2 r(u,v) + b
Final Ranking
Predicted Rating
1
26. Learning to Rank Approaches
■ ML problem: construct ranking model from training data
1.
2.
3.
Pointwise (Ordinal regression, Logistic regression, SVM, GBDT, …)
■
Loss function defined on individual relevance judgment
Pairwise (RankSVM, RankBoost, RankNet, FRank…)
■
Loss function defined on pair-wise preferences
■
Goal: minimize number of inversions in ranking
Listwise
■
Indirect Loss Function (RankCosine, ListNet…)
■
Directly optimize IR measures (NDCG, MRR, FCP…)
■
■
■
■
Genetic Programming or Simulated Annealing
Use boosting to optimize NDCG (Adarank)
Gradient descent on smoothed version (CLiMF, TFMAP, GAPfm @cikm13)
Iterative Coordinate Ascent (Direct Rank @kdd13)
27. Other research questions we are working on
●
●
●
●
●
●
●
●
●
Row selection
Diversity
Similarity
Context-aware recommendations
Explore/exploit
Presentation bias correction
Mood and session intent inference
Unavailable Title Search
...
29. More data or better models?
Really?
Anand Rajaraman: Former Stanford Prof. &
Senior VP at Walmart
30. More data or better models?
Sometimes, it’s not
about more data
31. More data or better models?
[Banko and Brill, 2001]
Norvig: “Google does not
have better Algorithms,
only more Data”
Many features/
low-bias models
32. More data or better models?
Sometimes, it’s not
about more data