In this talk at the Netflix Machine Learning Platform Meetup on 12 Sep 2019, Fernando Amat and Elliot Chow from Netflix talk about the Bandit infrastructure for Personalized Recommendations
LinkedIn talk at Netflix ML Platform meetup Sep 2019Faisal Siddiqi
In this talk at the Netflix Machine Learning Platform Meetup on 12 Sep 2019, Kinjal Basu from LinkedIn discussed Online Parameter Selection for web-based Ranking vis Bayesian Optimization
Facebook Talk at Netflix ML Platform meetup Sep 2019Faisal Siddiqi
In this talk at the Netflix Machine Learning Platform Meetup on 12 Sep 2019, Sam Daulton from Facebook discusses "Practical Solutions to real-world exploration problems".
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.
Personalized Page Generation for Browsing RecommendationsJustin Basilico
Talk from First Workshop on Recommendation Systems for TV and Online Video at RecSys 2014 in Foster City, CA on 2014-10-10 about how we personalize the layout of the Netflix homepage to make it easier for people to browse the recommendations to quickly find something to watch and enjoy.
LinkedIn talk at Netflix ML Platform meetup Sep 2019Faisal Siddiqi
In this talk at the Netflix Machine Learning Platform Meetup on 12 Sep 2019, Kinjal Basu from LinkedIn discussed Online Parameter Selection for web-based Ranking vis Bayesian Optimization
Facebook Talk at Netflix ML Platform meetup Sep 2019Faisal Siddiqi
In this talk at the Netflix Machine Learning Platform Meetup on 12 Sep 2019, Sam Daulton from Facebook discusses "Practical Solutions to real-world exploration problems".
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.
Personalized Page Generation for Browsing RecommendationsJustin Basilico
Talk from First Workshop on Recommendation Systems for TV and Online Video at RecSys 2014 in Foster City, CA on 2014-10-10 about how we personalize the layout of the Netflix homepage to make it easier for people to browse the recommendations to quickly find something to watch and enjoy.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
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.
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.
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
Personalizing "The Netflix Experience" with Deep LearningAnoop Deoras
These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
In this presentation, we survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will present case studies in the space of deep latent variable models applied to recommender systems.
Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges an...Qi Guo
*** Please check out our LinkedIn Engineering blog post: https://engineering.linkedin.com/blog/2019/04/ai-behind-linkedin-recruiter-search-and-recommendation-systems ***
LinkedIn Talent Solutions business contributes to around 65% of LinkedIn’s annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities. LinkedIn’s job ecosystem has been designed as a platform to connect job providers and job seekers, and to serve as a marketplace for efficient matching between potential candidates and job openings. A key mechanism to help achieve these goals is the LinkedIn Recruiter product, which enables recruiters to search for relevant candidates and obtain candidate recommendations for their job postings.
We highlight a few unique information retrieval, system, and modeling challenges associated with talent search and recommendation systems.
In this talk, we will present how we formulated and addressed the problems, the overall system design and architecture, the challenges encountered in practice, and the lessons learned from the production deployment of these systems at LinkedIn. By presenting our experiences of applying techniques at the intersection of recommender systems, information retrieval, machine learning, and statistical modeling in a large-scale industrial setting and highlighting the open problems, we hope to stimulate further research and collaborations within the SIGIR community.
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.
How do we protect privacy of users in large-scale systems? How do we ensure fairness and transparency when developing machine learned models? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical and legal challenges encountered by researchers and practitioners alike. In this talk (presented at QConSF 2018), we first present an overview of privacy breaches as well as algorithmic bias / discrimination issues observed in the Internet industry over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving privacy and fairness in data-driven systems. We motivate the need for adopting a "privacy and fairness by design" approach when developing data-driven AI/ML models and systems for different consumer and enterprise applications. We also focus on the application of privacy-preserving data mining and fairness-aware machine learning techniques in practice, by presenting case studies spanning different LinkedIn applications, and conclude with the key takeaways and open challenges.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
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.
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.
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
Personalizing "The Netflix Experience" with Deep LearningAnoop Deoras
These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
In this presentation, we survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will present case studies in the space of deep latent variable models applied to recommender systems.
Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges an...Qi Guo
*** Please check out our LinkedIn Engineering blog post: https://engineering.linkedin.com/blog/2019/04/ai-behind-linkedin-recruiter-search-and-recommendation-systems ***
LinkedIn Talent Solutions business contributes to around 65% of LinkedIn’s annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities. LinkedIn’s job ecosystem has been designed as a platform to connect job providers and job seekers, and to serve as a marketplace for efficient matching between potential candidates and job openings. A key mechanism to help achieve these goals is the LinkedIn Recruiter product, which enables recruiters to search for relevant candidates and obtain candidate recommendations for their job postings.
We highlight a few unique information retrieval, system, and modeling challenges associated with talent search and recommendation systems.
In this talk, we will present how we formulated and addressed the problems, the overall system design and architecture, the challenges encountered in practice, and the lessons learned from the production deployment of these systems at LinkedIn. By presenting our experiences of applying techniques at the intersection of recommender systems, information retrieval, machine learning, and statistical modeling in a large-scale industrial setting and highlighting the open problems, we hope to stimulate further research and collaborations within the SIGIR community.
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.
How do we protect privacy of users in large-scale systems? How do we ensure fairness and transparency when developing machine learned models? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical and legal challenges encountered by researchers and practitioners alike. In this talk (presented at QConSF 2018), we first present an overview of privacy breaches as well as algorithmic bias / discrimination issues observed in the Internet industry over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving privacy and fairness in data-driven systems. We motivate the need for adopting a "privacy and fairness by design" approach when developing data-driven AI/ML models and systems for different consumer and enterprise applications. We also focus on the application of privacy-preserving data mining and fairness-aware machine learning techniques in practice, by presenting case studies spanning different LinkedIn applications, and conclude with the key takeaways and open challenges.
Real-time big data analytics based on product recommendations case studydeep.bi
We started as an ad network. The challenge was to recommend the best product (out of millions) to the right person in a given moment (thousands of users within a second). We have delivered 5 billion ad views since 24 months. To put it in the scale context: If we would serve 1 ad per second it will take 160 years to serve 5 billion ads.
So we needed a solution. SQL databases did not work. Popular NoSQL databases did not work. Standard data warehouse approaches (pre-aggregations, creating schemas) - did not work too.
Re-thinking all the problems with huge data streams flowing to us every second we have built a complete solution based on open-source technologies and fresh, smart ideas from our engineering team. It is called deep.bi and now we make it available to other companies.
deep.bi lets high-growth companies solve fast data problems by providing scalable, flexible and real-time data collection, enrichment and analytics.
It was built using:
- Node.js - API
- Kafka - collecting and distributing data
- Spark Streaming - ETL, data enrichments
- Druid - real-time analytics
- Cassandra - user events store
- Hadoop + Parquet + Spark - raw data store + ad-hoc queries
Pinterest - Big Data Machine Learning Platform at PinterestAlluxio, Inc.
This was presented by the Yongsheng Wu, head of big data and ML platform at Pinterest, at the Alluxio bay area meetup.
Yongsheng shares Pinterest's journey to build a fast and scalable big data and ML platform in AWS for Pinterest to handle the requests and complexity in data at scale. In this talk, he will cover different aspects from the requirements of the platform, the challenges encountered, the technologies chosen, and the tradeoffs that were made.
IRIS.TV Talks Future of Video Personalization at Cross Campus LAIRIS.TV
In a digital economy moving more towards personalization, consumers expect recommendation services to work seamlessly and intelligently. Publishers and distributors of video are being judged not only on the quality of their content, but also on their user experience. This has resulted in a shift toward personalized recommendation engines to increase engagement and improve retention. Beyond the user interface, the quality of the experience is largely influenced by the ability to present the viewer with the most relevant content in real time. IRIS.TV Chief Data Scientist Dr. Thomas Sullivan and VP of Engineering Joel Spitalnik explore these topics before the LA Machine Learning Group.
Watch the talk here http://www.iris.tv/iris-tv-industry-research-reports/iris-tv-talks-future-of-personalization-la-machine-learning-group/
Snowplow: open source game analytics powered by AWSGiuseppe Gaviani
This is a presentation by Alex Dean and Yali Sassoon at Snowplow about open source game analytics powered by AWS. It was presented at the Games Developer Conference (GDC) in San Francisco, February 2017
Big Data LDN 2018: ENABLING DATA-DRIVEN DECISIONS WITH AUTOMATED INSIGHTSMatt Stubbs
Date: 13th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Charlotte Emms
Organisation: seenit
About: How do you get your colleagues interested in the power of data? Taking you through Seenit’s journey using Couchbase's NoSQL database to create a regular, fully automated update in an easily digestible format.
Using Apache Kafka to Analyze Session Windowsconfluent
Speaker: Michael Noll, Product Manager, Confluent
In this online talk, we’ll introduce the concept of a session window, talk about common use cases, and walk through how Apache Kafka can be used for session-oriented use cases.
Apache Unomi presentation and update. By Serge Huber, CTO JahiaJahia Solutions Group
Serge Huber, CTO & Co-founder of Jahia presents Apache Unomi Project and it's evolution over years. He also updates us with the project's upcoming news & updates.
ApacheCon NA 2018 : Apache Unomi, an Open Source Customer Data Platformapache...Serge Huber
In the days of personalized and privacy-conscious websites, more and more integrators and developers are quickly integrating different microservices to develop final solutions, be it websites or mobile applications. The downside is that many of the biggest services will then have total control over the data, and in some cases might even charge you for access to your own raw data!
Apache Unomi provides an alternative to this problem, as it is a completely free and open platform for all the data that is collected when visitors interact with your systems. It also offers features such as visitor segmentation and real-time rule execution for decisioning and personalization. In this presentation we will show you (possibly in live coding) how to integrate Apache Unomi into your own website and what the benefits are. And all this while respecting your customer’s data privacy rights (GDPR) !
In April 2014, Pinterest engineers presented to members of the engineering community at a series of Tech Talks held at the Pinterest offices in San Francisco. Topics included:
- Mobile & Growth: Scaling user education on mobile, and a deep dive into the new user experience (with engineers Dannie Chu and Wendy Lu)
- Monetization & Data: The open sourcing of Pinterest Secor and a look at zero data loss log persistence services (with engineer Pawel Garbacki)
- Developing & Shipping Code at Pinterest: The tools and technologies Pinterest uses to build quickly and deploy confidently.
You can find more at: engineering.pinterest.com and facebook.com/pinterestengineering
How Celtra Optimizes its Advertising Platform with DatabricksGrega Kespret
Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions.
In this webinar, you will learn how Databricks helps Celtra to:
- Utilize Apache Spark to power their production analytics pipeline.
- Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics.
- Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.
user Behavior Analysis with Session Windows and Apache Kafka's Streams APIconfluent
For many industries the need to group together related events based on a period of activity or inactivity is key. Advertising businesses, content producers are just a few examples of where session windows can be used to better understand user behavior.
While such sessionization has been possible in Apache Kafka up to this point, implementing it has been rather complex and required leveraging low-level APIs. In the most recent release of Kafka, however, new capabilities have been added making session windows much easier to implement.
In this online talk, we’ll introduce the concept of a session window, talk about common use cases, and walk through how Apache Kafka can be used for session-oriented use cases.
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
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
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
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.
3. Recommendations at Netflix
● Personalized Homepage for each member
● Goal: quickly help members find content they love
● Challenge:
○ 150M+ members in 190 countries
○ New content added daily
● Recommendations Valued at: $1B*
*Carlos A. Gomez-Uribe, Neil Hunt: The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Trans. Management Inf. Syst. 6(4): 13:1-13:19 (2016)
9. Evidence personalization
● Multiple choices for each (show, evidence) pair
● For each title, hard to predict what resonates before launch
● Taste can change over time
11. Bandit setup
● Generic framework to obtain unbiased training data
● Very vast literature (beyond scope)
● Every session is a mini A/B test
Pred 1-Pred
reward?
12. Bandit setup for evidence
● Goal: for each mini A/B test collect a sample with
○ Context x
○ Selected action (or treatment) a
○ Propensity p
○ Set of possible actions s
○ Reward r
● Once you have this, very similar to other ML approaches
● Obtaining this data is the hard part infrastructure-wise
(Elliot’s section)
13. System requirements
● Scalable: at Netflix’s global size
● Generic: common policy (or model) and offline metrics API
● Flexible:
○ Each recommendation problems has different
attribution, reward and context definition
○ Easy to add new canvases to the bandit system (image,
video, text, cards, etc)
14. Policy API
● Class Slate (List[Items] items)
○ Vector of ids to recreate a composition
(“row_10”, “img_124”, “img_1037”,...,
“synopsis_32”, “card_101”)
1
2 3 4 5
7 6
8
9
15. Policy API
● Slate select(List[List[Item]] items)
○ Given a list of possible slates, return one of them
(explore-exploit trade-off)
● Map[Slate, Double] propensity(List[List[Item]
items)
○ Return propensity of each possible slate to debias data
16. Offline policy evaluation
● Unbiased offline evaluation from logged samples
● Inverse Propensity Scoring (IPS), Doubly Robust, etc
E[Reward] = 2 / 3
User 1 User 2 User 3 User 4 User 5 User 6
Logged stochastic
treatment
Play?
(binary
reward)
New policy
assignment
17. Offline policy evaluation system
● Input:
○ Logged sample (x, a, s, r, p)
○ Trained policy
● Output:
○ Metric (IPS, SNIPS, DM, DR, RECAP, etc)
● Observations:
○ Trivially parallelizable (decomposes per sample)
○ Each sample needs to consider all possible candidates
19. Actions taken by a Netflix member
- Log-in
- Search for “stra”
- Play Stranger Things
...
Data:
Member Activity
20. Describes Netflix’s desire to take a specific action
- Recommend Stranger Things
- Display Image
...
Data:
Intent-to-Treat (ITT)
21. The actual experience as seen by the Netflix member
- Showed Stranger Things on Home Page
- Displayed Image
...
Data:
Treatment
22. Log In Play Stranger
Things
Post-PlayIntent-to-treatHome
Page
13 Reasons Why
on Popular on
Netflix
Stranger
Things
on My List
Treatment
Member Actions
24. “Closing the
Loop”:
Join Intent-to-
Treat with
Treatment
- Did the intent-to-treat
take effect as desired?
- Which policy was
used?
- What were the
propensities?
- What features were
used?
29. Real-time
Processing with
Flink
Process member activity,
treatment, and intent-to-treat
events in real-time
- Join intent-to-treat and
treatment events
- Prepare data in format
amenable to reward
computation
- Kafka and Hive outputs
30. Spark Client For
Attribution and
Reward
Computation
Provide flexible processing of the
joined member activity, treatment,
and intent-to-treat data
- Events sorted by time
- Unbounded windowing
support
- Optimizations for common
access patterns
Mention study of 1B is from 2016. It is just an approximation.
One aspect of recommendations is once you have selected a title for the user, trying to explain to them why the system things is a good title for them.
This especially important for Netflix Originals, where there is almost no previous knowledge.
For example, I go to my home page and I see this title recommended in the big billboard. OK, seems definitely romance. But is it also commedy? Or more drama? Are there any of my favourite actors/actresses? Do I have time to watch it?
Imagine a recommendation homepage without any evidence… this is how it would look like. You would probably only click on things that you know really well (for example, play The Office S2:E3 for the hundredth time).
TODO: run animation
More detail about Data and Infra side
Can be different from ITT - fall backs, business rules overriding
Timeline view of when this data is produced/by whom
Connect these different discrete pieces of data back together to update our policies
Allows us to answer important questions
Ctr, abandonment….
As soon as possible to allow policies up-to-date/reactive to changes
Cool stuff Fernando mentioned before.
As interest in bandits grew organically, learn from every individual use case and build a number of components to make things easier and more reusable.
Instead of each application logging to different places in different formats…
Ingesting new data becomes easier because uniform.
Make data available ASAP
Library to access data in batch. Provide events in sorted order, unbounded….
In addition to library access, found common patterns in the way rewards are computed. Templatize this so we can materialize this data for our consumers regularly, enable simple SQL access, with no job scheduling/operations work involved.
I’ll highlight a few high-level challenges we faced
Netflix powered by microservices. All requests from the devices go through a single layer called Edge and it fans out to call many microservices. Makes joining ITT and Treatment a bit challenging.
Let’s say we are collecting data for an algorithm used for selecting the best image for a video.
Simple case - ID1 is minted and threaded through subsequent requests for tracing - logged and then passed back to device and then logged with treatment. Join directly.
Let’s say we are collecting data for an algorithm used for selecting the best image for a video.
Simple case - ID1 is minted and threaded through subsequent requests for tracing - logged and then passed back to device and then logged with treatment. Join directly.
Let’s say we are collecting data for an algorithm used for selecting the best image for a video.
Simple case - ID1 is minted and threaded through subsequent requests for tracing - logged and then passed back to device and then logged with treatment. Join directly.
Many cases - caching/precompute. Computation service is unaware of the use of its output when logging
Many cases - caching/precompute. Computation service is unaware of the use of its output when logging. Cannot connect T2 and ITT1. Could approximate with time but not accurate.
Introduced new thing to be logged as part of standardization - id pairs. Each service that uses data logs id pairs when using data (not computing data) so that we can resolve the ids logged with treatment and ITT data.
Implemented as stream processing - typical challenges faced
A lot of IDs and a lot of (Terabytes) state to be dealt with
The question you probably want to ask…
Only scratched the surface - many more parts of the experience to personalize using bandits. Infra will make it much simplet...
