New challenges for scalable machine learning in online advertisingOlivier Koch
The document discusses challenges and opportunities for machine learning in online advertising at scale. It notes that while ML has helped with tasks like bidding and recommendations, challenges remain around long-term effects, overfitting, personalization across devices, and optimal credit assignment and metrics. The document proposes that reinforcement learning, counterfactual analysis, transfer learning and factorization could help address issues like optimal bidding strategies, offline evaluation, and modeling long tail users and products. It concludes by inviting others to help solve remaining open challenges.
New machine learning challenges at CriteoOlivier Koch
This document summarizes machine learning challenges at Criteo, an online advertising company. It discusses how Criteo uses machine learning for bidding, product recommendations, and banner selection. It also outlines some of Criteo's machine learning challenges, including optimal bidding strategies under uncertainty, probabilistic cross-device matching, and modeling long tail users and products. The document concludes that while machine learning applies well to online advertising at scale, there is still room to improve the user experience through new algorithms and making sense of new data sources.
Damien Lefortier, Senior Machine Learning Engineer and Tech Lead in the Predi...MLconf
Machine Learning for Display Advertising @ Scale: In this talk, we will briefly introduce the display advertising marketplace, its stakeholders and the key performance metrics. We will then present the models we have developed at Criteo for bidding in real-time auctions, product recommendation, and look & feel optimization at scale (1B+ monthly users, 3B+ products in our catalog, and 30K ad displayed / sec at peak traffic). For these tasks, we’ve moved over time from predicting rare, binary events (clicks) to predicting very rare events (sales) and continuous events (sales amounts), all of them being quite noisy, and we’ll discuss the different methods that we have tried to build these models (such as generalized linear models, trees or factorization machines). We’ll continue by discussing how we evaluate these models both offline and online. We will describe the infrastructure for large-scale distributed data processing that these algorithms rely upon and discuss different optimization techniques we have experimented with (such as SGD, L-BFGS, SVRG). Finally, we will conclude with future areas of research and discuss open challenges we are currently facing.”
Making advertising personal, 4th NL Recommenders MeetupOlivier Koch
Criteo is a performance advertising company that buys ad inventory and sells clicks at scale. They use real-time personalized product recommendations to select which ads to display to each user from billions of products. Their recommendation system retrieves candidate products for each user based on their browsing history and scores products from multiple data sources to select the top recommendations within 8 milliseconds to support their high traffic levels across many servers and data centers globally. They discuss challenges maintaining large user profiles, improving product data, and optimizing response time and independence of recommendations.
Criteo is an advertising company that displays ads on websites for its partners. It uses machine learning for click prediction models and product recommendations. Criteo has a large infrastructure across multiple data centers and handles over 30 billion HTTP requests per day. The presentation discusses the challenges of building machine learning models at Criteo's scale for real-time bidding and recommendations, such as dealing with many variables, changing products, and different timeframes.
Criteo provides targeted display advertising that is able to predict user clicks. It buys large advertising inventories from publishers using a cost-per-million (CPM) model and sells campaigns to advertisers on a cost-per-click (CPC) model. Criteo's Real-Time Audience solution allows it to drop cookies and show targeted ads to iOS and Safari users. This solution has increased revenue for publishers by over 100% on average by enabling more accurate frequency capping, higher volume, and price points. Integrating Criteo's RTA solution is described as easy and having no impact on page loading times.
Amanda Casari, Senior Data Scientist, Concur at MLconf SEA - 5/20/16MLconf
This document discusses how to scale data science products rather than data science teams. It presents examples of common problems faced when scaling products and classifies them as either product design problems, software engineering problems, or mathy/machine learning problems. The key issues discussed include managing user expectations, maintaining many models, using shared code across customer bases, testing accuracy in new markets, addressing cold starts for unknown customers, and identifying feedback loops.
New challenges for scalable machine learning in online advertisingOlivier Koch
The document discusses challenges and opportunities for machine learning in online advertising at scale. It notes that while ML has helped with tasks like bidding and recommendations, challenges remain around long-term effects, overfitting, personalization across devices, and optimal credit assignment and metrics. The document proposes that reinforcement learning, counterfactual analysis, transfer learning and factorization could help address issues like optimal bidding strategies, offline evaluation, and modeling long tail users and products. It concludes by inviting others to help solve remaining open challenges.
New machine learning challenges at CriteoOlivier Koch
This document summarizes machine learning challenges at Criteo, an online advertising company. It discusses how Criteo uses machine learning for bidding, product recommendations, and banner selection. It also outlines some of Criteo's machine learning challenges, including optimal bidding strategies under uncertainty, probabilistic cross-device matching, and modeling long tail users and products. The document concludes that while machine learning applies well to online advertising at scale, there is still room to improve the user experience through new algorithms and making sense of new data sources.
Damien Lefortier, Senior Machine Learning Engineer and Tech Lead in the Predi...MLconf
Machine Learning for Display Advertising @ Scale: In this talk, we will briefly introduce the display advertising marketplace, its stakeholders and the key performance metrics. We will then present the models we have developed at Criteo for bidding in real-time auctions, product recommendation, and look & feel optimization at scale (1B+ monthly users, 3B+ products in our catalog, and 30K ad displayed / sec at peak traffic). For these tasks, we’ve moved over time from predicting rare, binary events (clicks) to predicting very rare events (sales) and continuous events (sales amounts), all of them being quite noisy, and we’ll discuss the different methods that we have tried to build these models (such as generalized linear models, trees or factorization machines). We’ll continue by discussing how we evaluate these models both offline and online. We will describe the infrastructure for large-scale distributed data processing that these algorithms rely upon and discuss different optimization techniques we have experimented with (such as SGD, L-BFGS, SVRG). Finally, we will conclude with future areas of research and discuss open challenges we are currently facing.”
Making advertising personal, 4th NL Recommenders MeetupOlivier Koch
Criteo is a performance advertising company that buys ad inventory and sells clicks at scale. They use real-time personalized product recommendations to select which ads to display to each user from billions of products. Their recommendation system retrieves candidate products for each user based on their browsing history and scores products from multiple data sources to select the top recommendations within 8 milliseconds to support their high traffic levels across many servers and data centers globally. They discuss challenges maintaining large user profiles, improving product data, and optimizing response time and independence of recommendations.
Criteo is an advertising company that displays ads on websites for its partners. It uses machine learning for click prediction models and product recommendations. Criteo has a large infrastructure across multiple data centers and handles over 30 billion HTTP requests per day. The presentation discusses the challenges of building machine learning models at Criteo's scale for real-time bidding and recommendations, such as dealing with many variables, changing products, and different timeframes.
Criteo provides targeted display advertising that is able to predict user clicks. It buys large advertising inventories from publishers using a cost-per-million (CPM) model and sells campaigns to advertisers on a cost-per-click (CPC) model. Criteo's Real-Time Audience solution allows it to drop cookies and show targeted ads to iOS and Safari users. This solution has increased revenue for publishers by over 100% on average by enabling more accurate frequency capping, higher volume, and price points. Integrating Criteo's RTA solution is described as easy and having no impact on page loading times.
Amanda Casari, Senior Data Scientist, Concur at MLconf SEA - 5/20/16MLconf
This document discusses how to scale data science products rather than data science teams. It presents examples of common problems faced when scaling products and classifies them as either product design problems, software engineering problems, or mathy/machine learning problems. The key issues discussed include managing user expectations, maintaining many models, using shared code across customer bases, testing accuracy in new markets, addressing cold starts for unknown customers, and identifying feedback loops.
The document provides tips for running successful performance display marketing campaigns. It outlines 5 common mistakes to avoid: 1) Not defining clear goals and KPIs, 2) Focusing on placements over understanding customer journeys, 3) Not asking the right questions of data to gain insights, 4) Allowing low quality inventory like bot traffic, and 5) Not optimizing campaigns for mobile users. Following people across devices, prioritizing premium inventory, and gaining insights from smart data analysis are some of the keys to driving maximum performance.
This document discusses Criteo's performance advertising solutions. It highlights that Criteo works with over 9,000 publishers and 7,000 advertisers across 130+ countries. Criteo uses a prediction engine and dynamic creative optimization to deliver measurable ROI at unmatched scale across desktop, mobile, in-app, and social platforms. The document also outlines Criteo's solutions for display, email, and cross-device marketing and notes that setup to go live typically takes about 4 weeks with world-class support included.
This document discusses Criteo's transition from using SQL databases to NoSQL databases like Couchbase to handle their real-time advertising needs at scale. It describes how Criteo grew to handle over 10 million hits per second across 24 Couchbase clusters containing 550 servers with 107 TB of RAM and SSD storage. Key lessons learned included not mixing RAM and persisted data usages, extracting Couchbase stats to Graphite for flexibility, and investing as much in development as operations. The presentation concludes by outlining Criteo's plans to utilize Couchbase replication and improve failover capabilities between data centers.
Presentations from Criteo Labs’ Infrastructure team with a guest speakers from Yandex.
• FastTrack: scaling customer integration
• Evolution of data structures in Yandex.Metrica
• Don't take your software for granted
• Evolution of analytics at Criteo
Back to the Future: Bringing Performance Targeting to Mobile Devices from DRS...Digiday
This document discusses Criteo's approach to bringing performance targeting to mobile devices. It notes that mobile traffic and commerce are growing rapidly, becoming a major opportunity. However, mobile also introduces complexity from fragmentation and lack of standards. Criteo has expanded its platform to address these challenges, offering consistent personalized ads across browsers on mobile web and within apps. Case studies show their mobile performance solutions driving increased click-through rates and conversions for advertisers. The document concludes by advising marketers to capitalize on the shift to mobile by getting involved early and applying techniques that work on mobile.
This 3 sentence summary provides the high level and essential information from the document:
Criteo is a company that provides performance display advertising and has over 550 employees globally that generated $200 million in revenue in 2011 across 4 continents and 3000 clients in 3 countries. The document discusses challenges in search marketing, how Criteo's performance display advertising works through personalized ads and bid optimization, benchmarks showing Criteo's ads outperform search ads in ROI and conversion rates, how clickers are actually valuable buyers contrary to myths, and a case study of success with Overstock
Adx Connect is an ad exchange that:
- Connects publishers, ad networks, and RTB sources with buying channels for high yield and performance.
- Provides features for optimized traffic selling and campaign optimization on both the supply and demand sides.
- Uses advanced algorithms and targeting methodologies to optimize ad revenue through precise audience targeting.
Criteo's Ad Week 2012 presentation - Big Data and the Value of ClickersCriteo
Big data analytics has shown that the conventional wisdom about display ad clicks being worthless was incorrect. It revealed that people who click on ads (clickers) actually buy 3 times more frequently than non-clickers, and half of all clicks and sales come from just 20% of users. Real-time big data is now being used to serve highly targeted display ads to the right users at the right time with the right message, making the ads actually worth clicking on and representing a $20 billion opportunity for the display advertising industry.
Brandscreen is developing a digital media trading platform to address challenges in online display advertising such as lower CPMs, lack of automated planning and buying systems, and need for reliable ROI metrics. The platform allows buyers to plan, optimize and buy across multiple publishers, networks and exchanges in a single live trading screen with automated and transparent trading. It is currently in beta trials in the US and Australia.
Criteo is a technology company that uses machine learning to deliver personalized display advertisements to users. It analyzes large amounts of shopping behavior data to determine the right products to show users at the right time. Criteo has grown rapidly, with over $350 million in revenue in 2012 and a compound annual growth rate of 104% since 2010. It has a global presence with over 750 employees across 15 offices worldwide serving over 4,000 retailer clients. Criteo is also pursuing opportunities in mobile advertising given the growth of mobile commerce.
In 2016, IBM announced their partnership with UNICOM for future development of Content Manager OnDemand (CMOD). While IBM maintains that nothing has changed from a business perspective, they have significantly reduced the CMOD sales team and are no longer handling support. Therefore, it appears that the future of CMOD is uncertain. If you are a current or prospective customer of CMOD, there are three things to consider when analyzing a product that has recently been sold to another corporation.
Can the new provider deliver the support you need to be successful with a product they didn’t build?
Are they truly committed to the product’s features?
Are they trying to lock you into a proprietary cloud service?
During this presentation we will address the challenges of maintaining the CMOD platform and offer an alternative solution: Mobius by ASG Technologies. Zia and ASG have experience in the intricacies of a CMOD rollout and customization as well as in the planning and execution of migrating from a mainframe (or other legacy) repository to Mobius—on-premises or in the cloud.
When business meets measurement protocol - atdconf - 2017 - Tel AvivZorin Radovancevic
Sending data to Google Analytics is easy nowadays due to Measurement Protocol yet when combining offline and online behaviour mind the fine details in order to preserve attribution.
ActOnCloud, powered by ActOnMagic was presented at the recently concluded CloudStack Collaboration Conference, Budapest.
ActOnCloud helps CSPs to run their business effectively and efficiently and potentially lead to them feature in Gartner Magic Quadrant.
Obtaining the Programmatic Holy Grail: Transparency, Flexibility & ControlMediaPost
What if there were a way to receive all of the benefits of a full-stack DSP with the transparency, flexibility, and control of a bespoke solution? You’d be interested right? Beeswax is empowering a new generation of media buyers to break free from the limitations of one-size-fits-all programmatic buying platforms and unlock the benefits of a programmatic solution developed to optimize for the unique needs of their organization.
Presenter:
Ari Paparo, CEO, Beeswax
Our Experience with Adobe Audience Manager DMPMatěj Novák
The document discusses an experience using Adobe Audience Manager for audience targeting on the Czech market. It found that Audience Manager provided flexibility in building complex audience segments and high performance through precise targeting. However, implementation required involvement from multiple parties due to different ad servers used by publishers. Looking forward, expanding data coverage and custom integrations were seen as opportunities to drive more volume.
This document summarizes a cloud call center reseller program from AloTech for Google Partners. The program offers benefits like additional recurring revenue, lead generation training, and Google integration. It is designed to establish collaboration and revenue sharing models for AloTech's cloud call center product. The call center product is competitive, replacing vendors like Cisco and Avaya, and resellers can earn $3,000-$30,000 monthly depending on converted leads. AloTech is the first and only call center running natively on Google Cloud.
Axonite Campaign Automation Infrastructure for HasOffersYuval Shefler
Together with Tune's highly flexible open API platform, Axonite hopes to replace the troublesome ideals associated with ‘bolting’ disparate systems together, and conversely, create an environment that fosters a true sense of unification.
Slides from Berlin buzzwords 2015:
Behavioral retargeting consists of displaying online advertisements that are personalized according to each user’s browsing history. At this point, the selection of the products to display in the banner needs to be fast and accurate. At Criteo, we built a recommender system which is able to choose a dozen of relevant products from over two billion products in a few milliseconds. In this talk, we will expose the problems we faced whilst building this system and how we solved them thanks to a mix of online and offline computations.
Simon Dollé_Large-scale Real-time recommendation at Criteo Dataconomy Media
Criteo sells billions of advertisements per day and recommends relevant products to users within 10 milliseconds. They use large datasets containing ad display data, user behavior data, and product catalog information. Similar products are identified offline using collaborative filtering on browsing data, which takes around 12 hours to compute. Candidates are then ranked online using machine learning models trained on ad display data. Challenges include creating longer user profiles, improving product information, and instantly updating similar product calculations.
Explain the role of a Software Engineer in a tech company like Criteo for students of last year (graduate degree M2) at Grenoble INP - Ensimag, a top french computer engineer school in order to choose his/her career professional path.
See https://ensimag.grenoble-inp.fr/
Top Trends in Building Data Lakes for Machine Learning and AI Holden Ackerman
Presentation by Ashish Thusoo, Co-Founder & CEO at Qubole, on exploring the big data industry trends in moving from data warehouses to cloud-based data lakes.This presentation will cover how companies today are seeing a significant rise in the success of their big data projects by moving to the cloud to iteratively build more cost-effective data pipelines and new products with ML and AI.
Uncovering how services like AWS, Google, Oracle, and Microsoft Azure provide the storage and compute infrastructure to build self-service data platforms that can enable all teams and new products to scale iteratively.
The document provides tips for running successful performance display marketing campaigns. It outlines 5 common mistakes to avoid: 1) Not defining clear goals and KPIs, 2) Focusing on placements over understanding customer journeys, 3) Not asking the right questions of data to gain insights, 4) Allowing low quality inventory like bot traffic, and 5) Not optimizing campaigns for mobile users. Following people across devices, prioritizing premium inventory, and gaining insights from smart data analysis are some of the keys to driving maximum performance.
This document discusses Criteo's performance advertising solutions. It highlights that Criteo works with over 9,000 publishers and 7,000 advertisers across 130+ countries. Criteo uses a prediction engine and dynamic creative optimization to deliver measurable ROI at unmatched scale across desktop, mobile, in-app, and social platforms. The document also outlines Criteo's solutions for display, email, and cross-device marketing and notes that setup to go live typically takes about 4 weeks with world-class support included.
This document discusses Criteo's transition from using SQL databases to NoSQL databases like Couchbase to handle their real-time advertising needs at scale. It describes how Criteo grew to handle over 10 million hits per second across 24 Couchbase clusters containing 550 servers with 107 TB of RAM and SSD storage. Key lessons learned included not mixing RAM and persisted data usages, extracting Couchbase stats to Graphite for flexibility, and investing as much in development as operations. The presentation concludes by outlining Criteo's plans to utilize Couchbase replication and improve failover capabilities between data centers.
Presentations from Criteo Labs’ Infrastructure team with a guest speakers from Yandex.
• FastTrack: scaling customer integration
• Evolution of data structures in Yandex.Metrica
• Don't take your software for granted
• Evolution of analytics at Criteo
Back to the Future: Bringing Performance Targeting to Mobile Devices from DRS...Digiday
This document discusses Criteo's approach to bringing performance targeting to mobile devices. It notes that mobile traffic and commerce are growing rapidly, becoming a major opportunity. However, mobile also introduces complexity from fragmentation and lack of standards. Criteo has expanded its platform to address these challenges, offering consistent personalized ads across browsers on mobile web and within apps. Case studies show their mobile performance solutions driving increased click-through rates and conversions for advertisers. The document concludes by advising marketers to capitalize on the shift to mobile by getting involved early and applying techniques that work on mobile.
This 3 sentence summary provides the high level and essential information from the document:
Criteo is a company that provides performance display advertising and has over 550 employees globally that generated $200 million in revenue in 2011 across 4 continents and 3000 clients in 3 countries. The document discusses challenges in search marketing, how Criteo's performance display advertising works through personalized ads and bid optimization, benchmarks showing Criteo's ads outperform search ads in ROI and conversion rates, how clickers are actually valuable buyers contrary to myths, and a case study of success with Overstock
Adx Connect is an ad exchange that:
- Connects publishers, ad networks, and RTB sources with buying channels for high yield and performance.
- Provides features for optimized traffic selling and campaign optimization on both the supply and demand sides.
- Uses advanced algorithms and targeting methodologies to optimize ad revenue through precise audience targeting.
Criteo's Ad Week 2012 presentation - Big Data and the Value of ClickersCriteo
Big data analytics has shown that the conventional wisdom about display ad clicks being worthless was incorrect. It revealed that people who click on ads (clickers) actually buy 3 times more frequently than non-clickers, and half of all clicks and sales come from just 20% of users. Real-time big data is now being used to serve highly targeted display ads to the right users at the right time with the right message, making the ads actually worth clicking on and representing a $20 billion opportunity for the display advertising industry.
Brandscreen is developing a digital media trading platform to address challenges in online display advertising such as lower CPMs, lack of automated planning and buying systems, and need for reliable ROI metrics. The platform allows buyers to plan, optimize and buy across multiple publishers, networks and exchanges in a single live trading screen with automated and transparent trading. It is currently in beta trials in the US and Australia.
Criteo is a technology company that uses machine learning to deliver personalized display advertisements to users. It analyzes large amounts of shopping behavior data to determine the right products to show users at the right time. Criteo has grown rapidly, with over $350 million in revenue in 2012 and a compound annual growth rate of 104% since 2010. It has a global presence with over 750 employees across 15 offices worldwide serving over 4,000 retailer clients. Criteo is also pursuing opportunities in mobile advertising given the growth of mobile commerce.
In 2016, IBM announced their partnership with UNICOM for future development of Content Manager OnDemand (CMOD). While IBM maintains that nothing has changed from a business perspective, they have significantly reduced the CMOD sales team and are no longer handling support. Therefore, it appears that the future of CMOD is uncertain. If you are a current or prospective customer of CMOD, there are three things to consider when analyzing a product that has recently been sold to another corporation.
Can the new provider deliver the support you need to be successful with a product they didn’t build?
Are they truly committed to the product’s features?
Are they trying to lock you into a proprietary cloud service?
During this presentation we will address the challenges of maintaining the CMOD platform and offer an alternative solution: Mobius by ASG Technologies. Zia and ASG have experience in the intricacies of a CMOD rollout and customization as well as in the planning and execution of migrating from a mainframe (or other legacy) repository to Mobius—on-premises or in the cloud.
When business meets measurement protocol - atdconf - 2017 - Tel AvivZorin Radovancevic
Sending data to Google Analytics is easy nowadays due to Measurement Protocol yet when combining offline and online behaviour mind the fine details in order to preserve attribution.
ActOnCloud, powered by ActOnMagic was presented at the recently concluded CloudStack Collaboration Conference, Budapest.
ActOnCloud helps CSPs to run their business effectively and efficiently and potentially lead to them feature in Gartner Magic Quadrant.
Obtaining the Programmatic Holy Grail: Transparency, Flexibility & ControlMediaPost
What if there were a way to receive all of the benefits of a full-stack DSP with the transparency, flexibility, and control of a bespoke solution? You’d be interested right? Beeswax is empowering a new generation of media buyers to break free from the limitations of one-size-fits-all programmatic buying platforms and unlock the benefits of a programmatic solution developed to optimize for the unique needs of their organization.
Presenter:
Ari Paparo, CEO, Beeswax
Our Experience with Adobe Audience Manager DMPMatěj Novák
The document discusses an experience using Adobe Audience Manager for audience targeting on the Czech market. It found that Audience Manager provided flexibility in building complex audience segments and high performance through precise targeting. However, implementation required involvement from multiple parties due to different ad servers used by publishers. Looking forward, expanding data coverage and custom integrations were seen as opportunities to drive more volume.
This document summarizes a cloud call center reseller program from AloTech for Google Partners. The program offers benefits like additional recurring revenue, lead generation training, and Google integration. It is designed to establish collaboration and revenue sharing models for AloTech's cloud call center product. The call center product is competitive, replacing vendors like Cisco and Avaya, and resellers can earn $3,000-$30,000 monthly depending on converted leads. AloTech is the first and only call center running natively on Google Cloud.
Axonite Campaign Automation Infrastructure for HasOffersYuval Shefler
Together with Tune's highly flexible open API platform, Axonite hopes to replace the troublesome ideals associated with ‘bolting’ disparate systems together, and conversely, create an environment that fosters a true sense of unification.
Slides from Berlin buzzwords 2015:
Behavioral retargeting consists of displaying online advertisements that are personalized according to each user’s browsing history. At this point, the selection of the products to display in the banner needs to be fast and accurate. At Criteo, we built a recommender system which is able to choose a dozen of relevant products from over two billion products in a few milliseconds. In this talk, we will expose the problems we faced whilst building this system and how we solved them thanks to a mix of online and offline computations.
Simon Dollé_Large-scale Real-time recommendation at Criteo Dataconomy Media
Criteo sells billions of advertisements per day and recommends relevant products to users within 10 milliseconds. They use large datasets containing ad display data, user behavior data, and product catalog information. Similar products are identified offline using collaborative filtering on browsing data, which takes around 12 hours to compute. Candidates are then ranked online using machine learning models trained on ad display data. Challenges include creating longer user profiles, improving product information, and instantly updating similar product calculations.
Explain the role of a Software Engineer in a tech company like Criteo for students of last year (graduate degree M2) at Grenoble INP - Ensimag, a top french computer engineer school in order to choose his/her career professional path.
See https://ensimag.grenoble-inp.fr/
Top Trends in Building Data Lakes for Machine Learning and AI Holden Ackerman
Presentation by Ashish Thusoo, Co-Founder & CEO at Qubole, on exploring the big data industry trends in moving from data warehouses to cloud-based data lakes.This presentation will cover how companies today are seeing a significant rise in the success of their big data projects by moving to the cloud to iteratively build more cost-effective data pipelines and new products with ML and AI.
Uncovering how services like AWS, Google, Oracle, and Microsoft Azure provide the storage and compute infrastructure to build self-service data platforms that can enable all teams and new products to scale iteratively.
VUCA - Planning for the essentially unplannable in a disruptive worldJoakim Lindbom
Existing approaches used in delivering IT and business solutions are overthrown when the planning horizon is becoming shorter and shorter. How do you success and avoid being disrupted?
1. Cloud Adoption Journey reference framework to help Teams move to Cloud and become Cloud Native
2. Define basic Pillars to include Security & Compliance, Costs Optimization, Scalability and Performance as well as Operational Excellence, AWS Well-Architected as guidance
3. Goal is to assess and guide Companies/Teams in Portfolio to faster adopt and evolve Cloud concepts to focus on Business value
4. Governance as a key driver to boost flexibility, reduce risks and foster efficiency
5. Enterprise Transformation Architecture offerings
This document discusses why Liferay is a better choice than other enterprise portal solutions like IBM Websphere, Microsoft SharePoint, and BEA Weblogic. It argues that Liferay has significant advantages in terms of cost, innovation, standards compliance, and value. As an open source product, Liferay offers lower total cost of ownership compared to proprietary alternatives. It concludes that choosing Liferay allows organizations to spend money, including public funds, in a more responsible and effective manner.
Francesco Furiani - Marketing is a serious business, moreover tracking and monetizing the campaign that allows your marketing to flourish is very important: our tool allows anyone to monitor, compare and optimize all those campaigns (delivered via links) in one place and to deliver insights about who's using those links. Making this infrastructure, making it works, deliver results in real-time (when necessary) and keep everyone happy from the customer to the CFO will be the point of this talk, from the design to the final result with an eye on the costs/risks/benefits of having everything in the cloud.
BIG Data & Hadoop Applications in E-CommerceSkillspeed
Explore the applications of BIG Data & Hadoop in eCommerce via Skillspeed.
BIG Data & Hadoop in eCommerce is a key differentiator, especially in terms of generating optimized customer & back-end experiences. They are used for tracking consumer behavior, optimizing logistics networks and forecasting demand - inventory cycles.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Platform approach to scaling machine learning across the enterpriseOlalekan Fuad Elesin
We will walk through how we are scaling and democratizing the development of intelligent products based on AI with a platform approach. From the culture needed to shape this mindset, to execution which resulted into reducing the time it takes to productionize machine learning by 50%. We will discuss how we leveraged product mindset, coupled with data, to enable data scientists to be 50% more productive, while scaling the knowledge across our internal builder community.
20151119 Sensibilisation des Utilisateurs aux coûts d'usage du CloudObjectif Libre
The document discusses how cloud users can improve cost-efficiency. It introduces Teevity and Objectif Libre, companies that provide cost analytics and OpenStack expertise. Cloud architectures are compared to LEGO bricks, with different options having varying capabilities and costs. True cost-efficiency requires balancing value and cost. The document promotes autoscaling, reducing idle resources, and using CloudKitty and OpenStack to provide cost visibility for public and private clouds.
1) Learn about Myplanet's Headless CMS solution using Gatsby Preview and Contentful’s UI Extensions (https://www.contentful.com/resources/serverless/)
2) their Serverless project with IBM - using Apache OpenWhisk (https://www.ibm.com/cloud/functions)
3) how Myplanet got involved with AWS DeepRacer - a fun way to get started with Reinforcement Learning (RL), and their racing experience at re:Invent DeepRacer League (https://reinvent.awsevents.com/learn/deepracer/)
4) their Machine Learning (ML) research related to finding DeepRacer’s ideal line (https://medium.com/myplanet-musings/the-best-path-a-deepracer-can-learn-2a468a3f6d64).
BONUS: Two TED Talks referenced in the intro
5) When ideas have sex | Matt Ridley | Jul 14, 2010 https://www.ted.com/talks/matt_ridley_when_ideas_have_sex
6) Why The Best Leaders Make Love The Top Priority | Matt Tenney | Dec 5, 2019 https://www.youtube.com/watch?v=qCVoohdyI6I
VIDEO: https://youtu.be/ZH1xxmBNx5k
Connecta Event: Big Query och dataanalys med Google Cloud PlatformConnectaDigital
Avancerad dataanalys och ”big data” har under de senaste åren klättrat på trendlistorna och är nu ett av de mest prioriterade områdena i utvecklingen av nya tjänster och produkter för ledarföretag i det digitala landskapet.
Informationen som byggs upp i systemen när kundmötena digitaliseras har visat sig vara guld värt. Här finns allt vi behöver veta för att göra våra affärer mer effektiva.
Sedan sommaren 2013 har Connecta tillsammans med Google ett etablerat samarbete för att hjälpa våra kunder med övergången till moln-tjänster för bland annat avancerad dataanalys. För att göra oss själva redo att hjälpa våra kunder har vi under ett antal år utvecklat såväl kunskaper som skaffat oss erfarenheter kring Googles olika moln-produkter, som exempelvis ”Big Query”.
Big Query är ett molnbaserat analysverktyg och en del av Google Cloud Platform. Big Query gör det möjligt att ställa snabba frågor mot enorma dataset på bara någon sekund. Big Query och Google Cloud Platform erbjuder färdiga lösningar för att sätta upp och underhålla en infrastruktur som med enkla medel gör allt detta möjligt.
På Connecta Digital Consultings tredje event för våren introducerade vi våra kunder och partners i koncepten dataanalys och Big Query.
Under eventet berördes följande punkter:
- Big Data och Business Intelligence (BI)
- “The Google Big Data tools” – framgångsfaktorer och hur man kommer igång
- Google Cloud Platform och hur man genomför en framgångsrik molnsatsning
Vi presenterade case och berättade om viktiga lärdomar vi dragit i samarbetet med Google och våra kunder.
The challenges of every day life as the CTO of ClickMeter. Crafting and managing a "big data" ready infrastructure is no easy task, but it can be done step-by-step also by startups. The cloud is a cool starting ground which provides you with many of the toys you'll need, so you can focus on what part of "big data" provides you with the most value.
The Data Lake: Empowering Your Data Science TeamSenturus
Data science overview: defined, purpose, relation to BI, differences from BI and benefits from using both data science and BI. View the webinar video recording and download this deck: http://www.senturus.com/resources/data-lake-empowering-data-science-team/.
Learn how the data lake can empower data science teams and free up valuable data warehouse resources.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Optimizing Innovation: Modular Toolchains that Enable Digital TransformationsDevOps.com
Lean practices for software delivery are critical to digital transformation and innovation, and the failure to execute on them opens the door to disruption. Software investment and staffing decisions are made anecdotally, using static and stale slivers of data. But what if we could take an fMRI (Functional Magnetic Resonance Imaging) of the organization and see the flow of business value in real-time? See evidence of bottlenecks and use them to prioritize IT investment? Join us as we introduce the concept of Value Stream Networks and explain how to create a modular framework enabling end-to-end business value flow, at any scale.
Optimizing Innovation- Modular Toolchains that Enable Digital TransformationsTasktop
Lean practices for software delivery are critical to digital transformation and innovation, and the failure to execute on them opens the door to disruption. Software investment and staffing decisions are made anecdotally, using static and stale slivers of data. But what if we could take an fMRI (Functional Magnetic Resonance Imaging) of the organization and see the flow of business value in real-time? See evidence of bottlenecks and use them to prioritize IT investment? Join us as we introduce the concept of Value Stream Networks and explain how to create a modular framework enabling end-to-end business value flow, at any scale.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.