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TECH AT MAGNETIC
The motivation for this blog post is simple: I was having trouble searching Google for a simple formula for the confidence interval of lift. Lift is a very important metric in our industry, and after all the work I put into researching it I want to make sure the next person to google ‘confidence interval of lift’ has an easier time.TECH AT MAGNETIC
For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “ VW ”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go.. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which FINDING A CONFIDENCE INTERVAL FOR LIFT The motivation for this blog post is simple: I was having trouble searching Google for a simple formula for the confidence interval of lift. Lift is a very important metric in our industry, and after all the work I put into researching it I want to make sure the next person to google ‘confidence interval of lift’ has an easier time. DEMYSTIFYING LOGISTIC REGRESSION For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “VW”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which is ONE-PASS DISTRIBUTED RANDOM SAMPLING One of the important factors that affects efficiency of our predictive models is the recency of the model. The earlier our bidders get new version of prediction model, the better decisions they can make. Delays in producing the model result in lost money due to incorrect predictions. The slowest steps in our modeling pipeline are those that require manipulating the full data set — multiple VIRBS AND SAMPLING EVENTS FROM STREAMS VIRB (Variable Incoming Rate Biased) reservoir sampling is a streaming sampling algorithm that stores a representative fixed-sized sample of events from the recent past (the user specifies the desired mean age of samples), even when the incoming rate varies. It is heavily inspired by reservoir sampling. Motivation. When an ad exchange invites us to bid on an auction, our system automatically CLICK PREDICTION WITH VOWPAL WABBIT At the core of our automated campaign optimization algorithms lies a difficult problem: predicting the outcome of an event before it happens. With a good predictor, we can craft algorithms to maximize campaign performance, minimize campaign cost, or balance the two in some way. Without a good predictor, all we can do is hope for thebest.
GOOD TEST, BAD TEST
A good test suite is a developer’s best friend — it tells you what your code does and what it’s supposed to do. It’s your second set of eyes as you’re working, and your safety net before you go to production. By contrast, a bad test suite stands in the way of progress — whenever you make a small change, suddenly fifty tests are failing, and it’s not clear how or why the cases are BLOOM FILTER-ASSISTED JOINS WITH PYSPARK One of the most attractive features of Spark is the fine grained control of what you can broadcast to every executor with very simple code. When I first studied broadcast variables my thought process centered around map-side joins and other obvious candidates. I’ve since expanded my understanding of just how much flexibility broadcast variables can offer. REAL TIME FACIAL RECOGNITION IN PYTHON Strategy. After spending an hour or so researching computer vision libraries I’d have access to, I settled on OpenCV which appeared to have all of what I needed functionality-wise to accomplish my goal.. I even found that OpenCV ships with generated Python bindings, and that there are other projects which wrap OpenCV for Python in various ways .Each of them though used either ctypes or aTECH AT MAGNETIC
The motivation for this blog post is simple: I was having trouble searching Google for a simple formula for the confidence interval of lift. Lift is a very important metric in our industry, and after all the work I put into researching it I want to make sure the next person to google ‘confidence interval of lift’ has an easier time.TECH AT MAGNETIC
For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “ VW ”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go.. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which FINDING A CONFIDENCE INTERVAL FOR LIFT The motivation for this blog post is simple: I was having trouble searching Google for a simple formula for the confidence interval of lift. Lift is a very important metric in our industry, and after all the work I put into researching it I want to make sure the next person to google ‘confidence interval of lift’ has an easier time. DEMYSTIFYING LOGISTIC REGRESSION For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “VW”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which is ONE-PASS DISTRIBUTED RANDOM SAMPLING One of the important factors that affects efficiency of our predictive models is the recency of the model. The earlier our bidders get new version of prediction model, the better decisions they can make. Delays in producing the model result in lost money due to incorrect predictions. The slowest steps in our modeling pipeline are those that require manipulating the full data set — multiple VIRBS AND SAMPLING EVENTS FROM STREAMS VIRB (Variable Incoming Rate Biased) reservoir sampling is a streaming sampling algorithm that stores a representative fixed-sized sample of events from the recent past (the user specifies the desired mean age of samples), even when the incoming rate varies. It is heavily inspired by reservoir sampling. Motivation. When an ad exchange invites us to bid on an auction, our system automatically CLICK PREDICTION WITH VOWPAL WABBIT At the core of our automated campaign optimization algorithms lies a difficult problem: predicting the outcome of an event before it happens. With a good predictor, we can craft algorithms to maximize campaign performance, minimize campaign cost, or balance the two in some way. Without a good predictor, all we can do is hope for thebest.
GOOD TEST, BAD TEST
A good test suite is a developer’s best friend — it tells you what your code does and what it’s supposed to do. It’s your second set of eyes as you’re working, and your safety net before you go to production. By contrast, a bad test suite stands in the way of progress — whenever you make a small change, suddenly fifty tests are failing, and it’s not clear how or why the cases are BLOOM FILTER-ASSISTED JOINS WITH PYSPARK One of the most attractive features of Spark is the fine grained control of what you can broadcast to every executor with very simple code. When I first studied broadcast variables my thought process centered around map-side joins and other obvious candidates. I’ve since expanded my understanding of just how much flexibility broadcast variables can offer. REAL TIME FACIAL RECOGNITION IN PYTHON Strategy. After spending an hour or so researching computer vision libraries I’d have access to, I settled on OpenCV which appeared to have all of what I needed functionality-wise to accomplish my goal.. I even found that OpenCV ships with generated Python bindings, and that there are other projects which wrap OpenCV for Python in various ways .Each of them though used either ctypes or aTECH AT MAGNETIC
For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “ VW ”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go.. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which OPTIMIZE PYTHON WITH CLOSURES Magnetic’s real-time bidding system, written in pure Python, needs to keep up with a tremendous volume of incoming requests. On an ordinary weekday, our application handles about 300,000 requests per second at peak volumes, and responds in under 10 milliseconds. It should be obvious that at this scale optimizing the performance of the hottest sections of our code is of utmost importance. VIRBS AND SAMPLING EVENTS FROM STREAMS VIRB (Variable Incoming Rate Biased) reservoir sampling is a streaming sampling algorithm that stores a representative fixed-sized sample of events from the recent past (the user specifies the desired mean age of samples), even when the incoming rate varies. It is heavily inspired by reservoir sampling. Motivation. When an ad exchange invites us to bid on an auction, our system automaticallyGOOD TEST, BAD TEST
A good test suite is a developer’s best friend — it tells you what your code does and what it’s supposed to do. It’s your second set of eyes as you’re working, and your safety net before you go to production. By contrast, a bad test suite stands in the way of progress — whenever you make a small change, suddenly fifty tests are failing, and it’s not clear how or why the cases areTECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. EMBARRASSINGLY SERIAL The past decade has seen a surge in technologies around “big data,” claiming to make it easy to process large data sets quickly, or at least scalably, by distributing work across a cluster of machines. This is not a story of success with a big data framework. This is a story of a small data set suffering at the hands of big data assumptions, and a warning to developers to check what your REAL-TIME AD TARGETING WITH APACHE KAFKA Here at Magnetic, as a search-retargeting company, our core business model is to provide relevant ads to viewers. Our platform performs this task well, matching viewers up with related ads through various methods including page visits, search queries, and data analytics of each. It currently takes about 15 minutes on average for us to be able to react to new events in our core targeting DETECTING BRANDS IN USER SEARCH QUERIES Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. SKIP, THE SEARCH KEYWORD INTENT PREDICTOR Magnetic specializes in search retargeting, thus we really need to understand our users’ searches — it is our bread and butter. We need to recognize what a user’s search means in an understandable way for both humans and computers. This is why we map each search to a category (e.g. “Automotive”), brand (e.g. “BMW”), or otherintent data.
INSTALLING SPARK 1.5 ON CDH 5.4 If you have not tried processing data with Spark yet, you should. It’s the next happening framework, centered around processing data up to 100x more efficiently than Hadoop, while leveraging the existing Hadoop’s components (HDFS and YARN). Since Spark is evolving rapidly, in most cases you will want to run the latest released version by the Spark community, rather than the versionTECH AT MAGNETIC
Mon 09 May 2016 — Thomas Gauthier. At Magnetic we use logistic regression and Vowpal Wabbit in order to determine the probability of a given impression resulting in either a click or a conversion. In order to decide which variables to include in our models, we need objective metrics to determine if we are doing a good job.TECH AT MAGNETIC
For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “ VW ”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go.. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which DEMYSTIFYING LOGISTIC REGRESSION For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “VW”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which is VIRBS AND SAMPLING EVENTS FROM STREAMS VIRB (Variable Incoming Rate Biased) reservoir sampling is a streaming sampling algorithm that stores a representative fixed-sized sample of events from the recent past (the user specifies the desired mean age of samples), even when the incoming rate varies. It is heavily inspired by reservoir sampling. Motivation. When an ad exchange invites us to bid on an auction, our system automatically FINDING A CONFIDENCE INTERVAL FOR LIFT The motivation for this blog post is simple: I was having trouble searching Google for a simple formula for the confidence interval of lift. Lift is a very important metric in our industry, and after all the work I put into researching it I want to make sure the next person to google ‘confidence interval of lift’ has an easier time. EMBARRASSINGLY SERIAL The past decade has seen a surge in technologies around “big data,” claiming to make it easy to process large data sets quickly, or at least scalably, by distributing work across a cluster of machines. This is not a story of success with a big data framework. This is a story of a small data set suffering at the hands of big data assumptions, and a warning to developers to check what yourTECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. DETECTING BRANDS IN USER SEARCH QUERIES Detecting Brands in User Search Queries Michal Laclavík Magnetic Media Online 360 Park Ave S, 19th Floor New York, NY 10010 laclavik@magnetic.com BLOOM FILTER-ASSISTED JOINS WITH PYSPARK The first step is to generate a function that creates a bloom filter for all cookies seen in one partition. The capacity is initialized to the number of unique users expected in our 30-day page views dataset. The function returns a function pointer to a closure, which outputs just a single bloom filter based on the cookies seen in a given CLICK PREDICTION WITH VOWPAL WABBIT At the core of our automated campaign optimization algorithms lies a difficult problem: predicting the outcome of an event before it happens. With a good predictor, we can craft algorithms to maximize campaign performance, minimize campaign cost, or balance the two in some way. Without a good predictor, all we can do is hope for thebest.
TECH AT MAGNETIC
Mon 09 May 2016 — Thomas Gauthier. At Magnetic we use logistic regression and Vowpal Wabbit in order to determine the probability of a given impression resulting in either a click or a conversion. In order to decide which variables to include in our models, we need objective metrics to determine if we are doing a good job.TECH AT MAGNETIC
For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “ VW ”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go.. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which DEMYSTIFYING LOGISTIC REGRESSION For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “VW”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which is VIRBS AND SAMPLING EVENTS FROM STREAMS VIRB (Variable Incoming Rate Biased) reservoir sampling is a streaming sampling algorithm that stores a representative fixed-sized sample of events from the recent past (the user specifies the desired mean age of samples), even when the incoming rate varies. It is heavily inspired by reservoir sampling. Motivation. When an ad exchange invites us to bid on an auction, our system automatically FINDING A CONFIDENCE INTERVAL FOR LIFT The motivation for this blog post is simple: I was having trouble searching Google for a simple formula for the confidence interval of lift. Lift is a very important metric in our industry, and after all the work I put into researching it I want to make sure the next person to google ‘confidence interval of lift’ has an easier time. EMBARRASSINGLY SERIAL The past decade has seen a surge in technologies around “big data,” claiming to make it easy to process large data sets quickly, or at least scalably, by distributing work across a cluster of machines. This is not a story of success with a big data framework. This is a story of a small data set suffering at the hands of big data assumptions, and a warning to developers to check what yourTECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. DETECTING BRANDS IN USER SEARCH QUERIES Detecting Brands in User Search Queries Michal Laclavík Magnetic Media Online 360 Park Ave S, 19th Floor New York, NY 10010 laclavik@magnetic.com BLOOM FILTER-ASSISTED JOINS WITH PYSPARK The first step is to generate a function that creates a bloom filter for all cookies seen in one partition. The capacity is initialized to the number of unique users expected in our 30-day page views dataset. The function returns a function pointer to a closure, which outputs just a single bloom filter based on the cookies seen in a given CLICK PREDICTION WITH VOWPAL WABBIT At the core of our automated campaign optimization algorithms lies a difficult problem: predicting the outcome of an event before it happens. With a good predictor, we can craft algorithms to maximize campaign performance, minimize campaign cost, or balance the two in some way. Without a good predictor, all we can do is hope for thebest.
TECH AT MAGNETIC
For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “ VW ”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go.. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which THE MAGNETIC ENGINEERING MANIFESTO Creating a sustainable and consistent engineering culture means answering some fundamental questions: What do we believe in? How do we align all our ideas into a vision that is easy to understand? How do we turn that vision into something long-lived and actionable that can be used to drive our cultural growth? To address these questions, we recently released a Magnetic Engineering ManifestoTECH AT MAGNETIC
The motivation for this blog post is simple: I was having trouble searching Google for a simple formula for the confidence interval of lift. Lift is a very important metric in our industry, and after all the work I put into researching it I want to make sure the next personto
EMBARRASSINGLY SERIAL The past decade has seen a surge in technologies around “big data,” claiming to make it easy to process large data sets quickly, or at least scalably, by distributing work across a cluster of machines. This is not a story of success with a big data framework. This is a story of a small data set suffering at the hands of big data assumptions, and a warning to developers to check what your A SEARCH BASED APPROACH TO ENTITY RECOGNITION: MAGNETIC techniques from information retrieval, semantic web, infor-mation extraction and complex networks . IISAS moti-vation comes also from the VENIS project6, where we try to enhance the enterprise search with an entity centric ap-TECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs.GOOD TEST, BAD TEST
A good test suite is a developer’s best friend — it tells you what your code does and what it’s supposed to do. It’s your second set of eyes as you’re working, and your safety net before you go to production. By contrast, a bad test suite stands in the way of progress — whenever you make a small change, suddenly fifty tests are failing, and it’s not clear how or why the cases areTECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. REAL TIME FACIAL RECOGNITION IN PYTHON Strategy. After spending an hour or so researching computer vision libraries I’d have access to, I settled on OpenCV which appeared to have all of what I needed functionality-wise to accomplish my goal.. I even found that OpenCV ships with generated Python bindings, and that there are other projects which wrap OpenCV for Python in various ways .Each of them though used either ctypes or a DETECTING BRANDS IN USER SEARCH QUERIES Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs.TECH AT MAGNETIC
Mon 09 May 2016 — Thomas Gauthier. At Magnetic we use logistic regression and Vowpal Wabbit in order to determine the probability of a given impression resulting in either a click or a conversion. In order to decide which variables to include in our models, we need objective metrics to determine if we are doing a good job.TECH AT MAGNETIC
For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “ VW ”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go.. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which DEMYSTIFYING LOGISTIC REGRESSION For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “VW”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which isTECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs.TECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. CLICK PREDICTION WITH VOWPAL WABBIT At the core of our automated campaign optimization algorithms lies a difficult problem: predicting the outcome of an event before it happens. With a good predictor, we can craft algorithms to maximize campaign performance, minimize campaign cost, or balance the two in some way. Without a good predictor, all we can do is hope for thebest.
TECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. ONE-PASS DISTRIBUTED RANDOM SAMPLING One-Pass Distributed Random Sampling. One of the important factors that affects efficiency of our predictive models is the recency of the model. The earlier our bidders get new version of prediction model, the better decisions they can make. Delays in producing the model result in lost money due to incorrect predictions.GOOD TEST, BAD TEST
A good test suite is a developer’s best friend — it tells you what your code does and what it’s supposed to do. It’s your second set of eyes as you’re working, and your safety net before you go to production. By contrast, a bad test suite stands in the way of progress — whenever you make a small change, suddenly fifty tests are failing, and it’s not clear how or why the cases are FINDING A CONFIDENCE INTERVAL FOR LIFT The motivation for this blog post is simple: I was having trouble searching Google for a simple formula for the confidence interval of lift. Lift is a very important metric in our industry, and after all the work I put into researching it I want to make sure the next person to google ‘confidence interval of lift’ has an easier time.TECH AT MAGNETIC
Mon 09 May 2016 — Thomas Gauthier. At Magnetic we use logistic regression and Vowpal Wabbit in order to determine the probability of a given impression resulting in either a click or a conversion. In order to decide which variables to include in our models, we need objective metrics to determine if we are doing a good job.TECH AT MAGNETIC
For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “ VW ”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go.. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which DEMYSTIFYING LOGISTIC REGRESSION For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka “VW”) in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go. Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which isTECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs.TECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. CLICK PREDICTION WITH VOWPAL WABBIT At the core of our automated campaign optimization algorithms lies a difficult problem: predicting the outcome of an event before it happens. With a good predictor, we can craft algorithms to maximize campaign performance, minimize campaign cost, or balance the two in some way. Without a good predictor, all we can do is hope for thebest.
TECH AT MAGNETIC
Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. ONE-PASS DISTRIBUTED RANDOM SAMPLING One-Pass Distributed Random Sampling. One of the important factors that affects efficiency of our predictive models is the recency of the model. The earlier our bidders get new version of prediction model, the better decisions they can make. Delays in producing the model result in lost money due to incorrect predictions.GOOD TEST, BAD TEST
A good test suite is a developer’s best friend — it tells you what your code does and what it’s supposed to do. It’s your second set of eyes as you’re working, and your safety net before you go to production. By contrast, a bad test suite stands in the way of progress — whenever you make a small change, suddenly fifty tests are failing, and it’s not clear how or why the cases are FINDING A CONFIDENCE INTERVAL FOR LIFT The motivation for this blog post is simple: I was having trouble searching Google for a simple formula for the confidence interval of lift. Lift is a very important metric in our industry, and after all the work I put into researching it I want to make sure the next person to google ‘confidence interval of lift’ has an easier time. THE MAGNETIC ENGINEERING MANIFESTO Creating a sustainable and consistent engineering culture means answering some fundamental questions: What do we believe in? How do we align all our ideas into a vision that is easy to understand? How do we turn that vision into something long-lived and actionable that can be used to drive our cultural growth? To address these questions, we recently released a Magnetic Engineering Manifesto VIRBS AND SAMPLING EVENTS FROM STREAMS VIRB (Variable Incoming Rate Biased) reservoir sampling is a streaming sampling algorithm that stores a representative fixed-sized sample of events from the recent past (the user specifies the desired mean age of samples), even when the incoming rate varies. It is heavily inspired by reservoir sampling. Motivation. When an ad exchange invites us to bid on an auction, our system automatically DETECTING BRANDS IN USER SEARCH QUERIES Detecting Brands in User Search Queries Michal Laclavík Magnetic Media Online 360 Park Ave S, 19th Floor New York, NY 10010 laclavik@magnetic.com EMBARRASSINGLY SERIAL The past decade has seen a surge in technologies around “big data,” claiming to make it easy to process large data sets quickly, or at least scalably, by distributing work across a cluster of machines. This is not a story of success with a big data framework. This is a story of a small data set suffering at the hands of big data assumptions, and a warning to developers to check what your DETECTING BRANDS IN USER SEARCH QUERIES Capturing user intent with brands can be valuable, especially in online advertising. In the online advertising domain, brand detection can help capture user interests and improve user modeling, which, in turn, can lead to an increase in precision of user targeting with ads relevant to their interests and needs. A SEARCH BASED APPROACH TO ENTITY RECOGNITION: MAGNETIC techniques from information retrieval, semantic web, infor-mation extraction and complex networks . IISAS moti-vation comes also from the VENIS project6, where we try to enhance the enterprise search with an entity centric ap- REAL TIME FACIAL RECOGNITION IN PYTHON Strategy. After spending an hour or so researching computer vision libraries I’d have access to, I settled on OpenCV which appeared to have all of what I needed functionality-wise to accomplish my goal.. I even found that OpenCV ships with generated Python bindings, and that there are other projects which wrap OpenCV for Python in various ways .Each of them though used either ctypes or a REAL-TIME AD TARGETING WITH APACHE KAFKA Here at Magnetic, as a search-retargeting company, our core business model is to provide relevant ads to viewers. Our platform performs this task well, matching viewers up with related ads through various methods including page visits, search queries, and data analytics of each. It currently takes about 15 minutes on average for us to be able to react to new events in our core targeting SKIP, THE SEARCH KEYWORD INTENT PREDICTOR Magnetic specializes in search retargeting, thus we really need to understand our users’ searches — it is our bread and butter. We need to recognize what a user’s search means in an understandable way for both humans and computers. This is why we map each search to a category (e.g. “Automotive”), brand (e.g. “BMW”), or otherintent data.
INSTALLING SPARK 1.5 ON CDH 5.4 If you have not tried processing data with Spark yet, you should. It’s the next happening framework, centered around processing data up to 100x more efficiently than Hadoop, while leveraging the existing Hadoop’s components (HDFS and YARN). Since Spark is evolving rapidly, in most cases you will want to run the latest released version by the Spark community, rather than the version This site uses cookies to provide you with a more responsive and personalized service. By using this site you agree to our use of cookies. Please read our cookie noticefor
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Hux
The future of the connected human experience Drive hyper-growth by elevating the human experience at everyinteraction.
People are more than figures in a data column. They’re more dimensional than consumers. More holistic than patients. And more layered than citizens. Your audience is a collection of ever-evolving, think-for-themselves, live-life-to the-fullest, for lack of a better word: people. And people contain multitudes, just like your business. In order to connect with your audience, you need to be equipped to reach them on a more human level, through a more connected humanexperience.
Hux by Deloitte Digital helps you build those connections by integrating a wide variety of data, technology, and services across various touch points in the lives of your audience. And we give you complete ownership of it all. This is the future of the connected human experience.THIS IS HUX.
HUX BY DELOITTE DIGITALMAKING CONNECTIONS
The ability for brands to make individual, meaningful connections with customers on a human level has never been greater, while the demand from customers for these connections has never been higher. Key connections enable the personalized, contextual experiences across the customer journey, at scale. Hux can help you to connect:MEET HUX
Hux by Deloitte Digital integrates Deloitte know-how, IP, and creative resources to deliver on the promise of the elevated human experience, bringing the power of data science, human psychology, and content efficiently together. We often start with a customer data platform (CDP), which we set up under your ownership and ideally in your environment to capture all forms of data including experience and operational data. Building on this single view of the customer, Hux then enables orchestrated decisioning at every touchpoint and channel to optimize your desired business outcomes. Hux offerings help to take you from lead to loyalty. The offeringsinclude:
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