Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017
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Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention: PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll explain how we employ sophisticated machine learning tools – open source and in-house developed. I will also present results from experiments conducted on a very large graph data set containing millions of edges and vertices.
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Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Many ML Platforms cover data collection, feature engineering, training, deploying, productionalization, and monitoring but few, if any, do all of the above seamlessly.
Bighead aims to tie together various open source and in-house projects to remove incidental complexity from ML workflows. Bighead is built on Python and Spark and can be used in modular pieces as each ML problem presents unique challenges. Through standardization of the path to production, training environments and the methods for collecting and transforming data on Spark, each model is reproducible and iterable.
This talk covers the architecture, the problems that each individual component and the overall system aims to solve, and a vision for the future of machine learning infrastructure. It’s widely adapted in Airbnb and we have variety of models running in production. We have seen the overall model development time go down from many months to days on Bighead. We plan to open source Bighead to allow the wider community to benefit from our work.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & Deep Learning ...
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you use it? This talk will cover three of the most popular deep learning frameworks: TensorFlow, Keras, and Deep Learning Pipelines, and when, where, and how to use them.
We’ll also discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data), as well as help you answer questions such as:
– As a developer how do I pick the right deep learning framework for me?
– Do I want to develop my own model or should I employ an existing one
– How do I strike a trade-off between productivity and control through low-level APIs?
In this session, we will show you how easy it is to build an image classifier with Tensorflow, Keras, and Deep Learning Pipelines in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you, and perhaps with a better sense for how to fool an image classifier!
Kaz Sato, Evangelist, Google at MLconf ATL 2016
Machine Intelligence at Google Scale: Tensor Flow and Cloud Machine Learning: The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn’t scale for production service, so you need a Distributed Training on the cloud eventually. Google has been building infrastructure for training the large scale neural network on the cloud for years, and now started to share the technology with external developers. In this session, we will introduce new pre-trained ML services such as Cloud Vision API and Speech API that works without any training. Also, we will look how TensorFlow and Cloud Machine Learning will accelerate custom model training for 10x – 40x with Google’s distributed training infrastructure.
Auto-Pilot for Apache Spark Using Machine Learning
At Qubole, users run Spark at scale on cloud (900+ concurrent nodes). At such scale, for efficiently running SLA critical jobs, tuning Spark configurations is essential. But it continues to be a difficult undertaking, largely driven by trial and error. In this talk, we will address the problem of auto-tuning SQL workloads on Spark. The same technique can also be adapted for non-SQL Spark workloads. In our earlier work[1], we proposed a model based on simple rules and insights. It was simple yet effective at optimizing queries and finding the right instance types to run queries. However, with respect to auto tuning Spark configurations we saw scope of improvement. On exploration, we found previous works addressing auto-tuning using Machine learning techniques. One major drawback of the simple model[1] is that it cannot use multiple runs of query for improving recommendation, whereas the major drawback with Machine Learning techniques is that it lacks domain specific knowledge. Hence, we decided to combine both techniques. Our auto-tuner interacts with both models to arrive at good configurations. Once user selects a query to auto tune, the next configuration is computed from models and the query is run with it. Metrics from event log of the run is fed back to models to obtain next configuration. Auto-tuner will continue exploring good configurations until it meets the fixed budget specified by the user. We found that in practice, this method gives much better configurations compared to configurations chosen even by experts on real workload and converges soon to optimal configuration. In this talk, we will present a novel ML model technique and the way it was combined with our earlier approach. Results on real workload will be presented along with limitations and challenges in productionizing them. [1] Margoor et al,'Automatic Tuning of SQL-on-Hadoop Engines' 2018,IEEE CLOUD
Graph Gurus 15: Introducing TigerGraph 2.4
TigerGraph 2.4 includes:
-Pattern Matching
-Interpreted GSQL
-AWS S3 connectivity
-JDBC Driver
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
Instead of better understanding and optimizing their machine learning models, data scientists spend a majority of their time training and iterating through different models even in cases where there the data is reliable and clean. Important aspects of creating an ML model include (but are not limited to) data preparation, feature engineering, identifying the correct models, training (and continuing to train) and optimizing their models. This process can be (and often is) laborious and time-consuming.
In this session, we will explore this process and then show how the AutoML toolkit (from Databricks Labs) can significantly simplify and optimize machine learning. We will demonstrate all of this financial loan risk data with code snippets and notebooks that will be free to download.
Conversational AI with Transformer Models
With the advancements in Artificial Intelligence (AI) and cognitive technologies, automation has been a key prospect for many enterprises in various domains. Conversational AI is one such area where many organizations are heavily investing in.
In this session, we discuss the building blocks of conversational agents, Natural Language Understanding Engine with transformer models which have proven to offer state of the art results in standard NLP tasks.
We will first talk about the advantages of Transformer models over RNN/LSTM models and later talk about knowledge distillation and model compression techniques to make these parameter heavy models work in production environments with limited resources.
Key takeaways:
Understanding the building blocks & flow of Conversational Agents.
Advantages of Transformer based models over RNN/LSTMS
Knowledge distillation techniques
Different model compressions techniques including Quantization
Sample code in PyTorch & TF2
The Potential of GPU-driven High Performance Data Analytics in Spark
Spark Summit EU talk by Andy Steinbach
Using BigDL on Apache Spark to Improve the MLS Real Estate Search Experience ...
BigDL-enabled Deep Learning analysis of photos attached to property listings in Multiple Listings Services database allowed us to extract image features and identify similar-looking properties. We leveraged this information to in real-time property search application to improve the relevancy of user search results. Imagine identifying a property listing photo you like and having the system suggest other listings you should also review. Traditional real-estate MLS (multiple-listings services) search methods rely on SQL-type queries to search and serve real-estate listings results.
However, using BigDL in conjunction with MLSLinstings standard APIs allows users to include photos as search parameters in real-time, based both on image similarities and semantic feature search. The information extracted from listing’s images is used to improve the relevancy of the search results. To enable this use-case, we implemented several CNNs using BigDL framework on Microsoft’s Azure hosted Apache Spark: – Image feature extraction and tagging. Extracts features from real estate images and classifies them according to Real Estates Standards Organization rules, such as overall house style, interior and exterior attributes, etc. – Image similarity network which allows for comparing images that belong to different properties based on their extracted features and create a similarity score to be used in search results.
We’ll discuss the above networks in details as well as run a live demo of real-estate search results. Key takeaways: a) Why invest into Spark BigDL from the start. b) Why choose cloud-based solution from the start. c) Choice of Scala vs Python.
ML at the Edge: Building Your Production Pipeline with Apache Spark and Tens...
The explosion of data volume in the years to come challenge the idea of a centralized cloud infrastructure which handles all business needs. Edge computing comes to rescue by pushing the needs of computation and data analysis at the edge of the network, thus avoiding data exchange when makes sense. One of the areas where data exchange could impose a big overhead is scoring ML models especially where data to score are files like images eg. in a computer vision application.
Another concern in some applications, is that of keeping data as private as possible and this is where keeping things local makes sense. In this talk we will discuss current needs and recent advances in model serving, like newly introduced formats for pushing models at the edge nodes eg. mobile phones and how a unified model serving architecture could cover current and future needs for both data scientists and data engineers. This architecture is based among others, on training models in a distributed fashion with TensorFlow and leveraging Spark for cleaning data before training (eg. using TensorFlow connector).
Finally we will describe a microservice based approach for scoring models back at the cloud infrastructure side (where bandwidth can be high) eg. using TensorFlow serving and updating models remotely with a pull model approach for edge devices. We will talk also about implementing the proposed architecture and how that might look on a modern deployment environment eg. Kubernetes.
Application and Challenges of Streaming Analytics and Machine Learning on Mu...
Application and Challenges of Streaming Analytics and Machine Learning on Multi-Variate Time Series Data for Smart Manufacturing
Lessons Learned Replatforming A Large Machine Learning Application To Apache ...
Morningstar’s Risk Model project is created by stitching together statistical and machine learning models to produce risk and performance metrics for millions of financial securities. Previously, we were running a single version of this application, but needed to expand it to allow for customizations based on client demand. With the goal of running hundreds of custom Risk Model runs at once at an output size of around 1TB of data each, we had a challenging technical problem on our hands! In this presentation, we’ll talk about the challenges we faced replatforming this application to Spark, how we solved them, and the benefits we saw.
Some things we’ll touch on include how we created customized models, the architecture of our machine learning application, how we maintain an audit trail of data transformations (for rigorous third party audits), and how we validate the input data our model takes in and output data our model produces. We want the attendees to walk away with some key ideas of what worked for us when productizing a large scale machine learning platform.
Plume - A Code Property Graph Extraction and Analysis Library
See all on-demand Graph + AI Sessions: https://www.tigergraph.com/graph-ai-world-sessions/
Get TigerGraph: https://www.tigergraph.com/get-tigergraph/
Analytics Zoo: Building Analytics and AI Pipeline for Apache Spark and BigDL ...
A long time ago, there was Caffe and Theano, then came Torch and CNTK and Tensorflow, Keras and MXNet and Pytorch and Caffe2….a sea of Deep learning tools but none for Spark developers to dip into. Finally, there was BigDL, a deep learning library for Apache Spark. While BigDL is integrated into Spark and extends its capabilities to address the challenges of Big Data developers, will a library alone be enough to simplify and accelerate the deployment of ML/DL workloads on production clusters? From high level pipeline API support to feature transformers to pre-defined models and reference use cases, a rich repository of easy to use tools are now available with the ‘Analytics Zoo’. We’ll unpack the production challenges and opportunities with ML/DL on Spark and what the Zoo can do
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016
Big Data Processing Above and Beyond Hadoop: Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
SparkML: Easy ML Productization for Real-Time Bidding
dataxu bids on ads in real-time on behalf of its customers at the rate of 3 million requests a second and trains on past bids to optimize for future bids. Our system trains thousands of advertiser-specific models and runs multi-terabyte datasets. In this presentation we will share the lessons learned from our transition towards a fully automated Spark-based machine learning system and how this has drastically reduced the time to get a research idea into production. We'll also share how we: - continually ship models to production - train models in an unattended fashion with auto-tuning capabilities - tune and overbooked cluster resources for maximum performance - ported our previous ML solution into Spark - evaluate the performance of high-rate bidding models
Speakers: Maximo Gurmendez, Javier Buquet
Graph representation learning to prevent payment collusion fraud
PayPal is at the forefront of applying large-scale graph processing and machine learning algorithms to keep fraudsters at bay. While deep learning algorithms have found enormous success in the application to unstructured data, their application to structured data, such as graphs, is in the nascent stage of research. One of the emerging research area is graph-based representation learning where features are learned automatically from network structure.
In this talk, I'll present how this emerging area can help to automatically learn features from a large scale payment network. Learned features are used to build machine learning models that are used to prevent complex fraudster activities where they collude with several other actors to commit fraud. I'll elaborate on specific challenges in applying this state-of-the-art machine learning algorithm to large scale payment network. Results from experiments conducted on a very large graph data set containing billions of edges and vertices will be presented.
Speaker
Venkatesh Ramanathan, Data Scientist, Paypal
Hardware Accelerated Machine Learning Solution for Detecting Fraud and Money ...
See all on-demand Graph + AI Sessions: https://www.tigergraph.com/graph-ai-world-sessions/
Get TigerGraph: https://www.tigergraph.com/get-tigergraph/
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Apache Spark-Based Stratification Library for Machine Learning Use Cases at N...
Apache Spark-Based Stratification Library for Machine Learning Use Cases at N...
Sanjeev Satheesj, Research Scientist, Baidu at The AI Conference 2017
Sanjeev Satheesj, Research Scientist, Baidu at The AI Conference 2017
Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...
Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & Deep Learning ...
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & Deep Learning ...
Auto-Pilot for Apache Spark Using Machine Learning
Auto-Pilot for Apache Spark Using Machine Learning
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
The Potential of GPU-driven High Performance Data Analytics in Spark
The Potential of GPU-driven High Performance Data Analytics in Spark
Using BigDL on Apache Spark to Improve the MLS Real Estate Search Experience ...
Using BigDL on Apache Spark to Improve the MLS Real Estate Search Experience ...
ML at the Edge: Building Your Production Pipeline with Apache Spark and Tens...
ML at the Edge: Building Your Production Pipeline with Apache Spark and Tens...
Application and Challenges of Streaming Analytics and Machine Learning on Mu...
Application and Challenges of Streaming Analytics and Machine Learning on Mu...
Lessons Learned Replatforming A Large Machine Learning Application To Apache ...
Lessons Learned Replatforming A Large Machine Learning Application To Apache ...
Plume - A Code Property Graph Extraction and Analysis Library
Plume - A Code Property Graph Extraction and Analysis Library
Analytics Zoo: Building Analytics and AI Pipeline for Apache Spark and BigDL ...
Analytics Zoo: Building Analytics and AI Pipeline for Apache Spark and BigDL ...
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016
SparkML: Easy ML Productization for Real-Time Bidding
SparkML: Easy ML Productization for Real-Time Bidding
Graph representation learning to prevent payment collusion fraud
Graph representation learning to prevent payment collusion fraud
Hardware Accelerated Machine Learning Solution for Detecting Fraud and Money ...
Hardware Accelerated Machine Learning Solution for Detecting Fraud and Money ...
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Matineh Shaker, Artificial Intelligence Scientist, Bonsai at MLconf SF 2017
Deep Reinforcement Learning with Shallow Trees:
In this talk, I present Concept Network Reinforcement Learning (CNRL), developed at Bonsai. It is an industrially applicable approach to solving complex tasks using reinforcement learning, which facilitates problem decomposition, allows component reuse, and simplifies reward functions. Inspired by Sutton’s options framework, we introduce the notion of “Concept Networks” which are tree-like structures in which leaves are “sub-concepts” (sub-tasks), representing policies on a subset of state space. The parent (non-leaf) nodes are “Selectors”, containing policies on which sub-concept to choose from the child nodes, at each time during an episode. There will be a high-level overview on the reinforcement learning fundamentals at the beginning of the talk.
Bio: Matineh Shaker is an Artificial Intelligence Scientist at Bonsai in Berkeley, CA, where she builds machine learning, reinforcement learning, and deep learning tools and algorithms for general purpose intelligent systems. She was previously a Machine Learning Researcher at Geometric Intelligence, Data Science Fellow at Insight Data Science, Predoctoral Fellow at Harvard Medical School. She received her PhD from Northeastern University with a dissertation in geometry-inspired manifold learning.
LN Renganarayana, Architect, ML Platform and Services and Madhura Dudhgaonkar...
Lessons Learnt from building ML Products for enterprise SaaS:
Having spent the last 4+ years productizing ML powered enterprise products, we have learnt a lot! Join us to hear the stories of our stumbles (ahem learnings) in applying machine learning to solve business problems for Fortune 500 companies. Our hands-on experience has shaped our product strategy, ML platform design and organization’s operational principles. And the investments we made based on our learnings have helped us drastically improve our time to market for ML products. Come on by to hear the technical and organizational challenges (and some solutions) in building ML products for enterprise SaaS. Hopefully our learnings will be useful in your journey.
Bio: LN leads the architecture and design of Workday’s ML Platform and Services. He is all about building large scale distributed systems and data platforms. Currently his days (and some nights) are spent on solving the challenges in building ML products for Enterprise SaaS. LN’s career spans across HP, IBM Research, Symantec and now Workday. At Symantec, he was the architect and lead of a streaming platform that ingested and processed 2+ billions of events per day. As a Research Staff Member at IBM T.J. Watson Research Center, LN built optimizations for automatic parallelization, techniques for approximate computing, deployment automation for OpenStack, and analytics for large scale cloud services.
LN holds a Ph.D. in Computer Science from Colorado State University and has published more than 40 technical publications / patents. His work has received awards from ACM, IBM, and HP.
Bio:Madhura Dudhgaonkar is responsible for leading Workday’s search, data science and machine learning teams based in San Francisco. Her teams have spent ~4 years building machine learning products used by Fortune 500 companies. Her experience ranges from being a hands-on engineer to leading large engineering organizations. Madhura’s career spans across SUN Microsystems, Adobe and now Workday. During her career, she has been involved with building a variety of products – from developing Java Language to building a version 1.0 consumer product to building enterprise SaaS products.
She holds a bachelor’s degree in electronics and telecommunications and a master’s degree in math and computer science.
Madhura is originally from a small town in India and came to the United States to pursue her passion in technology. She currently calls San Francisco home, and despite nine years here, can’t get enough of its hilly charm, the diversity of people, culture, and experiences.
Jessica Rudd, PhD Student, Analytics and Data Science, Kennesaw State Univers...
Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification:
Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive information for disease modeling. In this study, we combine simulated social network metrics with SVM to predict diabetes in a sample of data from the Behavioral Risk Factor Surveillance System. In this dataset, Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models with and without graph metrics, respectively. SVM with a polynomial kernel had ROC index of 72.9 and 75.6 for models with and without graph metrics, respectively. Although this did not perform as well as Logistic Regression, the results are consistent with previous studies utilizing SVM to classify diabetes.
Jonas Schneider, Head of Engineering for Robotics, OpenAI
Machine Learning Systems at Scale:
OpenAI is a non-profit research company, discovering and enacting the path to safe artificial general intelligence. As part of our work, we regularly push the limits of scalability in cutting-edge ML algorithms. We’ve found that in many cases, designing the systems we build around the core algorithms is as important as designing the algorithms themselves. This means that many systems engineering areas, such as distributed computing, networking, and orchestration, are crucial for machine learning to succeed on large problems requiring thousands of computers. As a result, at OpenAI engineers and researchers work closely together to build these large systems as opposed to a strict researcher/engineer split. In this talk, we will go over some of the lessons we’ve learned, and how they come together in the design and internals of our system for learning-based robotics research.
Bio: Jonas leads technology development for OpenAI’s robotics group, developing methods to apply machine learning and AI to robots. He also helped build the infrastructure to scale OpenAI’s distributed ML systems to thousands of machines.
Alexandra Johnson, Software Engineer, SigOpt at MLconf ATL 2017
Best Practices for Hyperparameter Optimization:
All machine learning and artificial intelligence pipelines – from reinforcement agents to deep neural nets – have tunable hyperparameters. Optimizing these hyperparameters provides tremendous performance gains, but only if the optimization is done correctly. This presentation will discuss topics including selecting performance criteria, why you should always use cross validation, and choosing between state of the art optimization methods.
Xavier Amatriain, Cofounder & CTO, Curai at MLconf SF 2017
ML to Cure the World:
The practice of medicine involves diagnosis, treatment, and prevention of diseases. Recent technological breakthroughs have made little dent to the centuries-old system of practicing medicine: complex diagnostic decisions are still mostly dependent on “educated” work-ups of the doctors, and rely on somewhat outdated tools and incomplete data. All of this often leads to imperfect, biased, and, at times, incorrect diagnosis and treatment.
With a growing research community as well as tech companies working on AI advances to medicine, the hope for healthcare renaissance is definitely not lost. The emphasis of this talk will be on ML-driven medicine. We will discuss recent AI advancements for aiding medical decision including language understanding, medical knowledge base construction and diagnosis systems. We will discuss the importance of personalized medicine that takes into account not only the user, but also the context, and other metadata. We will also highlight challenges in designing ML-based medical systems that are accurate, but at the same time engaging and trustworthy for the user.
Bio: Xavier Amatriain is currently co-founder and CTO of Curai, a stealth startup trying to radically improve healthcare for patients by using AI. Previous to this, he was VP of Engineering at Quora, and Research/engineering Director at Netflix, where he led the team building the famous Netflix recommendation algorithms. Before going into leadership positions in industry, Xavier was a research scientist at Telefonica Research and a research director at UCSB. With over 50 publications (and 3k+ citations) in different fields, Xavier is best known for his work on machine learning in general and recommender systems in particular. He has lectured at different universities both in the US and Spain and is frequently invited as a speaker at conferences and companies.
Daniel Shank, Data Scientist, Talla at MLconf SF 2017
Getting Value Out of Chat Data:
Chat-based interfaces are increasingly common, whether as customers interacting with companies or as employees communicating with each other within an organization. Given the large number of chat logs being captured, along with recent advances in natural language processing, there is a desire to leverage this data for both insight generation and machine learning applications. Unfortunately, chat data is user-generated data, meaning it is often noisy and difficult to normalize. It is also mostly short texts and heavily context-dependent, which cause difficulty in applying methods such as topic modeling and information extraction.
Despite these challenges, it is still possible to extract useful information from these data sources. In this talk, I will be providing an overview of techniques and practices for working with chat-based user interaction data with a focus on machine-augmented data annotation and unsupervised learning methods.
Bio: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
Ashfaq Munshi, ML7 Fellow, Pepperdata
Classifying Multi-Variate Time Series at Scale:
Characterizing and understanding the runtime behavior of large scale Big Data production systems is extremely important. Typical systems consist of hundreds to thousands of machines in a cluster with hundreds of terabytes of storage costing millions of dollars, solving problems that are business critical. By instrumenting each running process, and measuring their resource utilization including CPU, Memory, I/O, network etc., as time series it is possible to understand and characterize the workload on these massive clusters. Each time series is a series consisting of tens to tens of thousands of data points that must be ingested and then classified. At Pepperdata, our instrumentation of the clusters collects over three hundred metrics from each task every five seconds resulting in millions of data points per hour. At this scale the data are equivalent to the biggest IOT data sets in the world. Our objective is to classify the collection of time series into a set of classes that represent different work load types. Or phrased differently, our problem is essentially the problem of classifying multivariate time series.
In this talk, we propose a unique, off-the-shelf approach to classifying time series that achieves near best-in-class accuracy for univariate series and generalizes to multivariate time series. Our technique maps each time series to a Grammian Angular Difference Field (GADF), interprets that as an image, uses Google’s pre-trained CNN (trained on Inception v3) to map the GADF images into a 2048-dimensional vector space and then uses a small MLP with two hidden layers, with fifty nodes in each layer, and a softmax output to achieve the final classification. Our work is not domain specific – a fact proven by our achieving competitive accuracies with published results on the univariate UCR data set as well as the multivariate UCI data set.
Bio: Before joining Pepperdata, Ash was executive chairman for Marianas Labs, a deep learning startup sold in December 2015. Prior to that he was CEO for Graphite Systems, a big data storage startup that was sold to EMC DSSD in August 2015. Munshi also served as CTO of Yahoo, as a CEO of both public and private companies, and is on the board of several technology startups.
Dr. Steve Liu, Chief Scientist, Tinder at MLconf SF 2017
Personalized User Recommendations at Tinder: The TinVec Approach:
With 26 million matches per day and more than 20 billion matches made to date, Tinder is the world’s most popular app for meeting new people. Our users swipe for a variety of purposes, like dating to find love, expanding social networks and meeting locals when traveling.
Recommendation is an important service behind-the-scenes at Tinder, and a good recommendation system needs to be personalized to meet an individual user’s preferences. In this talk, we will discuss a new personalized recommendation approach being developed at Tinder, called TinVec. TinVec embeds users’ preferences into vectors leveraging on the large amount of swipes by Tinder users. We will discuss the design, implementation, and evaluation of TinVec as well as its application to
personalized recommendations.
Bio: Dr. Steve Liu is chief scientist at Tinder. In his role, he leads research innovation and applies novel technologies to new product developments.
He is currently a professor and William Dawson Scholar at McGill University School of Computer Science. He has also served as a visiting research scientist at HP Labs. Dr. Liu has published more than 280 research papers in peer-reviewed international journals and conference proceedings. He has also authored and co-authored several books. Over the course of his career, his research has focused on big data, machine learning/AI, computing systems and networking, Internet of Things, and more. His research has been referenced in articles publishing across The New York Times, IDG/Computer World, The Register, Business Insider, Huffington Post, CBC, NewScientist, MIT Technology Review, McGill Daily and others. He is a recipient of the Outstanding Young Canadian Computer Science Researcher Prizes from the Canadian Association of Computer Science and is a recipient of the Tomlinson Scientist Award from McGill University.
He is serving or has served on the editorial boards of ACM Transactions on Cyber-Physical Systems (TCPS), IEEE/ACM Transactions on Networking (ToN), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Vehicular Technology (TVT), and IEEE Communications Surveys and Tutorials (COMST). He has also served on the organizing committees of more than 38 major international conferences and workshops.
Dr. Liu received his Ph.D. in Computer Science with multiple honors from the University of Illinois at Urbana-Champaign. He received his Master’s degree in Automation and BSc degree in Mathematics from Tsinghua University.
Doug Eck, Research Scientist, Google Magenta, at MLconf SF 2017
The Role of AI and Machine Learning in Creativity:
I’ll discuss Magenta, a Google Brain project investigating music and art generation using deep learning and reinforcement learning. I’ll describe the goals of Magenta and how it fits into the general trend of AI moving into our daily lives. One crucial question is: Where does AI and Machine Learning fit in the creative process? I’ll argue that it’s about augmenting and extending the artist rather than just creating artifacts (songs, paintings, etc.) with machines. I’ll talk about two recent projects. In the first, we explore the use of recurrent neural networks to extend musical phrases in different ways. In the second we look at teaching a neural network to draw with strokes. This will be a high-level overview talk with no need for knowledge of AI or Machine Learning.
Bio:Doug leads Magenta, a Google Brain project working to generate music, video, image and text using deep learning and reinforcement learning. A main goal of Magenta is to better understanding how AI can enable artists and musicians to express themselves in innovative new ways. Before Magenta, Doug led the Google Play Music search and recommendation team. From 2003 to 2010 Doug was faculty at the University of Montreal’s MILA Machine Learning lab, where he worked on expressive music performance and automatic tagging of music audio.
Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017
Machine Learning Based Attack Vector Modeling for CyberSecurity:
Connections have behavioural patterns that are unique to protocols, loads, window sizes, bandwidth, and mainly the type of traffic. A CDN enterprise behaves completely differently than how a Cloud service company would behave and they both would be different from a corporation. This also means that attack vectors and attack landscapes are different in all these places. In this talk we speak about modeling different kinds of attacks and build a model that is able to identify these different kinds of attacks using ML.
The method we use is to identify different profiles based on many variables that specifically but robustly identify attacks of different kinds. The variables are specific to business, network profile, traffic. The variables are also high-level i.e. aggregate, and packet-level. This way the models are specifically picking up on constant variations in traffic, and create machine learning models to identify these attacks. Using the power of H2O these analyses are not just limited to a research and analysis of the traffic and concluding with a “OH, this was what it was.” moment but to actually deploy code, besides existing IDS and IPS, or deploying highly optimized, independent programs that can handle high thruputs at the rate of 1.2 Million decisions per second making it one of the fastest implementations of ML to identify, defend and protect critical infrastructure that are potentially under threat.
Ryan West, Machine Learning Engineer, Nexosis at MLconf ATL 2017
Codifying Data Science Intuition: Using Decision Theory to Automate Time Series Model Selection:
While models generated from cross-sectional data can utilize cross-validation for model selection, most time series models cannot be cross-validated due to the temporal structure of the data used to create them. It is possible to employ a rolling cross-validation technique, however this process is computationally expensive and provides no indication of the long-term forecast accuracies of the models.
The purpose of this talk is to elaborate how decision theory can be used to automate time series model selection in order to streamline the manual process of validation and testing. By creating consecutive, temporally independent holdout sets, performance metrics for each model’s prediction on each holdout set are fed into a decision function to select an unbiased model. The decision function helps minimize the poorest performance of each model across all holdout sets in order to counteract the possibility of choosing a model that overfits or underfits the holdout sets. Not only does this process improve forecast accuracy, but it also reduces computation time by only requiring the creation of a fixed number of proposed forecasting models.
Tamara G. Kolda, Distinguished Member of Technical Staff, Sandia National Lab...
Tensor Decomposition: A Mathematical Tool for Data Analysis:
Tensors are multiway arrays, and tensor decompositions are powerful tools for data analysis. In this talk, we demonstrate the wide-ranging utility of the canonical polyadic (CP) tensor decomposition with examples in neuroscience and chemical detection. The CP model is extremely useful for interpretation, as we show with an example in neuroscience. However, it can be difficult to fit to real data for a variety of reasons. We present a novel randomized method for fitting the CP decomposition to dense data that is more scalable and robust than the standard techniques. We further consider the modeling assumptions for fitting tensor decompositions to data and explain alternative strategies for different statistical scenarios, resulting in a _generalized_ CP tensor decomposition.
Bio: Tamara G. Kolda is a member of the Data Science and Cyber Analytics Department at Sandia National Laboratories in Livermore, CA. Her research is generally in the area of computational science and data analysis, with specialties in multilinear algebra and tensor decompositions, graph models and algorithms, data mining, optimization, nonlinear solvers, parallel computing and the design of scientific software. She has received a Presidential Early Career Award for Scientists and Engineers (PECASE), been named a Distinguished Scientist of the Association for Computing Machinery (ACM) and a Fellow of the Society for Industrial and Applied Mathematics (SIAM). She was the winner of an R&D100 award and three best paper prizes at international conferences. She is currently a member of the SIAM Board of Trustees and serves as associate editor for both the SIAM J. Scientific Computing and the SIAM J. Matrix Analysis and Applications.
Rushin Shah, Engineering Manager, Facebook at MLconf SF 2017
Natural Language Understanding @ Facebook Scale:
At Facebook, text understanding is the key to surfacing content that’s relevant and personalized, plus enabling new experiences like social recommendations and Marketplace suggestions. In this talk, I will introduce you to DeepText, Facebook’s platform for text understanding, and discuss the various models it supports.
Bio: Rushin Shah is an engineering leader at Facebook, currently working on natural language understanding and dialog. Previously, he was at Siri at Apple for 5 years, where he built and headed the natural language understanding group, which included teams dedicated to modeling, engineering and data science. He also worked at the query understanding group at Yahoo. He has worked on a broad range of problems in the NLP area including parsing, information extraction, dialog and question answering. He holds degrees in language technologies and computer science from Carnegie Mellon and IIT Kharagpur.
Michael Alcorn, Sr. Software Engineer, Red Hat Inc. at MLconf SF 2017
Representation Learning @ Red Hat:
For many companies, the vast majority of their data is unstructured and unlabeled; however, the data often contains information that could be useful in a variety of scenarios. Representation learning is the process of extracting meaningful features from unlabeled data so that it can be used in other tasks. In this talk, you’ll hear about how Red Hat is using deep learning to discover meaningful entity representations in a number of different settings, including: (1) identifying duplicate documents on the Customer Portal, (2) finding contextually similar URLs with word2vec, and (3) clustering behaviorally similar customers with doc2vec. To close, we will walk through an example demonstrating how representation learning can be applied to Major League Baseball players.
Bio: Michael first developed his data crunching chops as an undergraduate at Auburn University (War Eagle!) where he used a number of different statistical techniques to investigate various aspects of salamander biology (work that led to several publications). He then went on to earn a M.S. in evolutionary biology from The University of Chicago (where he wrote a thesis on frog ecomorphology) before changing directions and earning a second M.S. in computer science (with a focus on intelligent systems) from The University of Texas at Dallas. As a Machine Learning Engineer – Information Retrieval at Red Hat, Michael is constantly looking for ways to use the latest and greatest machine learning technology to improve search.
Dr. June Andrews, Principal Data Scientist, Wise.io, From GE Digital at MLcon...
Counter Intuitive Machine Learning for the Industrial Internet of Things:
The Industrial Internet of Things (IIoT) is the infrastructure and data flow built around the world’s most valuable things like airplane engines, medical scanners, nuclear power plants, and oil pipelines. These machines and systems require far greater uptime, security, governance, and regulation than the IoT landscape based around consumer activity. In the IIoT the cost of being wrong can be the catastrophic loss of life on a massive scale. Nevertheless, given the growing scale through the digitalization of industrial assets, there is clearly a growing role for machine learning to help augment and automate human decision making. It is against this backdrop that traditional machine learning techniques must be adapted and need based innovations created. We see industrial machine learning as distinct from consumer machine learning and in this talk we will cover the counterintuitive changes of featurization, metrics for model performance, and human-in-the-loop design changes for using machine learning in an industrial environment.
Bio: June Andrews is a Principal Data Scientist at Wise.io, From GE Digital working on a machine learning and data science platform for the Industrial Internet of Things, which includes aviation, trains, and power plants. Previously, she worked at Pinterest spearheading the Data Trustworthiness and Signals Program to create a healthy data ecosystem for machine learning. She has also lead efforts at LinkedIn on growth, engagement, and social network analysis to increase economic opportunity for professionals. June holds degrees in applied mathematics, computer science, and electrical engineering from UC Berkeley and Cornell.
Anima Anadkumar, Principal Scientist, Amazon Web Services, Endowed Professor,...
Large-scale Machine Learning: Deep, Distributed and Multi-Dimensional:
Modern machine learning involves deep neural network architectures which yields state-of-art performance on multiple domains such as computer vision, natural language processing and speech recognition. As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. Apache MXNet is an open-source framework developed for distributed deep learning. I will describe the underlying lightweight hierarchical parameter server architecture that results in high efficiency in distributed settings.
Pushing the current boundaries of deep learning requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. We present new deep learning architectures that preserve the multi-dimensional information in data end-to-end. We show that tensor contractions and regression layers are an effective replacement for fully connected layers in deep learning architectures. They result in significant space savings with negligible performance degradation. These functionalities are available in the Tensorly package with MXNet backend interface for large-scale efficient learning.
Bio: Anima Anandkumar is a principal scientist at Amazon Web Services and a Bren professor at Caltech CMS department. Her research interests are in the areas of large-scale machine learning, non-convex optimization and high-dimensional statistics. In particular, she has been spearheading the development and analysis of tensor algorithms. She is the recipient of several awards such as the Alfred. P. Sloan Fellowship, Microsoft Faculty Fellowship, Google research award, ARO and AFOSR Young Investigator Awards, NSF Career Award, Early Career Excellence in Research Award at UCI, Best Thesis Award from the ACM Sigmetrics society, IBM Fran Allen PhD fellowship, and several best paper awards. She has been featured in a number of forums such as the yourstory, Quora ML session, O’Reilly media, and so on. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, an assistant professor at U.C. Irvine between 2010 and 2016, and a visiting researcher at Microsoft Research New England in 2012 and 2014.
Talha Obaid, Email Security, Symantec at MLconf ATL 2017
A Machine Learning approach for detecting a Malware:
The project is to improve the way we detect script based malware using Machine Learning. Malware has become one of the most active channel to deliver threats like Banking Trojans and Ransomware. The talk is aimed at finding a new and effective way to detect the malware. We started with acquiring both malicious and clean samples. Later we performed feature identification, while building on top of existing knowledge base of malware. Then we performed automated feature extraction. After certain feature set is obtained, we teased-out feature which are categorical, interdependent or composite. We applied varying machine learning models, producing both binary and categorical outcomes. We cross validated our results and re-tuned our feature set and our model, until we obtained satisfying results, with least false-positives. We concluded that not all the extracted features are significant, in fact some features are detrimental on the model performance. Once such features are factored-out, it results not only in better match, but also provides a significant gain in performance.
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Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017
Ryan West, Machine Learning Engineer, Nexosis at MLconf ATL 2017
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Tamara G. Kolda, Distinguished Member of Technical Staff, Sandia National Lab...
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Rushin Shah, Engineering Manager, Facebook at MLconf SF 2017
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Michael Alcorn, Sr. Software Engineer, Red Hat Inc. at MLconf SF 2017
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Big Stream Processing Systems, Big Graphs
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.
This episode of our series will be focussed on processing systems capable of dealing with Big Streams. But analysing data lacking graphical representation will not be very convenient for us. And this is where we have to use a platform capable of visualising Big Graphs. All these topics will be covered in today’s presentation.
#CHEDTEB
www.chedteb.eu
High Performance Spatial-Temporal Trajectory Analysis with Spark
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Neo4j GraphTour New York_EY Presentation_Michael Moore
Presentation from GraphTour New York 2019 held on October 16 - Roadmap for Enterprise Strategy presented by Michael Moore
An Introduction to Apache Ignite - Mandhir Gidda - Codemotion Rome 2017
Apache Ignite is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies.
Your Roadmap for An Enterprise Graph Strategy
Speaker: Michael Moore, Ph.D., Executive Director, Knowledge Graphs + AI, EY National Advisory
Abstract: Knowledge graphs have enormous potential for delivering superior customer experiences, advanced analytics and efficient data management.
Learn valuable tips from a leading practitioner on how to position, organize and implement your first enterprise graph project.
CEP - simplified streaming architecture - Strata Singapore 2016
We describe an application of CEP using a microservice-based streaming architecture. We use Drools business rule engine to apply rules in real time to an event stream from IoT traffic sensor data.
The Impact of SMACT on the Data Management Stack
This presentation introduces the concept of the "Integrator's Dilemma" and reviews some of the challenges faced by traditional data and application integration technologies when it comes to keeping up with the new enterprise data, application and API connectivity and management requirements. We review the landscape and share examples of the steps more and more IT organizations are taking to improve business alignment through faster access to trusted data.
To learn more, visit http://www.snaplogic.com/ipaas
Hadoop Master Class : A concise overview
A concise, yet simple and substantial overview of Hadoop, Mahout, Hive, Pig & HBase.
In memory computing principles by Mac Moore of GridGain
In the presentation, we will provide an overview of general in-memory computing principles and the drivers behind it. We will start with a summary of the technical drivers (abundant hardware resources) and market forces (the rise of Big Data). We will cover popular and emerging use cases for in-memory computing, from financial industry trading platforms to mobile payment processing, online advertising, online/mobile gaming back-ends and more. We will then present some foundational concepts and terminology, and discuss considerations around any in-memory solution. From there, we will illustrate how a complete in-memory computing stack like GridGain combines clustering, high performance computing, in-memory data grids, stream processing and Hadoop acceleration into one unified and easy to use platform.
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)
(updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics:
• What the basic components of a DV model are
• How to build, and design structures incrementally, without constant refactoring
Advanced Analytics in Hadoop
On June 11 Thomas Dinsmore gave a nice outline on tools and technologies that are out there handling analytics in Hadoop. It is a must watch for anyone looking for what advance analytics Hadoop could deliver.
Please find video and slides below.
Synopsis
What is the state of play for advanced analytics in Hadoop? A year ago, options included "roll your own" and little else; today there are a number of serious open source and commercial options available, with new capabilities announced daily.
In this presentation, we begin with a brief overview of use cases for advanced analytics and a discussion of what types of analytics must run in Hadoop. We continue with an overview of available architectures. The presentation concludes with a hype-free survey of available open source and commercial software for advanced analytics in Hadoop.
Bio
Thomas W. Dinsmore is Director of Product Management for Revolution Analytics, a company that provides commercial support and services for open source R. In this role, Mr. Dinsmore closely tracks the market for commercial and open source software on all platforms, including Hadoop. Prior to joining Revolution Analytics, Mr. Dinsmore served as an Analytics Solution Architect for IBM Big Data, and as a Principal Consultant for Razorfish and SAS.
Mr. Dinsmore has hands-on experience with leading commercial and open source tools for advanced analytics, including SAS, SPSS, R, Oracle Data Mining across a range of platforms, including Hadoop, Netezza, Teradata and Oracle. He is certified in SAS 9.
In his career, Mr. Dinsmore has worked with more than 500 enterprises in the United States, Canada, Mexico, Venezuela, Chile, Brazil, the United Kingdom, Belgium, Italy, Turkey, Israel, Malaysia and Singapore.
Resume quaish abuzer
Informatica Developer having 5.8 years of Experience with Oracle,Teradata,Unix,Sql Server.
Also having good exposure in Big data Hadoop.
Roadmap for Enterprise Graph Strategy
Speaker: Michael Moore, Executive Director, Knowledge Graphs + AI at EY
Making Hadoop Realtime by Dr. William Bain of Scaleout Software
Hadoop has been widely embraced for its ability to economically store and analyze large data sets. Using parallel computing techniques like MapReduce, Hadoop can reduce long computation times to hours or minutes. This works well for mining large volumes of historical data stored on disk, but it is not suitable for gaining real-time insights from live operational data. Still, the idea of using Hadoop for real-time data analytics on live data is appealing because it leverages existing programming skills and infrastructure – and the parallel architecture of Hadoop itself. This presentation will describe how real-time analytics using Hadoop can be performed by combining an in-memory data grid (IMDG) with an integrated, stand-alone Hadoop MapReduce execution engine. This new technology delivers fast results for live data and also accelerates the analysis of large, static data sets.
Srikanth hadoop hyderabad_3.4yeras - copy
Srikanth K - Hadoop Developer, ADP india Pvt Ltd from Alwasi Software Pvt Ltd, 3.4 yrs, Hyderabad / Secunderabad
An Introduction to Graph: Database, Analytics, and Cloud Services
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
Getting Started with Apache Ignite as a Distributed Database
In-Memory Computing Meetup Vol.1, Tokyo, May 23
Similar to Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017 (20)
High Performance Spatial-Temporal Trajectory Analysis with Spark
High Performance Spatial-Temporal Trajectory Analysis with Spark
Danny Bickson - Python based predictive analytics with GraphLab Create
Danny Bickson - Python based predictive analytics with GraphLab Create
Neo4j GraphTour New York_EY Presentation_Michael Moore
Neo4j GraphTour New York_EY Presentation_Michael Moore
An Introduction to Apache Ignite - Mandhir Gidda - Codemotion Rome 2017
An Introduction to Apache Ignite - Mandhir Gidda - Codemotion Rome 2017
CEP - simplified streaming architecture - Strata Singapore 2016
CEP - simplified streaming architecture - Strata Singapore 2016
Evolving Beyond the Data Lake: A Story of Wind and Rain
Evolving Beyond the Data Lake: A Story of Wind and Rain
In memory computing principles by Mac Moore of GridGain
In memory computing principles by Mac Moore of GridGain
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)
Making Hadoop Realtime by Dr. William Bain of Scaleout Software
Making Hadoop Realtime by Dr. William Bain of Scaleout Software
An Introduction to Graph: Database, Analytics, and Cloud Services
An Introduction to Graph: Database, Analytics, and Cloud Services
Getting Started with Apache Ignite as a Distributed Database
Getting Started with Apache Ignite as a Distributed Database
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Understanding Human Impact: Social and Equity Assessments for AI Technologies
Social and Equity Impact Assessments have broad applications but can be a useful tool to explore and mitigate for Machine Learning fairness issues and can be applied to product specific questions as a way to generate insights and learnings about users, as well as impacts on society broadly as a result of the deployment of new and emerging technologies.
In this presentation, my goal is to advocate for and highlight the need to consult community and external stakeholder engagement to develop a new knowledge base and understanding of the human and social consequences of algorithmic decision making and to introduce principles, methods and process for these types of impact assessments.
Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding
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Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity.
In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.
Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...
Applying Computer Vision to Reduce Contamination in the Recycling Stream
With China’s recent refusal of most foreign recyclables, North American waste haulers are scrambling to figure out how to make on-shore recycling cost-effective in order to continue providing recycling services. Recyclables that were once being shipped to China for manual sorting are now primarily being redirected to landfills or incinerators. Without a solution, a nearly $5 billion annual recycling market could come to a halt.
Purity in the recycling stream is key to this effort as contaminants in the stream can increase the cost of operations, damage equipment and reduce the ability to create pure commodities suitable for creating recycled goods. This market disruption as a result of China’s new regulations, however, provides us the chance to re-examine and improve our current disposal & collection habits with modern monitoring & artificial intelligence technology.
Using images from our in-dumpster cameras, Compology has developed an ML-based process that helps identify, measure and alert for contaminants in recycling containers before they are picked-up, helping keep the recycling stream clean.
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In this presentation, we will walk through our ML-based contamination measurement and scoring process by showing how Waste Management, a national waste hauler, has experienced 57% contamination reduction in nearly 2,000 containers over six months, This progress shows significant strides towards financially viable recycling services.
Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush
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Quantum computers promise a significant step up in computational power over conventional computers, but also suffer a number of counterintuitive limitations --- both in their computational model and in leading lab implementations. In this talk, we review how quantum computers compete with conventional computers and how conventional computers try to hold their ground. Then we outline what stands in the way of successful quantum ML applications.
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Meghana Ravikumar - Optimized Image Classification on the Cheap
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- 1. Large Scale Graph Processing & Machine Learning for Fraud Prevention 1 Disclaimer: views expressed here are my own and do not necessarily represent the views of PayPal, its affiliates or subsidiaries.
- 2. © 2016 PayPal Inc. Confidential and proprietary. Agenda • Graph Processing • Machine Learning 2
- 3. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing - Motivation • (Big) Data is not flat • Data is multi-relational, spatial, temporal, multi-modal • Machine learning algorithms assume i.i.d (independent & identically distributed) • Need Data Science for Graphs 3 Software (Java/JVM) Source: Lise Getoor, UCSC
- 4. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing - Motivation • Much harder to get performance lift on our flagship models • Need to re-look at all aspects of traditional model building • Need out-of-the-box thinking 4 Area we are missing (AUC 0.98)
- 5. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing With Giraph (Current) • Pregel (Google’2010) • Vertex Centric • Giraph (Apache Open Source) 5
- 6. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing With Giraph (Current) • Used to compute 2-step neighbhors – IP,VID • Data • 5 events (login, logout, transaction, fso, signup) • Stampy HDFS • 1 year • Workflow • Series of (10) map-reduce jobs to join/pre-process construct graph • Neighorhood computation uses Giraph • Output pushed out online as RADD • Issues • 2 days for end-to-end process • Lots of intermediate read/write • Hadoop cluster stability • Unsupported by Hortonworks 6 Hadoop HDFS (Stampy) RADD M/R Jobs M/R Jobs Giraph Jobs Giraph Job Online Offline
- 7. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing (Future) • Leverage graph representation to understand, analyze, visualize relationships – their strengths, direction & influence and to predict future relationships and interactions between entities (consumers, merchant, IP address, device IDs, nodes in a network…). • Algorithmic convergence of graph theory & machine learning (statistical relations learning & probabilistic graph modeling) • Multi-layered full fledged specialized stack, hardware to software. 7 GPU, FPGA, TPU., SSD (specialized hardware) Graph DB (specialized software) Graph Algorithms (Deterministic & Probabilistic) Graph Analytics & Visualization Graph Modeling (Prediction)
- 8. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing (Future) • Solve new problems that traditional approaches can not solve: • What is the likelihood that particular consumer (node in graph) colluding (hidden relationship in graph) with a particular merchant (node in graph)? • How fraudsters (series of nodes & edges) move money through our network i.e., anti-money laundering? • If a consumer (node) is buying (relation) product X (node) from merchant (node), what else can we recommend to the consumer? • If a server (node) in a data center goes down/unhealthy, what would would be the impact to other critical components? • Who (what) are the central influential people (assets) in our network? • Graph theory & algorithms (link analysis, page rank, belief propagation, markov random field) can help to uncover influencing nodes & hidden relationships. • Graph databases are much more efficient in unearthing graph properties, have flexible schema that is amenable to changing data, scale naturally to large data sets as they do not require expensive join operations. 8
- 9. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing POC • POC with new stack • Single powerful machine in-lieu of 1200+ node stampy cluster • 160 core • State-of-the-art NVidia Pascal GPU (4 P100) • 1 TB RAM • 2 TB SSD • Graph DB optimized for scale-up architecture (Neo4j) • Graph computing frameworks optimized for single server/GPUs 9 Hadoop HDFS (Stampy) RADD Graph DB (Neo4J) Online Offline Hardware P100 GPU; SSD Graph Algorithms (Neo4J) Graph Algorithms (GunRock)
- 10. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing POC • Graph • 1.5 billion edges; 0.5 billion vertices • Static graph (1 year); No time dimension • Vertices - Customer ID, Merchant ID, IP; Edges – payment transaction 10 Degree 2^0: 58104040 (58.10%) Degree 2^1: 23034683 (23.03%) Degree 2^2: 11711920 (11.71%) Degree 2^3: 5098768 (5.10%) Degree 2^4: 1575787 (1.58%) Degree 2^5: 366774 (0.37%) Degree 2^6: 79654 (0.08%) Degree 2^7: 19796 (0.02%) Degree 2^8: 5861 (0.01%) Degree 2^9: 1886 (0.00%) Degree 2^10: 581 (0.00%) Degree 2^11: 179 (0.00%) Degree 2^12: 52 (0.00%) Degree 2^13: 12 (0.00%) Degree 2^14: 4 (0.00%) Degree 2^15: 1 (0.00%) Degree Distribution
- 11. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing POC • GraphAlgorithms • Page Rank • Connected Components • find sets of connected nodes • Each node is reachable from any other node in the set • Community Detection • Label Propagation – Group nodes with similar properties 11
- 12. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing POC • Results • Neo4j • GunRock, NvGraph, SNAP – in progress 12 Algorithm Compute Time (ms) Write Time (ms) Page Rank 77 124 Connected Components 156 337 Community Detection 384 361
- 13. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing - Key Learnings • Scale-up showing promise • 1 year graph ~70GB • Can fit entire graph in 1 machine’s memory • Significant reduction in processing time • ~2 days -> ~2 hours • Compute ~77 milliseconds • Significant value in Graph DB – sub-graph creation, interactive exploration, visualization, savings in computation 13 Source: Lise Getoor, UCSC
- 14. Machine Learning - Current Research Workflow Hardware - Research, Procure, Provision Modeling Algorithm Research Tool - Evaluate, Benchmar k Data & Variable Collection and Selection Model Training - Developm ent, Performan ce Eval, Spec (offline) Scoring Engine Developm ent (online) Deployme nt - One Box & Load test Auditing - Variable & Score Go Live ©2015 PayPal Inc. Confidential and proprietary. 14 • Manual Development (Spec -> Code) • ML Tool Specific Software Stack • Lack of research infrastructure/self-service tools • Serial pipeline; No early involvement of relevant teams
- 15. © 2016 PayPal Inc. Confidential and proprietary. Machine Learning - Future Modeling/Prediction Stack • Algorithm • Deep Learning • Offline to Online Learning • Human to Auto Feature Engineering • Model Spec • Language Agnostic Platform agnostic Spec (ex: protobufs) • Tools • Scalable & Flexible (ex: TensorFlow) • Software • Language Agnostic Software Stack • Hardware • Accelerators (GPUs, TPUs,..) 15 SS Hardware CPU + GPU/TPU Model Spec (protobufs) Tools (TensorFlow) Algorithm (Deep Learning) Software (Language Agnostic)
- 16. © 2016 PayPal Inc. Confidential and proprietary. Conclusions • Graph algorithms & Deep Learning shows promising analytical results • Scale-up architecture shows promise for our use cases • Probabilistic Graph Modeling + Deep Learning => Interpretable Models • Scratched surface; lot left to accomplish 16 Software (Java/JVM) Source: Lise Getoor, UCSC