Performance Comparison of Dimensionality Reduction Methods using MCDRAM Publications
The recent blast of dataset size, in number of records and in addition of attributes, has set off the improvement of various big data platforms and in addition parallel data analytic algorithms. In the meantime however, it has pushed for the utilization of data dimensionality reduction systems. Mobile Telecom Industry competition has become more and more fierce. In order to improve their services and business in the competitive world, they are ready to analyse the stored data by several data mining technologies to retain customers and maintain their relationship with them. Mobile Call Detail Record (MCDR) comprises diversity and complexity information containing information like Voice Call, Text Message, Video Calls, and other Data Services usages. It is proposed to evaluate and compare the performance of different dimensionality reduction methods such as Chi-Square (Chi2) Method, Principal Component Analysis (PCA), Information Gain Attribute Evaluator, Gain-Ratio Attribute Evaluator (GRAE), Attribute Selected Classifier (ASC) and Quantile Regression (QR) Methods.
Data imputing uses to posit missing data values, as missing data have a negative effect on the computation validity of models. This study develops a genetic algorithm (GA) to optimize imputing for missing cost data of fans used in road tunnels by the Swedish Transport Administration (Trafikverket). GA uses to impute the missing cost data using an optimized valid data period. The results show highly correlated data (R- squared 0.99) after imputing the missing data. Therefore, GA provides a wide search space to optimize imputing and create complete data. The complete data can be used for forecasting and life cycle cost analysis. Ritesh Kumar Pandey | Dr Asha Ambhaikar"Data Imputation by Soft Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14112.pdf http://www.ijtsrd.com/computer-science/real-time-computing/14112/data-imputation-by-soft-computing/ritesh-kumar-pandey
A VERY high level over view of Graph Analytics concepts and techniques, including structural analytics, Connectivity Analytics, Community Analytics, Path Analytics, as well as Pattern Matching
Performance Comparison of Dimensionality Reduction Methods using MCDRAM Publications
The recent blast of dataset size, in number of records and in addition of attributes, has set off the improvement of various big data platforms and in addition parallel data analytic algorithms. In the meantime however, it has pushed for the utilization of data dimensionality reduction systems. Mobile Telecom Industry competition has become more and more fierce. In order to improve their services and business in the competitive world, they are ready to analyse the stored data by several data mining technologies to retain customers and maintain their relationship with them. Mobile Call Detail Record (MCDR) comprises diversity and complexity information containing information like Voice Call, Text Message, Video Calls, and other Data Services usages. It is proposed to evaluate and compare the performance of different dimensionality reduction methods such as Chi-Square (Chi2) Method, Principal Component Analysis (PCA), Information Gain Attribute Evaluator, Gain-Ratio Attribute Evaluator (GRAE), Attribute Selected Classifier (ASC) and Quantile Regression (QR) Methods.
Data imputing uses to posit missing data values, as missing data have a negative effect on the computation validity of models. This study develops a genetic algorithm (GA) to optimize imputing for missing cost data of fans used in road tunnels by the Swedish Transport Administration (Trafikverket). GA uses to impute the missing cost data using an optimized valid data period. The results show highly correlated data (R- squared 0.99) after imputing the missing data. Therefore, GA provides a wide search space to optimize imputing and create complete data. The complete data can be used for forecasting and life cycle cost analysis. Ritesh Kumar Pandey | Dr Asha Ambhaikar"Data Imputation by Soft Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14112.pdf http://www.ijtsrd.com/computer-science/real-time-computing/14112/data-imputation-by-soft-computing/ritesh-kumar-pandey
A VERY high level over view of Graph Analytics concepts and techniques, including structural analytics, Connectivity Analytics, Community Analytics, Path Analytics, as well as Pattern Matching
Exploiting Structure in Representation of Named Entities using Active LearningYunyao Li
Slides for our COLING'18 paper: http://aclweb.org/anthology/C18-1058
Fundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations arise in complex ways that cannot be captured using textual similarity metrics. However, entities have underlying structures, typically shared by entities of the same entity type, that can help reason over their name variations. Discovering, learning and manipulating these structures typically requires high manual effort in the form of large amounts of labeled training data and handwritten transformation programs. In this work, we propose an active-learning based framework that drastically reduces the labeled data required to learn the structures of entities. We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels. Our experiments show that our framework consistently outperforms both handwritten programs and supervised learning models. We also demonstrate the utility of our framework in relation extraction and entity resolution tasks.
Data Patterns - A Native Open Source Data Profiling Tool for HPCC SystemsHPCC Systems
As part of the 2018 HPCC Systems Summit Community Day event:
Data profiling is a technique used to uncover information about a source of data. Information such as the shape or accuracy of the data is extremely useful during data discovery (when you're exploring a new dataset) or when verifying that updated data appears to be a valid replacement for old data. DataPatterns, an open sourced ECL bundle for HPCC Systems, offers a native function macro for data profiling that is easy to use and supports a number of options for tuning the profile result. This talk will briefly explore the bundle's profile feature and options.
Dan Camper has been with LexisNexis Risk for four years and is a Senior Architect in the Solutions Lab Group. He has worked for Apple and Dun & Bradstreet, and he ran his own custom programming shop for a decade. He's been writing software professionally for over 35 years and has worked on a myriad of systems, using a lot of different programming languages. He thinks ECL is pretty neat.
Developing Optimization Applications Quickly and Effectively with Algebraic M...Bob Fourer
Can you negotiate the complexities of the optimization modeling lifecycle, to deliver a working application before the funding runs out or the problem owner loses interest? Algebraic languages streamline the key steps of model formulation, testing, and revision, while offering powerful facilities for incorporating models into larger systems and deploying them to users. This presentation introduces algebraic modeling for optimization by carrying a single illustrative example through multiple contexts, from interactively evolved formula- tions to scripted iterative schemes to embedded applica- tions. We feature the new Python API for AMPL, and the QuanDec system for creating web-based collaborative applications from AMPL models.
Innovaccer service capabilities with case studiesAbhinav Shashank
Innovaccer is a California based research acceleration firm assisting hundreds of researchers from Harvard, Stanford, Wharton, MIT etc.
This slide describes our capabilities of assisting research endeavors. To get in touch with us, please write to info@innovaccer.com
To know more, please visit our website : www.innovaccer.com
Feature Importance Analysis with XGBoost in Tax auditMichael BENESTY
Presentation of a real use case at TAJ law firm (Deloitte Paris) of applying Machine learning on accounting to help clients to prepare their tax audit.
Attached is the slides deck of my speech at the Road to Big Data event held in Milan and Rome last March. Graph Analytics is a branch of Machine Learning with very high potential - it complements traditional Machine Learning techniques focusing on relationships rather than entities and relative attributes...
To download please go to: http://www.intelligentmining.com/knowledge-base.html
Slides as presented by Alex Lin to the NYC Predictive Analytics Meetup group: http://www.meetup.com/NYC-Predictive-Analytics/ on April 1, 2010 (no joke!) :)
Issues in Query Processing and OptimizationEditor IJMTER
The paper identifies the various issues in query processing and optimization while
choosing the best database plan. It is unlike preceding query optimization techniques that uses only a
single approach for identifying best query plan by extracting data from database. Our approach takes
into account various phases of query processing and optimization, heuristic estimation techniques
and cost function for identifying the best execution plan. A review report on various phases of query
processing, goals of optimizer, various rules for heuristic optimization and cost components involved
are presented in this paper.
SF Big Analytics: Introduction to Succinct by UC Berkeley AmpLabChester Chen
Topic: Introduction to Succinct by UC Berkeley AmpLab.
"Cloud services today need to perform fast, interactive queries on large data volumes. Several recent studies have shown that data is growing faster than memory capacity, making in-memory query execution increasingly challenging. At UC Berkeley, we have built Succinct, a distributed data store that overcomes this problem by enabling a wide range of interactive queries (e.g., search, random access, range queries, and even regular expressions) directly on compressed data. Besides its ability to execute queries on compressed data, Succinct differs from existing data stores along several dimensions. First, Succinct unifies several powerful data models (key-value stores, document stores, tables, etc.) using a single interface. Second, Succinct enables applications to choose a desired compression factor, allowing applications to use larger memory for improved performance. Finally, Succinct allows applications to change the compression factor on the fly, enabling new approaches to handling skewed query distributions, time-varying loads, and failure tolerance. In this talk, I will describe Succinct's design, implementation and semantics. Succinct is completely open-sourced, and we have also released Succinct as a library that simplifies integration of Succinct data structures and techniques with existing data stores.”
Speaker bio:
"Anurag is a graduate student at AMPLab, UC Berkeley, where he is advised by Prof. Ion Stoica. He co-created Succinct with Rachit Agarwal and Ion Stoica."
You can find more information about the project here: http://succinct.cs.berkeley.edu/wp/wordpress/?p=143
Real Time Machine Learning Visualization With SparkChester Chen
Training machine learning model involves a lot of experimentation, we need a way to visualize the training process.
We presented a system to enable real time machine learning visualization with Spark:
-- Gives visibility into the training of a model
-- Allows us monitor the convergence of the algorithms during training
-- Can stop the iterations when convergence is good enough.
Exploiting Structure in Representation of Named Entities using Active LearningYunyao Li
Slides for our COLING'18 paper: http://aclweb.org/anthology/C18-1058
Fundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations arise in complex ways that cannot be captured using textual similarity metrics. However, entities have underlying structures, typically shared by entities of the same entity type, that can help reason over their name variations. Discovering, learning and manipulating these structures typically requires high manual effort in the form of large amounts of labeled training data and handwritten transformation programs. In this work, we propose an active-learning based framework that drastically reduces the labeled data required to learn the structures of entities. We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels. Our experiments show that our framework consistently outperforms both handwritten programs and supervised learning models. We also demonstrate the utility of our framework in relation extraction and entity resolution tasks.
Data Patterns - A Native Open Source Data Profiling Tool for HPCC SystemsHPCC Systems
As part of the 2018 HPCC Systems Summit Community Day event:
Data profiling is a technique used to uncover information about a source of data. Information such as the shape or accuracy of the data is extremely useful during data discovery (when you're exploring a new dataset) or when verifying that updated data appears to be a valid replacement for old data. DataPatterns, an open sourced ECL bundle for HPCC Systems, offers a native function macro for data profiling that is easy to use and supports a number of options for tuning the profile result. This talk will briefly explore the bundle's profile feature and options.
Dan Camper has been with LexisNexis Risk for four years and is a Senior Architect in the Solutions Lab Group. He has worked for Apple and Dun & Bradstreet, and he ran his own custom programming shop for a decade. He's been writing software professionally for over 35 years and has worked on a myriad of systems, using a lot of different programming languages. He thinks ECL is pretty neat.
Developing Optimization Applications Quickly and Effectively with Algebraic M...Bob Fourer
Can you negotiate the complexities of the optimization modeling lifecycle, to deliver a working application before the funding runs out or the problem owner loses interest? Algebraic languages streamline the key steps of model formulation, testing, and revision, while offering powerful facilities for incorporating models into larger systems and deploying them to users. This presentation introduces algebraic modeling for optimization by carrying a single illustrative example through multiple contexts, from interactively evolved formula- tions to scripted iterative schemes to embedded applica- tions. We feature the new Python API for AMPL, and the QuanDec system for creating web-based collaborative applications from AMPL models.
Innovaccer service capabilities with case studiesAbhinav Shashank
Innovaccer is a California based research acceleration firm assisting hundreds of researchers from Harvard, Stanford, Wharton, MIT etc.
This slide describes our capabilities of assisting research endeavors. To get in touch with us, please write to info@innovaccer.com
To know more, please visit our website : www.innovaccer.com
Feature Importance Analysis with XGBoost in Tax auditMichael BENESTY
Presentation of a real use case at TAJ law firm (Deloitte Paris) of applying Machine learning on accounting to help clients to prepare their tax audit.
Attached is the slides deck of my speech at the Road to Big Data event held in Milan and Rome last March. Graph Analytics is a branch of Machine Learning with very high potential - it complements traditional Machine Learning techniques focusing on relationships rather than entities and relative attributes...
To download please go to: http://www.intelligentmining.com/knowledge-base.html
Slides as presented by Alex Lin to the NYC Predictive Analytics Meetup group: http://www.meetup.com/NYC-Predictive-Analytics/ on April 1, 2010 (no joke!) :)
Issues in Query Processing and OptimizationEditor IJMTER
The paper identifies the various issues in query processing and optimization while
choosing the best database plan. It is unlike preceding query optimization techniques that uses only a
single approach for identifying best query plan by extracting data from database. Our approach takes
into account various phases of query processing and optimization, heuristic estimation techniques
and cost function for identifying the best execution plan. A review report on various phases of query
processing, goals of optimizer, various rules for heuristic optimization and cost components involved
are presented in this paper.
SF Big Analytics: Introduction to Succinct by UC Berkeley AmpLabChester Chen
Topic: Introduction to Succinct by UC Berkeley AmpLab.
"Cloud services today need to perform fast, interactive queries on large data volumes. Several recent studies have shown that data is growing faster than memory capacity, making in-memory query execution increasingly challenging. At UC Berkeley, we have built Succinct, a distributed data store that overcomes this problem by enabling a wide range of interactive queries (e.g., search, random access, range queries, and even regular expressions) directly on compressed data. Besides its ability to execute queries on compressed data, Succinct differs from existing data stores along several dimensions. First, Succinct unifies several powerful data models (key-value stores, document stores, tables, etc.) using a single interface. Second, Succinct enables applications to choose a desired compression factor, allowing applications to use larger memory for improved performance. Finally, Succinct allows applications to change the compression factor on the fly, enabling new approaches to handling skewed query distributions, time-varying loads, and failure tolerance. In this talk, I will describe Succinct's design, implementation and semantics. Succinct is completely open-sourced, and we have also released Succinct as a library that simplifies integration of Succinct data structures and techniques with existing data stores.”
Speaker bio:
"Anurag is a graduate student at AMPLab, UC Berkeley, where he is advised by Prof. Ion Stoica. He co-created Succinct with Rachit Agarwal and Ion Stoica."
You can find more information about the project here: http://succinct.cs.berkeley.edu/wp/wordpress/?p=143
Real Time Machine Learning Visualization With SparkChester Chen
Training machine learning model involves a lot of experimentation, we need a way to visualize the training process.
We presented a system to enable real time machine learning visualization with Spark:
-- Gives visibility into the training of a model
-- Allows us monitor the convergence of the algorithms during training
-- Can stop the iterations when convergence is good enough.
InfoGAN: Interpretable Representation Learning by Information Maximizing Gene...홍배 김
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
오사카 대학 박사과정인 Takato Horii군이 작성한 자료
데이터 생성 모델로 우수한 GAN을 이용하여 비지도학습을 통해
"알기쉬게" 이미지의 정보를 표현하는 특징량을 "간단하게"획득하기
* 특징이 서로 얽혀있는 Physical space에서 서로 독립적인 Eigen space로 변환하는 것과 같은 원리
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
The Barclays Data Science Hackathon: Building Retail Recommender Systems base...Data Science Milan
In the depths of the last cold, wet British winter, the Advanced Data Analytics team from Barclays escaped to a villa on Lanzarote, Canary Islands, for a one week hackathon where they collaboratively developed a recommendation system on top of Apache Spark. The contest consisted on using Bristol customer shopping behaviour data to make personalised recommendations in a sort of Kaggle-like competition where each team's goal was to build an MVP and then repeatedly iterate on it using common interfaces defined by a specifically built framework.
The talk will cover:
• How to rapidly prototype in Spark (via the native Scala API) on your laptop and magically scale to a production cluster without huge re-engineering effort.
• The benefits of doing type-safe ETLs representing data in hybrid, and possibly nested, structures like case classes.
• Enhanced collaboration and fair performance comparison by sharing ad-hoc APIs plugged into a common evaluation framework.
• The co-existence of machine learning models available in MLlib and domain-specific bespoke algorithms implemented from scratch.
• A showcase of different families of recommender models (business-to-business similarity, customer-to-customer similarity, matrix factorisation, random forest and ensembling techniques).
• How Scala (and functional programming) helped our cause.
Gianmario is a Senior Data Scientist at Pirelli Tyre, processing telemetry data for smart manufacturing and connected vehicles applications. His main expertise is on building production-oriented machine learning systems. Co-author of the Professional Manifesto for Data Science, he loves evangelising his passion for best practices and effective methodologies amongst the community. Prior to Pirelli, he worked in Financial Services (Barclays), Cyber Security (Cisco) and Predictive Marketing (AgilOne).
Keynote presentation from ECBS conference. The talk is about how to use machine learning and AI in improving software engineering. Experiences from our project in Software Center (www.software-center.se).
Presented the hands-on session on “Introduction to Big Data Analysis” at Dayananda Sagar University. Around 150+ University students benefitted from this session.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian from the University of Oxford explains Data Science and its relationship with Big Data and Cloud Computing. Then he illustrates using AzureML to perform a simple data science analytics.
Applying linear regression and predictive analyticsMariaDB plc
In this session Alejandro Infanzon, Solutions Engineer, introduces the linear regression and statistical functions that debuted in MariaDB ColumnStore 1.2, and how you can use them to support powerful analytics. He explains how to perform even-more-powerful analytics by writing multi-parameter user-defined functions (UDFs) – also new in MariaDB ColumnStore 1.2.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
In this slidedeck, Infochimps Director of Product, Tim Gasper, discusses how Infochimps tackles business problems for customers by deploying a comprehensive Big Data infrastructure in days; sometimes in just hours. Tim unlocks how Infochimps is now taking that same aggressive approach to deliver faster time to value by helping customers develop analytic applications with impeccable speed.
Michael will present an overview of Elastic's machine learning capabilities.
As we know, data science work can be messy, fractured, and challenging as data volumes increase. This session will explore how the Elastic stack can offer a single destination for data ingestion and exploration, time series modeling, and communication of results through data visualizations by focusing on a few sample data sources.
We will also explore new functionality offered by Elastic machine learning, in particular an integration with our APM solution.
Trained as a mathematician, Michael Hirsch started his career with no development experience. His first task - "model the world in a relational database." Over the last 7 years Michael has established himself a data scientist, with a focus on building end-to-end systems. In his career, he has built machine learning powered platforms for clients including Nike, Samsung, and Marvel, and approaches his work with the idea that machine learning is only as useful as the interfaces that users interact with.
Currently, Michael is a Product Engineer for Machine Learning at Elastic. He focuses on tailoring Elastic's ML offering to customer use cases, as well as integrating machine learning capabilities across the entire Elastic Stack.
Predictive Analytics Project in Automotive IndustryMatouš Havlena
Original article: http://www.havlena.net/en/business-analytics-intelligence/predictive-analytics-project-in-automotive-industry/
I had a chance to work on a predictive analytics project for a US car manufacturer. The goal of the project was to evaluate the feasibility to use Big Data analysis solutions for manufacturing to solve different operational needs. The objective was to determine a business case and identify a technical solution (vendor). Our task was to analyze production history data and predict car inspection failures from the production line. We obtained historical data on defects on the car, how the car moved along the assembly line and car specific information like engine type, model, color, transmission type, and so on. The data covered the whole manufacturing history for one year. We used IBM BigInsights and SPSS Modeler to make the predictions.
Bhadale group of companies data science project methodologies catalogueVijayananda Mohire
This is our offering for data science project methodologies. We offer our expertise in transforming your enterprise for the next big data revolution for Data science project
Decision Making Framework in e-Business Cloud Environment Using Software Metr...ijitjournal
Cloud computing technology is most important one in IT industry by enabling them to offer access to their
system and application services on payment type. As a result, more than a few enterprises with Facebook,
Microsoft, Google, and amazon have started offer to their clients. Quality software is most important one in
market competition in this paper presents a hybrid framework based on the goal/question/metric paradigm
to evaluate the quality and effectiveness of previous software goods in project, product and organizations
in a cloud computing environment. In our approach it support decision making in the area of project,
product and organization levels using Neural networks and three angular metrics i.e., project metrics,
product metrics, and organization metrics
Similar to Alpine Tech Talk: System ML by Berthold Reinwald (20)
GPUs used with Apache Spark are leveraged to speed up machine learning (ML) model training and inference. Data preparation stages are traditionally run on CPUs. The RAPIDS Accelerator for Apache Spark is a plugin jar that takes advantage of Apache Spark 3.x's ability to schedule on GPUs. The RAPIDS Accelerator replaces CPU expressions in a physical plan with GPU equivalents for dataframe operations. Code change is not required, making transition to GPUs seamless.
We'll give an overview of what the RAPIDS Accelerator is, how it works, and benefits from using the accelerator. We will discuss benchmarks showing the performance and cost benefits of leveraging GPUs for Spark ETL processing. We'll showcase a user tool that will help estimate speedups and cost savings.
Talk at SF Big Analytics https://www.meetup.com/sf-big-analytics/events/285731741/
Distributed systems are made up of many components such as authentication, a persistence layer, stateless services, load balancers, and stateful coordination services. These coordination services are central to the operation of the system, performing tasks such as maintaining system configuration state, ensuring service availability, name resolution, and storing other system metadata. Given their central role in the system it is essential that these systems remain available, fault tolerant and consistent. By providing a highly available file system-like abstraction as well as powerful recipes such as leader election, Apache Zookeeper is often used to implement these services. Although powerful, the Zookeeper interface may not be flexible enough or provide sufficient performance for all applications and many systems are replacing Zookeeper based solutions with Raft which provides a more generic interface to high availability and fault tolerance through the use of State Machine replication. This talk will go over a generic example of stateful coordination service moving from Zookeeper to Raft.
Speaker: Tyler Crain ( Alluxio)
Tyler Crain is a software engineer at Alluxio, working on distributed systems within the Alluxio core team. Before this, Tyler held Post-Doc positions at the University of Sydney and Sorbonne Universities where he performed research on topics including distributed key-value stores, distributed consensus and blockchain. Tyler received his PhD from the University of Rennes where he worked on Transactional Memory. He also holds a Masters degree in Computer Science from University of California Santa Barbara.
talk at SF Big Analytics:
Related Blog: https://www.alluxio.io/blog/from-zookeeper-to-raft-how-alluxio-stores-file-system-state-with-high-availability-and-fault-tolerance/
SF Big Analytics 2022-03-15: Persia: Scaling DL Based Recommenders up to 100 ...Chester Chen
Recent years have witnessed an exponential growth of the model scale in recommendation/Ads/search—from Google’s 2016 model with 1 billion parameters to the latest Facebook’s model with 12 trillion parameters. Significant quality boost has come with each jump of the model capacity, which makes people believe the era of 100 trillion parameters is around the corner. To prepare the exponential growth of the model size, an efficient distributed training system is in urgent need. However, the training of such huge models is challenging even within industrial scale data centers. In this talk, I will introduce Persia -- an open training system developed by my team -- to resolve this challenge by careful co-design of both the optimization algorithm and the distributed system architecture. Persia admits nearly linear speedup properties while scaling the number of workers and the model size. Beside the capability of training 100 trillion parameters, it also shows a clear advantage in efficiency over other open sourced engines.
paper link:
https://arxiv.org/pdf/2111.05897.pdf
Speaker: Ji Liu
Dr. Ji Liu received his Ph.D in computer science and his bachelor degree in automation from University of Wisconsin-Madison and University of Science and Technology of China, respectively. After graduation, he joined the University of Rochester as an assistant professor, conducting research in machine learning, optimization, and reinforcement learning. The developed asynchronous and decentralized algorithms were widely used in industry, such as IBM, Microsoft, etc. He left academia and joined Tencent in 2017, exploring AI’s boundary. The developing AI agent Tstarbot was considered to be a milestone for mastering the most challenging RTS game -- Starcraft II. His second stop in industry is Kwai - the second largest short video company in China. He founded and led multiple international teams with different functionalities: platform team, product team, and research team. His team Contributed to 15+% annual revenue growth in Ads. He published 100+ papers in top-tier CS conferences and journals, and received multiple best paper awards (e.g., SIGKDD 2010 and UAI 2015 Facebook best paper). He was an awardee of MIT TR 35 under 35 in China and IBM faculty award in 2017. He was nominated to be one of China top 5 AI innovators under 35 in 2018
SF Big Analytics talk: NVIDIA FLARE: Federated Learning Application Runtime E...Chester Chen
Topic:
NVIDIA FLARE: Federated Learning Application Runtime Environment for Developing Robust AI Models
Summary:
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without moving data. We created NVIDIA FLARE as an open-source SDK to make it easier for data scientists to use FL in their research. The SDK allows existing machine learning and deep learning workflows adapted for distributed learning across enterprises and enables platform developers to build a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package and allows researchers to bring their data science workflows implemented in any training libraries (PyTorch, TensorFlow, or even NumPy), and apply them in real-world FL settings. This talk will introduce the key design principles of NVIDIA FLARE and illustrate use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms.
Speaker: Dr. Holger Roth ( Nvidia)
Holger Roth is a Sr. Applied Research Scientist at NVIDIA focusing on deep learning for medical imaging. He has been working closely with clinicians and academics over the past several years to develop deep learning based medical image computing and computer-aided detection models for radiological applications. He is an Associate Editor for IEEE Transactions of Medical Imaging and holds a Ph.D. from University College London, UK. In 2018, he was awarded the MICCAI Young Scientist Publication Impact Award.
A missing link in the ML infrastructure stack?Chester Chen
Talk at SF Big Analytics
Machine learning is quickly becoming a product engineering discipline. Although several new categories of infrastructure and tools have emerged to help teams turn their models into production systems, doing so is still extremely challenging for most companies. In this talk, we survey the tooling landscape and point out several parts of the machine learning lifecycle that are still underserved. We propose a new category of tool that could help alleviate these challenges and connect the fragmented production ML tooling ecosystem. We conclude by discussing similarities and differences between our proposed system and those of a few top companies.
Bio: Josh Tobin is the founder and CEO of a stealth machine learning startup. Previously, Josh worked as a deep learning & robotics researcher at OpenAI and as a management consultant at McKinsey. He is also the creator of Full Stack Deep Learning (fullstackdeeplearning.com), the first course focused on the emerging engineering discipline of production machine learning. Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel.
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...Chester Chen
Uber developed an new Spark ingestion system, Marmaray, for data ingestion from various sources. It’s designed to ingest billions of Kafka messages every 30 minutes. The amount of data handled by the pipeline is of the order hundreds of TBs. Omar details how to tackle such scale and insights into the optimizations techniques. Some key highlights are how to understand bottlenecks in Spark applications, to cache or not to cache your Spark DAG to avoid rereading your input data, how to effectively use accumulators to avoid unnecessary Spark actions, how to inspect your heap and nonheap memory usage across hundreds of executors, how you can change the layout of data to save long-term storage cost, how to effectively use serializers and compression to save network and disk traffic, and how to reduce amortize the cost of your application by multiplexing your jobs, different techniques for reducing memory footprint, runtime, and on-disk usage. CGI was able to significantly (~10%–40%) reduce memory footprint, runtime, and disk usage.
Speaker: Omkar Joshi (Uber)
Omkar Joshi is a senior software engineer on Uber’s Hadoop platform team, where he’s architecting Marmaray. Previously, he led object store and NFS solutions at Hedvig and was an initial contributor to Hadoop’s YARN scheduler.
SF Big Analytics 2019112: Uncovering performance regressions in the TCP SACK...Chester Chen
Uncovering performance regressions in the TCP SACKs vulnerability fixes
In early July 2019, Databricks noticed some Apache Spark workloads regressing by as much as 6x. In this talk, we'll discuss how we traced these regressions back to the Linux kernel and the fixes for the TCP SACKs vulnerabilities. We will explain the symptoms we were seeing, walk through how we debugged the TCP connections, and dive into the Linux source to uncover the root cause.
Speaker: Chris Stevens (Databricks)
Chris Stevens is a software engineer at Databricks where he works on the reliability, scalability, and security of Apache Spark clusters. His work focuses on auto-scaling compute, auto-scaling storage, node initialization performance, and node health monitoring. Prior to Databricks, Chris founded the Minoca OS project, where he built a POSIX compliant, general purpose OS - from scratch - to run on resource constrained device. He got his start at Microsoft working on the Windows kernel team, porting the Windows boot environment from BIOS to UEFI.
SFBigAnalytics_20190724: Monitor kafka like a ProChester Chen
Kafka operators need to provide guarantees to the business that Kafka is working properly and delivering data in real time, and they need to identify and triage problems so they can solve them before end users notice them. This elevates the importance of Kafka monitoring from a nice-to-have to an operational necessity. In this talk, Kafka operations experts Xavier Léauté and Gwen Shapira share their best practices for monitoring Kafka and the streams of events flowing through it. How to detect duplicates, catch buggy clients, and triage performance issues – in short, how to keep the business’s central nervous system healthy and humming along, all like a Kafka pro.
Speakers: Gwen Shapira, Xavier Leaute (Confluence)
Gwen is a software engineer at Confluent working on core Apache Kafka. She has 15 years of experience working with code and customers to build scalable data architectures. She currently specializes in building real-time reliable data processing pipelines using Apache Kafka. Gwen is an author of “Kafka - the Definitive Guide”, "Hadoop Application Architectures", and a frequent presenter at industry conferences. Gwen is also a committer on the Apache Kafka and Apache Sqoop projects.
Xavier Leaute is One of the first engineers to Confluent team, Xavier is responsible for analytics infrastructure, including real-time analytics in KafkaStreams. He was previously a quantitative researcher at BlackRock. Prior to that, he held various research and analytics roles at Barclays Global Investors and MSCI.
SF Big Analytics 2019-06-12: Managing uber's data workflows at scaleChester Chen
Talk 2. Managing Uber’s Data workflow at Scale.
Uber microservices serving millions of rides a day, leading to 100+ PB of data. To democratize data pipelines, Uber needed a central tool that provides a way to author, manage, schedule, and deploy data workflows at scale. This talk details Uber’s journey toward a unified and scalable data workflow system used to manage this data and shares the challenges faced and how the company has rearchitected several components of the system—such as scheduling and serialization—to make them highly available and more scalable.
Speaker Alex Kira (Uber)
Alex Kira is an engineering tech lead at Uber, where he works on the data workflow management team. His team provides a data infrastructure platform. In 19-year, he’s had experience across several software disciplines, including distributed systems, data infrastructure, and full stack development.
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...Chester Chen
Building highly efficient data lakes using Apache Hudi (Incubating)
Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake.
Speaker: Vinoth Chandar (Uber)
Vinoth is Technical Lead at Uber Data Infrastructure Team
SF Big Analytics_20190612: Scaling Apache Spark on Kubernetes at LyftChester Chen
Talk 1. Scaling Apache Spark on Kubernetes at Lyft
As part of this mission Lyft invests heavily in open source infrastructure and tooling. At Lyft Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark at Lyft has evolved to solve both Machine Learning and large scale ETL workloads. By combining the flexibility of Kubernetes with the data processing power of Apache Spark, Lyft is able to drive ETL data processing to a different level. In this talk, We will talk about challenges the Lyft team faced and solutions they developed to support Apache Spark on Kubernetes in production and at scale. Topics Include: - Key traits of Apache Spark on Kubernetes. - Deep dive into Lyft's multi-cluster setup and operationality to handle petabytes of production data. - How Lyft extends and enhances Apache Spark to support capabilities such as Spark pod life cycle metrics and state management, resource prioritization, and queuing and throttling. - Dynamic job scale estimation and runtime dynamic job configuration. - How Lyft powers internal Data Scientists, Business Analysts, and Data Engineers via a multi-cluster setup.
Speaker: Li Gao
Li Gao is the tech lead in the cloud native spark compute initiative at Lyft. Prior to Lyft, Li worked at Salesforce, Fitbit, Marin Software, and a few startups etc. on various technical leadership positions on cloud native and hybrid cloud data platforms at scale. Besides Spark, Li has scaled and productionized other open source projects, such as Presto, Apache HBase, Apache Phoenix, Apache Kafka, Apache Airflow, Apache Hive, and Apache Cassandra.
SFBigAnalytics- hybrid data management using cdapChester Chen
Cloud has emerged as a critical enabler of digital transformation, with the aim of reducing IT overheads and costs. However, cloud
migration is not instantaneous for a variety of reasons including data sensitivity, compliance and application performance. This results in the creation of diverse hybrid and multi-cloud environments and amplifies data management and integration challenges. This talk demonstrates how CDAP’s flexibility can allow you to utilize your existing on-premises infrastructure, as you evolve to the latest Big Data and Cloud services at your own pace, all while providing you a single, unified view of all your data, wherever it resides.
Speaker: Bhooshan Mogal, Google
Bhooshan Mogal is a Product Manager at Google, where he is focused on delivering best-in-class Data and Analytics services to GCP users. Prior to Google, he worked on data systems at Cask Data Inc, Pivotal and Yahoo.
Bighead: Airbnb's end-to-end machine learning platform
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. Bighead aims to tie together various open source and in-house projects to remove incidental complexity from ML workflows. Bighead is built on Python, Spark, and Kubernetes. The components include a lifecycle management service, an offline training and inference engine, an online inference service, a prototyping environment, and a Docker image customization tool. Each component can be used individually. In addition, Bighead includes a unified model building API that smoothly integrates popular libraries including TensorFlow, XGBoost, and PyTorch. Each model is reproducible and iterable through standardization of data collection and transformation, model training environments, and production deployment. 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 adopted in Airbnb and we have variety of models running in production. We plan to open source Bighead to allow the wider community to benefit from our work.
Speaker: Andrew Hoh
Andrew Hoh is the Product Manager for the ML Infrastructure and Applied ML teams at Airbnb. Previously, he has spent time building and growing Microsoft Azure's NoSQL distributed database. He holds a degree in computer science from Dartmouth College.
Sf big analytics_2018_04_18: Evolution of the GoPro's data platformChester Chen
Talk 1 : Evolution of the GoPro's data platform
In this talk, we will share GoPro’s experiences in building Data Analytics Cluster in Cloud. We will discuss: evolution of data platform from fixed-size Hadoop clusters to Cloud-based Spark Cluster with Centralized Hive Metastore +S3: Cost Benefits and DevOp Impact; Configurable, spark-based batch Ingestion/ETL framework;
Migration Streaming framework to Cloud + S3;
Analytics metrics delivery with Slack integration;
BedRock: Data Platform Management, Visualization & Self-Service Portal
Visualizing Machine learning Features via Google Facets + Spark
Speakers: Chester Chen
Chester Chen is the Head of Data Science & Engineering, GoPro. Previously, he was the Director of Engineering at Alpine Data Lab.
David Winters
David is an Architect in the Data Science and Engineering team at GoPro and the creator of their Spark-Kafka data ingestion pipeline. Previously He worked at Apple & Splice Machines.
Hao Zou
Hao is a Senior big data engineer at Data Science and Engineering team. Previously He worked as Alpine Data Labs and Pivotal
Analytics Metrics delivery and ML Feature visualization: Evolution of Data Pl...Chester Chen
GoPro’s camera, drone, mobile devices as well as web, desktop applications are generating billions of event logs. The analytics metrics and insights that inform product, engineering, and marketing team decisions need to be distributed quickly and efficiently. We need to visualize the metrics to find the trends or anomalies.
While trying to building up the features store for machine learning, we need to visualize the features, Google Facets is an excellent project for visualizing features. But can we visualize larger feature dataset?
These are issues we encounter at GoPro as part of the data platform evolution. In this talk, we will discuss few of the progress we made at GoPro. We will talk about how to use Slack + Plot.ly to delivery analytics metrics and visualization. And we will also discuss our work to visualize large feature set using Google Facets with Apache Spark.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
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See My Other Reviews Article:
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(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
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We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.