Kirk Haslbeck gave a presentation on data science at scale using Apache Spark. He discussed how Spark can handle large, distributed datasets and supports multiple programming languages. Spark addresses limitations of single-machine analysis and allows horizontal scaling. Haslbeck demonstrated how to build machine learning models for credit card fraud detection using Spark and showed visualizations created with R and Matplotlib in Apache Zeppelin.
How to Create 80% of a Big Data Pilot ProjectGreg Makowski
When evaluating Open Source Software, or other software of a certain size or complexity, organizations frequently want to conduct a Pilot project, or Proof of Concept (POC). This talk describes a process to reduce the length of the Pilot, by leveraging configurations from performance testing to POC starting configurations.
LUISS - Deep Learning and data analyses - 09/01/19Alberto Paro
The document provides an overview of a presentation on data analysis, mobility, proximity and app-based marketing. The presentation covers topics including big data concepts, artificial intelligence/machine learning, and architectures for data flow and machine learning. It discusses technologies like Elasticsearch, Kafka, and columnar databases. Example applications of AI in areas like retail, banking, and manufacturing are also presented.
Production model lifecycle management 2016 09Greg Makowski
This talk covers going over the various stages of building data mining models, putting them into production and eventually replacing them. A common theme throughout are three attributes of predictive models: accuracy, generalization and description. I assert you can have it all, and having all three is important for managing the lifecycle. A subtle point is that this is a step to developing embedded, automated data mining systems which can figure out themselves when they need to be updated.
Kamanja: Driving Business Value through Real-Time Decisioning SolutionsGreg Makowski
This is a first presentation of Kamanja, a new open-source real-time software product, which integrates with other big-data systems. See also links: http://www.meetup.com/SF-Bay-ACM/events/223615901/ and http://Kamanja.org to download, for docs or community support. For the YouTube video, see https://www.youtube.com/watch?v=g9d87rvcSNk (you may want to start at minute 33).
Deutsche Telekom and T-Systems are large European telecommunications companies. Deutsche Telekom has revenue of $75 billion and over 230,000 employees, while T-Systems has revenue of $13 billion and over 52,000 employees providing data center, networking, and systems integration services. Hadoop is an open source platform that provides more cost effective storage, processing, and analysis of large amounts of structured and unstructured data compared to traditional data warehouse solutions. Hadoop can help companies gain value from all their data by allowing them to ask bigger questions.
This document summarizes a presentation about the relationships between operations research (OR), data science, and business analytics. It begins by defining OR as applying analytical methods to help make better decisions, noting its broad scope. OR traditionally uses techniques like optimization, simulation, and forecasting. Data science also uses these techniques and focuses on descriptive, predictive, and prescriptive models. While OR and data science practitioners use similar methods, data scientists tend to have stronger software skills. The presentation argues that to be effective, OR practitioners need to expand their skills to work with new data types and technologies, and ensure their work is embedded within organizations to drive prescriptive analytics and cultural change. Bringing together soft OR methods, hard analytics techniques,
Big Data Hadoop ,Business Analytics& Data Warehousing Online Trainingsarala vanga
Big Data Hadoop Training & Certification online. Clear CCA175 & CCAH exams. 9 Real life Big Data projects. Led by industry experts.This online business analytics course introduces quantitative methods to analyze data and make better decisions.Our self-paced Data Warehousing training helps you master Data Warehousing Tools and concepts. The course also earns you a Data Warehousing ... Job Assistance. Enroll Now +1(210)503-7100 .For More Information : http://radiantits.com/.
How to Create 80% of a Big Data Pilot ProjectGreg Makowski
When evaluating Open Source Software, or other software of a certain size or complexity, organizations frequently want to conduct a Pilot project, or Proof of Concept (POC). This talk describes a process to reduce the length of the Pilot, by leveraging configurations from performance testing to POC starting configurations.
LUISS - Deep Learning and data analyses - 09/01/19Alberto Paro
The document provides an overview of a presentation on data analysis, mobility, proximity and app-based marketing. The presentation covers topics including big data concepts, artificial intelligence/machine learning, and architectures for data flow and machine learning. It discusses technologies like Elasticsearch, Kafka, and columnar databases. Example applications of AI in areas like retail, banking, and manufacturing are also presented.
Production model lifecycle management 2016 09Greg Makowski
This talk covers going over the various stages of building data mining models, putting them into production and eventually replacing them. A common theme throughout are three attributes of predictive models: accuracy, generalization and description. I assert you can have it all, and having all three is important for managing the lifecycle. A subtle point is that this is a step to developing embedded, automated data mining systems which can figure out themselves when they need to be updated.
Kamanja: Driving Business Value through Real-Time Decisioning SolutionsGreg Makowski
This is a first presentation of Kamanja, a new open-source real-time software product, which integrates with other big-data systems. See also links: http://www.meetup.com/SF-Bay-ACM/events/223615901/ and http://Kamanja.org to download, for docs or community support. For the YouTube video, see https://www.youtube.com/watch?v=g9d87rvcSNk (you may want to start at minute 33).
Deutsche Telekom and T-Systems are large European telecommunications companies. Deutsche Telekom has revenue of $75 billion and over 230,000 employees, while T-Systems has revenue of $13 billion and over 52,000 employees providing data center, networking, and systems integration services. Hadoop is an open source platform that provides more cost effective storage, processing, and analysis of large amounts of structured and unstructured data compared to traditional data warehouse solutions. Hadoop can help companies gain value from all their data by allowing them to ask bigger questions.
This document summarizes a presentation about the relationships between operations research (OR), data science, and business analytics. It begins by defining OR as applying analytical methods to help make better decisions, noting its broad scope. OR traditionally uses techniques like optimization, simulation, and forecasting. Data science also uses these techniques and focuses on descriptive, predictive, and prescriptive models. While OR and data science practitioners use similar methods, data scientists tend to have stronger software skills. The presentation argues that to be effective, OR practitioners need to expand their skills to work with new data types and technologies, and ensure their work is embedded within organizations to drive prescriptive analytics and cultural change. Bringing together soft OR methods, hard analytics techniques,
Big Data Hadoop ,Business Analytics& Data Warehousing Online Trainingsarala vanga
Big Data Hadoop Training & Certification online. Clear CCA175 & CCAH exams. 9 Real life Big Data projects. Led by industry experts.This online business analytics course introduces quantitative methods to analyze data and make better decisions.Our self-paced Data Warehousing training helps you master Data Warehousing Tools and concepts. The course also earns you a Data Warehousing ... Job Assistance. Enroll Now +1(210)503-7100 .For More Information : http://radiantits.com/.
The document is a report on the big data industry in 2011. It provides an overview of key big data technologies like Hadoop and NoSQL databases. It examines the major players in the space, both established companies looking to adopt these technologies and startups focused on Hadoop. The report also provides a market forecast for the big data industry from 2011-2015 and makes recommendations for vendors, users, investors, and others on engaging with emerging big data opportunities.
AI-SDV 2021: Francisco Webber - Efficiency is the New PrecisionDr. Haxel Consult
The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
A Big Data Telco Solution by Dr. Laura Wynterwkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
HPCC Systems - Open source, Big Data Processing & AnalyticsHPCC Systems
This document summarizes HPCC Systems, an open source big data processing and analytics platform. It provides high-performance computing capabilities to integrate vast amounts of data from multiple sources and enable real-time queries and analysis. The platform uses the ECL programming language which allows for declarative, implicitly parallel programming optimized for data-intensive applications. It also describes LexisNexis' use of HPCC Systems and related technologies like SALT and LexID to link and analyze large datasets to derive insights for risk assessment and fraud detection across various industries.
Transform Banking with Big Data and Automated Machine Learning 9.12.17Cloudera, Inc.
Banks are rich in valuable data and can build and maintain a competitive advantage by identifying and executing on high-value machine learning projects leveraging the rich data available.This webinar will describe use cases fit for big data and machine learning in the banking sector (commercial, consumer, regulatory, and markets) and the impact they can have for your organization.
3 things to learn:
* How to create a next generation data platform and why it is important
* How to monetize big data using predictive modeling and machine learning
* What is needed for automated machine learning as a sustainable, cost-effective, and efficient solution
This document describes 7 predictive analytics, Spark, and streaming use cases:
1) Live train time tables reduced spread by 40% for Dutch Railways
2) Intelligent equipment saved $40M/year for oil and gas companies
3) Algorithmic loyalty found products customers didn't know they needed for North Face
4) Predictive risk compliance avoided $440M loss in 40 minutes for ConvergEx
5) Live flight optimization helped get passengers home on time for United Airlines
6) Continuous transaction optimization monitored 20,000 systems for Morgan Stanley
7) IoT parcel tracking improved real-time tracking from 20% to 100% for Royal Mail
AI-SDV 2020: Special Hypertext Information Treatment in is Special Hypertext ...Dr. Haxel Consult
With all new technologies and intelligence one may think that all information issues will be solved in the (near) future. However, one of the most fundamental issues at hand is that with the lack of reliable, quality information there is no useable output to work with in the first place. This presentation looks at the global challenges that we are still faced with today relating to content that will keep us from truly intelligent discovery in the future if nothing is done.
The document discusses how DataRobot provides an automated machine learning platform to help address the shortage of data scientists. It notes that while demand for data scientists is increasing due to the growth of data and need to extract insights, the supply of data scientists cannot keep up. DataRobot aims to turn more data-focused resources into effective data scientists and make existing data scientists more productive by automating workflows like data preparation, model training, evaluation and deployment. This helps organizations capitalize on their data and gain business value from AI and machine learning applications.
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
This document provides an overview of Hadoop and big data use cases. It discusses the evolution of business analytics and data processing, as well as the architecture of traditional RDBMS systems compared to Hadoop. Examples of how companies have used Hadoop include a bank improving risk modeling by combining customer data, a telecom reducing churn by analyzing call logs, and a retailer targeting promotions by analyzing point-of-sale transactions. Hadoop allows these companies to gain valuable business insights from large and diverse data sources.
First in Class: Optimizing the Data Lake for Tighter IntegrationInside Analysis
The Briefing Room with Dr. Robin Bloor and Teradata RainStor
Live Webcast October 13, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=012bb2c290097165911872b1f241531d
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful data management solutions require a fusion of all relevant data, new and old, which has proven challenging for many companies. With a data lake that’s been optimized for fast queries, solid governance and lifecycle management, users can take data management to a whole new level.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses the relevance of data lakes in today’s information landscape. He’ll be briefed by Mark Cusack of Teradata, who will explain how his company’s archiving solution has developed into a storage point for raw data. He’ll show how the proven compression, scalability and governance of Teradata RainStor combined with Hadoop can enable an optimized data lake that serves as both reservoir for historical data and as a "system of record” for the enterprise.
Visit InsideAnalysis.com for more information.
Fit For Purpose: Preventing a Big Data LetdownInside Analysis
The Briefing Room with Dr. Robin Bloor and RedPoint Global
Live Webcast October 6, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9982ad3a2603345984895f279e849d35
Gartner recently placed Big Data in its “trough of disillusionment,” reflective of many leaders’ struggle to prove the value of Hadoop within their organization. While the promise of enhanced data integration and enrichment is obvious, measurable results have remained elusive. This episode of The Briefing Room will outline how to successfully tie Big Data to existing business applications, preventing your next Hadoop project from being another “Big Data letdown.”
Register today to learn from veteran Analyst Dr. Robin Bloor as he discusses the importance of converging enterprise data integration with intelligence and scalability. He’ll be briefed by George Corugedo of RedPoint Global, who will provide concrete examples of how the convergence of scalable cloud platforms, ever-expanding data sources and intelligent execution can turn the Big Data hype into demonstrable business value.
Visit InsideAnalysis.com for more information.
This document provides an overview of data science and machine learning. It discusses what data science and machine learning are, including extracting insights from data and computers learning without being explicitly programmed. It also covers Apache Spark, which is an open source framework for large-scale data processing. Finally, it discusses common machine learning algorithms like regression, classification, clustering, and dimensionality reduction.
As the Big Data market has evolved, the focus has shifted from data operations (storage, access and processing of data) to data science (understanding, analyzing and forecasting from data). And as new models are developed, organizations need a process for deploying analytics from research into the production environment. In this talk, we'll describe the five stages of real-time analytics deployment:
Data distillation
Model development
Model validation and deployment
Model refresh
Real-time model scoring
We'll review the technologies supporting each stage, and how Revolution Analytics software works with the entire analytics stack to bring Big Data analytics to real-time production environments.
Predictive Analytics - Big Data Warehousing MeetupCaserta
Predictive analytics has always been about the future, and the age of big data has made that future an increasingly dynamic place, filled with opportunity and risk.
The evolution of advanced analytics technologies and the continual development of new analytical methodologies can help to optimize financial results, enable systems and services based on machine learning, obviate or mitigate fraud and reduce cybersecurity risks, among many other things.
Caserta Concepts, Zementis, and guest speaker from FICO presented the strategies, technologies and use cases driving predictive analytics in a big data environment.
For more information, visit www.casertaconcepts.com or contact us at info@casertaconcepts.com
Integrating Structure and Analytics with Unstructured DataDATAVERSITY
How can you make sense of messy data? How do you wrap structure around non-relational, flexibly structured data? With the growth in cloud technologies, how do you balance the need for flexibility and scale with the need for structure and analytics? Join us for an overview of the marketplace today and a review of the tools needed to get the job done.
During this hour, we'll cover:
- How big data is challenging the limits of traditional data management tools
- How to recognize when tools like MongoDB, Hadoop, IBM Cloudant, R Studio, IBM dashDB, CouchDB, and others are the right tools for the job.
Apache Zeppelin and Spark for Enterprise Data ScienceBikas Saha
Apache Zeppelin and Spark are turning out to be useful tools in the toolkit of the modern data scientist when working on large scale datasets for machine learning. Zeppelin makes Big Data accessible with minimal effort using web browser based notebooks to interact with data in Hadoop. It enables data scientists to interactively explore and visualize their data and collaborate with others to develop models. Zeppelin has great integration with Apache Spark that delivers many machine learning algorithms out of the box to Zeppelin users as well as providing a fast engine to run custom machine learning on Big Data. The talk will describe the latest in Zeppelin and focus on how it has been made ready for the enterprise. With support for secure Hadoop clusters, LDAP/AD integration, user impersonation and session separation, Zeppelin can now be confidently used in secure and multi-tenant enterprise domains.
The document is a report on the big data industry in 2011. It provides an overview of key big data technologies like Hadoop and NoSQL databases. It examines the major players in the space, both established companies looking to adopt these technologies and startups focused on Hadoop. The report also provides a market forecast for the big data industry from 2011-2015 and makes recommendations for vendors, users, investors, and others on engaging with emerging big data opportunities.
AI-SDV 2021: Francisco Webber - Efficiency is the New PrecisionDr. Haxel Consult
The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
A Big Data Telco Solution by Dr. Laura Wynterwkwsci-research
Presented during the WKWSCI Symposium 2014
21 March 2014
Marina Bay Sands Expo and Convention Centre
Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University
HPCC Systems - Open source, Big Data Processing & AnalyticsHPCC Systems
This document summarizes HPCC Systems, an open source big data processing and analytics platform. It provides high-performance computing capabilities to integrate vast amounts of data from multiple sources and enable real-time queries and analysis. The platform uses the ECL programming language which allows for declarative, implicitly parallel programming optimized for data-intensive applications. It also describes LexisNexis' use of HPCC Systems and related technologies like SALT and LexID to link and analyze large datasets to derive insights for risk assessment and fraud detection across various industries.
Transform Banking with Big Data and Automated Machine Learning 9.12.17Cloudera, Inc.
Banks are rich in valuable data and can build and maintain a competitive advantage by identifying and executing on high-value machine learning projects leveraging the rich data available.This webinar will describe use cases fit for big data and machine learning in the banking sector (commercial, consumer, regulatory, and markets) and the impact they can have for your organization.
3 things to learn:
* How to create a next generation data platform and why it is important
* How to monetize big data using predictive modeling and machine learning
* What is needed for automated machine learning as a sustainable, cost-effective, and efficient solution
This document describes 7 predictive analytics, Spark, and streaming use cases:
1) Live train time tables reduced spread by 40% for Dutch Railways
2) Intelligent equipment saved $40M/year for oil and gas companies
3) Algorithmic loyalty found products customers didn't know they needed for North Face
4) Predictive risk compliance avoided $440M loss in 40 minutes for ConvergEx
5) Live flight optimization helped get passengers home on time for United Airlines
6) Continuous transaction optimization monitored 20,000 systems for Morgan Stanley
7) IoT parcel tracking improved real-time tracking from 20% to 100% for Royal Mail
AI-SDV 2020: Special Hypertext Information Treatment in is Special Hypertext ...Dr. Haxel Consult
With all new technologies and intelligence one may think that all information issues will be solved in the (near) future. However, one of the most fundamental issues at hand is that with the lack of reliable, quality information there is no useable output to work with in the first place. This presentation looks at the global challenges that we are still faced with today relating to content that will keep us from truly intelligent discovery in the future if nothing is done.
The document discusses how DataRobot provides an automated machine learning platform to help address the shortage of data scientists. It notes that while demand for data scientists is increasing due to the growth of data and need to extract insights, the supply of data scientists cannot keep up. DataRobot aims to turn more data-focused resources into effective data scientists and make existing data scientists more productive by automating workflows like data preparation, model training, evaluation and deployment. This helps organizations capitalize on their data and gain business value from AI and machine learning applications.
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
This document provides an overview of Hadoop and big data use cases. It discusses the evolution of business analytics and data processing, as well as the architecture of traditional RDBMS systems compared to Hadoop. Examples of how companies have used Hadoop include a bank improving risk modeling by combining customer data, a telecom reducing churn by analyzing call logs, and a retailer targeting promotions by analyzing point-of-sale transactions. Hadoop allows these companies to gain valuable business insights from large and diverse data sources.
First in Class: Optimizing the Data Lake for Tighter IntegrationInside Analysis
The Briefing Room with Dr. Robin Bloor and Teradata RainStor
Live Webcast October 13, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=012bb2c290097165911872b1f241531d
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful data management solutions require a fusion of all relevant data, new and old, which has proven challenging for many companies. With a data lake that’s been optimized for fast queries, solid governance and lifecycle management, users can take data management to a whole new level.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses the relevance of data lakes in today’s information landscape. He’ll be briefed by Mark Cusack of Teradata, who will explain how his company’s archiving solution has developed into a storage point for raw data. He’ll show how the proven compression, scalability and governance of Teradata RainStor combined with Hadoop can enable an optimized data lake that serves as both reservoir for historical data and as a "system of record” for the enterprise.
Visit InsideAnalysis.com for more information.
Fit For Purpose: Preventing a Big Data LetdownInside Analysis
The Briefing Room with Dr. Robin Bloor and RedPoint Global
Live Webcast October 6, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9982ad3a2603345984895f279e849d35
Gartner recently placed Big Data in its “trough of disillusionment,” reflective of many leaders’ struggle to prove the value of Hadoop within their organization. While the promise of enhanced data integration and enrichment is obvious, measurable results have remained elusive. This episode of The Briefing Room will outline how to successfully tie Big Data to existing business applications, preventing your next Hadoop project from being another “Big Data letdown.”
Register today to learn from veteran Analyst Dr. Robin Bloor as he discusses the importance of converging enterprise data integration with intelligence and scalability. He’ll be briefed by George Corugedo of RedPoint Global, who will provide concrete examples of how the convergence of scalable cloud platforms, ever-expanding data sources and intelligent execution can turn the Big Data hype into demonstrable business value.
Visit InsideAnalysis.com for more information.
This document provides an overview of data science and machine learning. It discusses what data science and machine learning are, including extracting insights from data and computers learning without being explicitly programmed. It also covers Apache Spark, which is an open source framework for large-scale data processing. Finally, it discusses common machine learning algorithms like regression, classification, clustering, and dimensionality reduction.
As the Big Data market has evolved, the focus has shifted from data operations (storage, access and processing of data) to data science (understanding, analyzing and forecasting from data). And as new models are developed, organizations need a process for deploying analytics from research into the production environment. In this talk, we'll describe the five stages of real-time analytics deployment:
Data distillation
Model development
Model validation and deployment
Model refresh
Real-time model scoring
We'll review the technologies supporting each stage, and how Revolution Analytics software works with the entire analytics stack to bring Big Data analytics to real-time production environments.
Predictive Analytics - Big Data Warehousing MeetupCaserta
Predictive analytics has always been about the future, and the age of big data has made that future an increasingly dynamic place, filled with opportunity and risk.
The evolution of advanced analytics technologies and the continual development of new analytical methodologies can help to optimize financial results, enable systems and services based on machine learning, obviate or mitigate fraud and reduce cybersecurity risks, among many other things.
Caserta Concepts, Zementis, and guest speaker from FICO presented the strategies, technologies and use cases driving predictive analytics in a big data environment.
For more information, visit www.casertaconcepts.com or contact us at info@casertaconcepts.com
Integrating Structure and Analytics with Unstructured DataDATAVERSITY
How can you make sense of messy data? How do you wrap structure around non-relational, flexibly structured data? With the growth in cloud technologies, how do you balance the need for flexibility and scale with the need for structure and analytics? Join us for an overview of the marketplace today and a review of the tools needed to get the job done.
During this hour, we'll cover:
- How big data is challenging the limits of traditional data management tools
- How to recognize when tools like MongoDB, Hadoop, IBM Cloudant, R Studio, IBM dashDB, CouchDB, and others are the right tools for the job.
Apache Zeppelin and Spark for Enterprise Data ScienceBikas Saha
Apache Zeppelin and Spark are turning out to be useful tools in the toolkit of the modern data scientist when working on large scale datasets for machine learning. Zeppelin makes Big Data accessible with minimal effort using web browser based notebooks to interact with data in Hadoop. It enables data scientists to interactively explore and visualize their data and collaborate with others to develop models. Zeppelin has great integration with Apache Spark that delivers many machine learning algorithms out of the box to Zeppelin users as well as providing a fast engine to run custom machine learning on Big Data. The talk will describe the latest in Zeppelin and focus on how it has been made ready for the enterprise. With support for secure Hadoop clusters, LDAP/AD integration, user impersonation and session separation, Zeppelin can now be confidently used in secure and multi-tenant enterprise domains.
IBM Netezza - The data warehouse in a big data strategyIBM Sverige
Big Data - Trender och verklighet inom Information Management.
Denna presentation hölls på IBM Data Server Day den 22 maj i Stockholm av Jacques Milman, Datawarehouse Architecture Leader, IBM
Data Pipelines and Telephony Fraud Detection Using Machine Learning Eugene
This document discusses data pipelines and machine learning for telephony fraud detection. It first covers data pipelines, including call detail records (CDRs), SIP messages, and local routing numbers being routed through Kafka for reliable delivery and stored in Cassandra and Postgres for storage and analysis. It then discusses fraud detection, including collecting CDR data, processing it asynchronously at scale using Spark Streaming and Cassandra, detecting anomalies both statically and dynamically, and alerting. Key challenges discussed are idempotency, partitioning, and consistency models for distributed systems.
The document discusses a presentation on fraud detection using database platforms. The session objectives are to understand why and how fraud patterns can be detected digitally, understand statistical approaches for quantification, identify tools and techniques, and understand how pattern detection fits in. The agenda includes sessions on managing fraud risk, statistical approaches, databases, demonstration, and Q&A. The presentation will cover fraud patterns detectable through digital analysis and data-driven approaches to fraud detection.
This document discusses Apache Zeppelin, an open-source web-based notebook that allows for interactive data analytics. It can be used for data exploration, visualization, collaboration and publishing. Zeppelin has deep integration with Apache Spark and supports multiple languages including Scala, Python, and SQL. It provides a Spark interpreter that allows users to analyze data using Spark without having to configure Spark themselves. The document demonstrates Zeppelin's functionality through examples and encourages readers to try it out and get involved in the community.
An introduction to Spark MLlib from the Apache Spark with Scala course available at https://www.supergloo.com/fieldnotes/portfolio/apache-spark-scala/. These slides present an overview on machine learning with Apache Spark MLlib.
For more background on machine learning see my other uploaded presentation "Machine Learning with Spark".
Presented at the MLConf in Seattle, this presentation offers a quick introduction to Apache Spark, followed by an overview of two novel features for data science
AWS re:Invent 2016: Fraud Detection with Amazon Machine Learning on AWS (FIN301)Amazon Web Services
In this session, we provide programmatic guidance on building tools and applications to detect and manage fraud and unusual activity specific to financial services institutions. Payment fraud is an ongoing concern for merchants and credit card issuers alike and these activities impact all industries, but are specifically detrimental to Financial Services. We provide a step-by-step walkthrough of a reference solution to detect and address credit card fraud in real time by using Apache Apex and Amazon Machine Learning capabilities. We also outline different resource and performance optimization options and how to work data security into the fraud detection workflow.
Real-Time Fraud Detection in Payment TransactionsChristian Gügi
This document discusses building a real-time fraud detection system using big data technologies. It outlines the cyber threat landscape, what anomalies and fraud detection are, and proposes an architecture with a data layer to integrate various sources and an analytics layer using stream processing, rules engines, and machine learning to score transactions in real-time and detect fraud. The system aims to scalably and reliably detect threats for increased security.
This document provides an overview of Apache Spark's MLlib machine learning library. It discusses machine learning concepts and terminology, the types of machine learning techniques supported by MLlib like classification, regression, clustering, collaborative filtering and dimensionality reduction. It covers MLlib's algorithms, data types, feature extraction and preprocessing capabilities. It also provides tips for using MLlib such as preparing features, configuring algorithms, caching data, and avoiding overfitting. Finally, it introduces ML Pipelines for constructing machine learning workflows in Spark.
Credit Fraud Prevention with Spark and Graph AnalysisJen Aman
This document discusses using Spark and graph analysis to prevent credit card fraud in real-time. It describes how fraud costs billions annually and affects millions of people. Common fraud types are outlined. The solution involves combining multiple data sources using Spark and a graph database to score applications for fraud in real-time. A demo is shown using sample fraudulent data and a fraud prediction model. Performance metrics are provided for the Databricks and Visallo platforms used to ingest data and detect fraud.
Practical Machine Learning Pipelines with MLlibDatabricks
This talk from 2015 Spark Summit East discusses Pipelines and related concepts introduced in Spark 1.2 which provide a simple API for users to set up complex ML workflows.
On December 5, 2013, Ron Steinkamp, principal, government advisory services at Brown Smith Wallace, presented at the 2013 MIS Training Institute Governance, Risk & Compliance Conference. Ron focused on the following keys to fraud prevention, detection and reporting:
1. Anti-fraud culture
2. Fraud policy
3. Fraud awareness/training
4. Hotline
5. Assess fraud risks
6. Review/investigation
7. Improved controls
How to Apply Big Data Analytics and Machine Learning to Real Time Processing ...Codemotion
The world gets connected more and more every year due to Mobile, Cloud and Internet of Things. "Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop to find patterns, e.g. for predictive maintenance or cross-selling. But how to increase revenue or reduce risks in new transactions? "Fast Data" via stream processing is the solution to embed patterns into future actions in real-time. This session discusses how machine learning and analytic models with R, Spark MLlib, H2O, etc. can be integrated into real-time event processing. A live demo concludes the session
Data Science lifecycle with Apache Zeppelin and Spark by Moonsoo LeeSpark Summit
This document discusses Apache Zeppelin, an open-source notebook for interactive data analytics. It provides an overview of Zeppelin's features, including interactive notebooks, multiple backends, interpreters, and a display system. The document also covers Zeppelin's adoption timeline, from its origins as a commercial product in 2012 to becoming an Apache Incubator project in 2014. Future projects involving Zeppelin like Helium and Z-Manager are also briefly described.
This document provides an overview of machine learning concepts and techniques using Apache Spark. It begins with introducing machine learning and describing supervised and unsupervised learning. Then it discusses Spark and how it can be used for large-scale machine learning tasks through its MLlib library and GraphX API. Several examples of machine learning applications are presented, such as classification, regression, clustering, and graph analytics. The document concludes with demonstrating machine learning algorithms in Spark.
This document discusses building a machine learning model for credit card fraud detection using a connected data platform. It describes limitations of traditional modeling approaches, how modern distributed tools can handle large data volumes, and security concerns around extracting data. The document then outlines requirements for a credit card fraud detection model, including detecting fraud within 2 seconds. It discusses rule-based logic, statistics, and machine learning approaches. Finally, it provides an overview of the technologies that could be used to build such a model at scale, including Spark, Storm, Kafka and HBase.
This workshop will provide a hands on introduction to basic Machine Learning techniques with Apache Spark ML using the cloud.
Format: A short introductory lecture on a select important supervised and unsupervised Machine Learning techniques followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Machine Learning with Spark ML. In the lab, you will use the following components: Apache Zeppelin (a “Modern Data Science Toolbox”) and Apache Spark. You will learn how to analyze the data, structure the data, train Machine Learning models and apply them to answer real-world questions.
Pre-requisites: Registrants must bring a laptop that can run the Hortonworks Data Cloud.
At this Crash Course everyone will have a cluster assigned to them to try several workloads using Machine Learning, Spark and Zeppelin on the cloud.
Speakers: Robert Hryniewicz
Hortonworks - IBM Cognitive - The Future of Data ScienceThiago Santiago
The document discusses Hortonworks and IBM's partnership around data management and analytics. It highlights how their combined platforms can power the modern data architecture with solutions for data at rest and in motion. Examples are provided of how customers like Merck and JPMC have leveraged Hortonworks' technologies to gain insights from their data and drive business outcomes. Industries that are investing in data science are also listed.
Enabling the Real Time Analytical EnterpriseHortonworks
This document discusses enabling real-time analytics in the enterprise. It begins with an overview of the challenges of real-time analytics due to non-integrated systems, varied data types and volumes, and data management complexity. A case study on real-time quality analytics in automotive is presented, highlighting the need to analyze varied data sources quickly to address issues. The Hortonworks/Attunity solution is then introduced using Attunity Replicate to integrate data from various sources in real-time into Hortonworks Data Platform for analysis. A brief demonstration of data streaming from a database into Kafka and then Hortonworks Data Platform is shown.
Real-Time With AI – The Convergence Of Big Data And AI by Colin MacNaughtonSynerzip
Making AI real-time to meet mission-critical system demands put a new spin on your architecture. To deliver AI-based applications that will scale as your data grows takes a new approach where the data doesn’t become the bottleneck. We all know that the deeper the data the better the results and the lower the risk. However, doing thousands of computations on big data requires new data structures and messaging to be used together to deliver real-time AI. During this session will look at real reference architectures and review the new techniques that were needed to make AI Real-Time.
This document discusses methods for harnessing big data. It describes how sensors collect Internet of Things (IoT) data and how Volvo applies analytics. It also summarizes three methods: 1) The US Air Force uses an integrated data warehouse and geospatial analysis to track assets globally. 2) Siemens uses data discovery processes to predict train failures by analyzing sensor and failure report data. 3) Yahoo uses Hadoop as a data lake to store and analyze large amounts of user data from various sources like social media and clickstreams. The document emphasizes that no single technology is a silver bullet for big data.
Introduction: This workshop will provide a hands-on introduction to Machine & Deep Learning.
Format: An introductory lecture on several supervised and unsupervised Machine Learning techniques followed by light introduction to Deep Learning. Both Apache Spark as well as TensorFlow will be introduced with relevant code samples that users can run in the cloud and explore.
Objective: To provide a quick and short hands-on introduction to Machine Learning with Spark Machine Learning library (MLlib) and Deep Learning with TensorFlow. In the lab, you will use the following components: Apache Zeppelin and Jupyter notebooks with Apache Spark and TensorFlow processing engines (respectively). You will learn how to analyze and structure data, train Machine Learning models and apply them to answer real-world questions. You will also learn how to select, train, and test Deep Learning models.
Prerequisites: Registrants must bring a laptop with a Chrome or Firefox web browser installed (with proxies disabled, i.e. must show venue IP to access cloud resources). These labs will be done in the cloud. At this Crash Course everyone will be assigned a cluster to try several workloads using Apache Spark and TensorFlow in Zeppelin and Jupyter notebooks (respectively) hosted in the cloud.
Apache Hadoop and its role in Big Data architecture - Himanshu Barijaxconf
In today’s world of exponentially growing big data, enterprises are becoming increasingly more aware of the business utility and necessity of harnessing, storing and analyzing this information. Apache Hadoop has rapidly evolved to become a leading platform for managing and processing big data, with the vital management, monitoring, metadata and integration services required by organizations to glean maximum business value and intelligence from their burgeoning amounts of information on customers, web trends, products and competitive markets. In this session, Hortonworks' Himanshu Bari will discuss the opportunities for deriving business value from big data by looking at how organizations utilize Hadoop to store, transform and refine large volumes of this multi-structured information. Connolly will also discuss the evolution of Apache Hadoop and where it is headed, the component requirements of a Hadoop-powered platform, as well as solution architectures that allow for Hadoop integration with existing data discovery and data warehouse platforms. In addition, he will look at real-world use cases where Hadoop has helped to produce more business value, augment productivity or identify new and potentially lucrative opportunities.
10 Lessons Learned from Meeting with 150 Banks Across the GlobeDataWorks Summit
This document summarizes 10 practical lessons learned from companies about their big data and analytics journeys:
1. There are clear leaders in each market who are gaining substantial benefits from using big data and machine learning, widening the gap with other companies.
2. Real transformation requires buy-in from top executives, as reflected by new innovation centers, roles, and organizations.
3. Projects should have clear revenue impact objectives and be selected based on estimated return, with pre- and post-implementation measurements.
4. While cost reduction brings the fastest ROI, new revenue opportunities can transform a business more lastingly if the projects address real customer and business needs.
This document provides an overview and crash course on Apache Spark and related big data technologies. It discusses the history and components of Spark including Spark Core, SQL, Streaming, and MLlib. It also discusses data sources, challenges of big data, and how Spark addresses them through its in-memory computation model. Finally, it introduces Apache Zeppelin for interactive notebooks and the Hortonworks Data Platform sandbox for experimenting with these technologies.
It is almost impossible to escape the topic of Data Science. While the core of Data Science has remained the same over the last decade, it’s emergence to the forefront is spurred by both the availability of new data types and a true realization of the value that it delivers. In this session, we will provide an overview of data science, the different classes of machine learning algorithm and deliver an end-to-end demonstration of performing Machine Learning Using Hadoop. Audience: Developers, Data Scientist Architects and System Engineers.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=4175a7421d00257f33df146f50c41af8
Hortonworks Open Connected Data Platforms for IoT and Predictive Big Data Ana...DataWorks Summit
The energy industry is well known to be laggard adopters of new technology. However, industry challenges such as aging assets & workforce, increased regulatory scrutiny, renewable energy sources, depressed commodity prices, changing customer expectations, and growing data volumes are pushing companies to explore new technologies to help solve these problems. Learn how energy companies are leveraging Hortonworks Open and Connected Data Platforms to provide the predictive analysis and data insights to optimize performance for the energy industry.
Speaker
Kenneth Smith, General Manager, Energy, Hortonworks
This document discusses 10 emerging data analytics trends and 5 cooling trends based on an analysis of current technologies and strategies. Emerging trends include self-service BI tools, mobile dashboards, deep learning frameworks like TensorFlow and MXNet, and cloud storage and analysis. Cooling trends include Hadoop due to complexity, batch processing due to lag, and IoT due to security issues. R, Scikit-learn and Jupyter Notebooks are also highlighted as growing in importance.
MammothDB is the first inexpensive enterprise analytics database, offered in the cloud or on-premises.
It's pointless to have big, or even medium sized data, if you don't have the ability to easily use and understand that data. We're making enterprise analytics accessible to every company in the world, particularly the under-served 88% of global companies that don't have enterprise analytics/business intelligence today.
Dr. Stefan Radtke gave a presentation on the journey to big data analytics. He discussed how analytics is affecting many industries and the evolution of analytic questions from descriptive to predictive to prescriptive. He emphasized the need to collect all potential data from both traditional and new sources. A strategic approach was presented that aligns business and IT goals, identifies strategic opportunities, prioritizes use cases, and recommends an analytics roadmap. Dell EMC offers various services to help customers with their big data and analytics initiatives and solutions.
The Impact of SMACT on the Data Management StackSnapLogic
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
With the rise of IoT and the increasing complexity of applications, clouds, networks and infrastructure, the battle to keep your data and your infrastructure safe from attackers is getting harder. As groups of bad actors collaborate, sharing information and offering illegal access, and botnets as a service, terabits of attack can be launched cheaply. Meanwhile, it’s hard to find enough security analysts to catch and prevent these attacks.
This is where community collaboration and open source efforts like Apache Metron come in. Metron presents a comprehensive framework for application and network, security built on Apache Hadoop and open source Streaming Analytics(ie Apache Nifi, Apache Kafka) tool’s highly scalable data management and processing stacks. Advanced features like profiling, machine learning, and visualization work with real-time streaming detection to make your SOC analysts more efficient, while the intrinsic extensibility of open source helps your data scientists get security insights out of the lab and into production fast.
We will discuss and demonstrate how some real-world businesses and managed service providers are using Apache Metron to identify and solve security threats at scale, and some approaches and ideas for how the platform can fit into your security architecture.
Speaker: Laurence Da Luz, Senior Solutions Architect, Hortonworks
Bitkom Cray presentation - on HPC affecting big data analytics in FSPhilip Filleul
High value analytics in FS are being enabled by Graph, machine learning and Spark technologies. To make these real at production scale HPC technologies are more appropriate than commodity clusters.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
We have other engines out there, plenty of them and we have SQL / Java
Language trends, thirst for answers.
Example each task takes a certain amount of time but we want to buffer the time and leave room for Murphy’s law that something will go wrong or take a bit longer. 20% more time. Each unit is independent.
Concise, declarartive but also provides greater description to CPU on how to handle the problem.
Anywhere is the real answer. Here’s a few examples that have been bubbling up among our customers.
Speaker: pick one of these and describe, briefly, an example that you know about in 4-5 sentences. One ‘close’ to the audience of course is the best. There’s separate use case deck by industry in the workshop wiki page that you can use for ideas.
Example from movie: “IDENTITY THEFT”
Some common applications of outlier detection include:
Fraud detection: Purchasing behavior of a credit card owner usually changes when the purchasing behavior of a credit card owner usually changes when the card is stolen and the abnormal buying patterns can indicate fraud.
Medicine: Unusual test results may indicate an underlying health issue
Sports: Exceptional players may appear as outliers in particular parameters and placed in positions where the team can most benefit
Gas, Convenience, retail,
Before we can detect an outlier, we have to define it. The most intuitive definition I’ve seen is from a 1980 book Identification of Outliers (Hawkins):
“An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism”
Speaker: It’s worth repeating that all of these topics a deep enough to have entire university library shelves filled with books on the topic. We’re just skimming the surface.
Anomaly detection is related to clustering, but almost its inverse.
If points that are similar to each other cluster together (representing “normal” behavior or patterns), we want to find the points that are NOT in any cluster.
What: Identify what group a new observation belong in.
This is a simplified visual example, taken from Andrew Ng’s ML class. Plotted are only 2 dimensions (features) from the full feature vector – age and tumor size.
The instances provided are “labeled” – in this case marked in red/x vs. blue/circle.
Note that some red instances are inside the blue cluster, which represents the fact that learning sets sometimes introduce noise, making the learning task not trivial.
Similarly some blue points are inside the red cluster.
Regression is supervised learning where instead of predicting a category (like malignant or benign from the previous examples) we predict a “value” – a number.
In this example (again from Andrew Ng’s class) we are trying to predict the “price of a house” given a single variable: size in feet.
Clearly more complex models in multiple dimensions would be better; for example, we can use other features like “age of house” or “number of previous owners”, “geographic location” or “score for closest public school”
With unsupervised learning, we again have as input a feature matrix with rows as instances and columns as variables, but NO LABELS.
Now the goal is to find a label (cluster number) for each instance, but we are not learning a given function to match, rather trying to figure out the natural way instances may be grouped together.
Note that we are usually NOT given the number of desired cluster (often called “K”), and may need to determine this on our own.
Over-fitting means the model performs very well on the training set but does not generalize well so results on unseen data are poor.
As shown in the diagram, this means the model learned the specific granular details of the training set and not the generic function it was meant to learn.
This is why we “evaluate” on the validation set (and not the training set), because if we measured error on the training set we may get a false sense of performance if the model is over-fitting.
Under-fittingmeans the model doesn’t have enough degrees of freedom to learn the needed model, and usually has a high bias.
Underfitting is often a result of an excessively simple model. In practice you won’t encounter underfitting very often. Data sets that are used for predictive modelling nowadays often come with too many predictors, not too few. Nonetheless, when building any predictive model, you should use validation or cross-validation to assess predictive accuracy and avoid these problems. Here we may have many observations, but too few features (matrix is tall and narrow).