The document discusses how companies can leverage data and analytics to gain competitive advantages. It notes that many companies collect large amounts of data but lack the skills and resources to extract useful insights from it. The document promotes Idiro as a company that can help organizations address common data challenges like too much data to manage, lack of analytical skills, and disparate data sources. Idiro provides tools and expertise to clean, analyze and generate business intelligence from big data to help companies better understand their business and customers.
This document summarizes how several large companies, including Adidas, eBay, and Walmart, are using Neo4j graph databases to gain insights from their data. Adidas uses Neo4j to combine product and content data into a single searchable graph to personalize customer experiences. eBay uses Neo4j to optimize delivery routing as their previous system could not handle growth. Walmart uses Neo4j to provide personalized recommendations to customers on their website. The document then discusses how graph databases are better suited than relational databases for real-time, dynamic queries on connected data. It provides examples of using graph analysis to detect fraud, such as insurance scams and banking fraud rings.
This document discusses how unlocking insights from "dark data" can provide competitive advantages. It notes that only a small fraction of available data is currently used and that cognitive technologies are needed to unlock the potential in all data, including unstructured data sources like social media, IoT sensors, images and video. Specifically, it describes how visual insights from photos and video can provide customer segmentation insights and how combining diverse data sources like transactions, interactions and external events can provide a more complete understanding of customers.
The document discusses how lessons learned from analyzing threats on the battlefield after 9/11 can be applied to analyzing financial risks. It advocates using multi-nodal network analysis and data analytics to identify hidden relationships and risks across large, complex global financial networks, as is done to identify terrorist networks. This approach provides a holistic view of risk exposure and can help mitigate issues like fraud, money laundering, and reputational risks through more efficient identification of linkages. The document argues that if analytics are correctly applied, they can transform the financial industry's ability to manage risk.
Thwart Fraud Using Graph-Enhanced Machine Learning and AINeo4j
This webinar will discuss using graph-enhanced machine learning and AI to thwart fraud. On February 6th, Scott Heath from Expero and Amy Hodler from Neo4j will discuss how graph databases can be used to identify patterns and relationships in complex transactional data to detect fraud. The webinar is part of a series that will also cover building intelligent fraud prevention systems using machine learning and graphs, and obtaining funding for graph-enhanced fraud solutions.
Global Business Intelligence (BI) software vendor, Yellowfin, and Actian Corporation, pioneers of the record-breaking analytical database Vectorwise, will host a series of Big Data and BI Best Practices Webinars.
These are the slides from that presentation.
The Big Data & BI Best Practices Webinars and associated slides examine the phenomenal growth in business data and outline strategies for effectively, efficiently and quickly harnessing and exploring ‘Big Data’ for competitive advantage.
Big Data LDN 2017: Collaborative Data Governance: GDPR Is Only the BeginningMatt Stubbs
1. The document discusses collaborative data governance and how GDPR compliance is just the beginning of ensuring trust and transparency with personal data. It provides an overview of the potential costs of non-compliance and challenges of meeting GDPR deadlines.
2. A demo is described that shows how an organization can achieve GDPR compliance through finding opt-in data, integrating it into a customer 360 view, and being able to prove consent and enable data access and portability.
3. The demo highlights benefits for business through increased revenue and customer intimacy, and for IT through lower costs and faster design, while ensuring compliance.
The document discusses how companies can leverage data and analytics to gain competitive advantages. It notes that many companies collect large amounts of data but lack the skills and resources to extract useful insights from it. The document promotes Idiro as a company that can help organizations address common data challenges like too much data to manage, lack of analytical skills, and disparate data sources. Idiro provides tools and expertise to clean, analyze and generate business intelligence from big data to help companies better understand their business and customers.
This document summarizes how several large companies, including Adidas, eBay, and Walmart, are using Neo4j graph databases to gain insights from their data. Adidas uses Neo4j to combine product and content data into a single searchable graph to personalize customer experiences. eBay uses Neo4j to optimize delivery routing as their previous system could not handle growth. Walmart uses Neo4j to provide personalized recommendations to customers on their website. The document then discusses how graph databases are better suited than relational databases for real-time, dynamic queries on connected data. It provides examples of using graph analysis to detect fraud, such as insurance scams and banking fraud rings.
This document discusses how unlocking insights from "dark data" can provide competitive advantages. It notes that only a small fraction of available data is currently used and that cognitive technologies are needed to unlock the potential in all data, including unstructured data sources like social media, IoT sensors, images and video. Specifically, it describes how visual insights from photos and video can provide customer segmentation insights and how combining diverse data sources like transactions, interactions and external events can provide a more complete understanding of customers.
The document discusses how lessons learned from analyzing threats on the battlefield after 9/11 can be applied to analyzing financial risks. It advocates using multi-nodal network analysis and data analytics to identify hidden relationships and risks across large, complex global financial networks, as is done to identify terrorist networks. This approach provides a holistic view of risk exposure and can help mitigate issues like fraud, money laundering, and reputational risks through more efficient identification of linkages. The document argues that if analytics are correctly applied, they can transform the financial industry's ability to manage risk.
Thwart Fraud Using Graph-Enhanced Machine Learning and AINeo4j
This webinar will discuss using graph-enhanced machine learning and AI to thwart fraud. On February 6th, Scott Heath from Expero and Amy Hodler from Neo4j will discuss how graph databases can be used to identify patterns and relationships in complex transactional data to detect fraud. The webinar is part of a series that will also cover building intelligent fraud prevention systems using machine learning and graphs, and obtaining funding for graph-enhanced fraud solutions.
Global Business Intelligence (BI) software vendor, Yellowfin, and Actian Corporation, pioneers of the record-breaking analytical database Vectorwise, will host a series of Big Data and BI Best Practices Webinars.
These are the slides from that presentation.
The Big Data & BI Best Practices Webinars and associated slides examine the phenomenal growth in business data and outline strategies for effectively, efficiently and quickly harnessing and exploring ‘Big Data’ for competitive advantage.
Big Data LDN 2017: Collaborative Data Governance: GDPR Is Only the BeginningMatt Stubbs
1. The document discusses collaborative data governance and how GDPR compliance is just the beginning of ensuring trust and transparency with personal data. It provides an overview of the potential costs of non-compliance and challenges of meeting GDPR deadlines.
2. A demo is described that shows how an organization can achieve GDPR compliance through finding opt-in data, integrating it into a customer 360 view, and being able to prove consent and enable data access and portability.
3. The demo highlights benefits for business through increased revenue and customer intimacy, and for IT through lower costs and faster design, while ensuring compliance.
By definition, “big data” involves large volumes of diverse data sources.
Considering all the data that your activities generate and that 99% of this data is irrelevant “noise,” business users and stakeholders have to struggle to understand your company’s status.
See how a business perspective on your big, small or just complex data will generate business value.
This document provides an overview of managing big data projects for business results. It discusses introducing big data and the project lifecycle, including planning, development, operation and support, and evaluation phases. Key activities, roles and deliverables are outlined for each phase. The document also covers determining big data opportunities, defining a team structure, types of analytics, key success factors, and concludes with thanks.
Idiro Analytics - Social Network Analysis for Online GamingIdiro Analytics
Idiro is a company that specializes in social network analysis (SNA) and advanced analytics to help online gaming companies. They can use SNA to detect social communities within games and predict player behavior. This allows gaming companies to increase revenue, reduce player defection, and identify cross-sell opportunities. Idiro's SNA tools can analyze player social data to predict which players may stop playing, segment the player base, identify players likely to purchase additional items or services, and find influential players. Understanding relationships between players is key as many players select games based on friends' recommendations. Idiro can help gaming companies apply SNA to better understand players and maximize retention and monetization.
The document defines and discusses the key characteristics of data quality: accuracy, precision, relevance, completeness, consistency, transparency, and timeliness. It provides examples to illustrate each characteristic, defining them as the degree to which data matches reality (accuracy), the specificity of data values (precision), how closely data meets the needs of its consumers (relevance), how fully the needs of consumers are met (completeness), how synchronized data is across systems (consistency), the ability to trace data back to its origin (transparency), and the availability of data when it is needed (timeliness).
1) Large banks are challenged by the vast amounts of data they hold as their most valuable asset, but few know how to effectively analyze and leverage this data.
2) Setting up a "Big Data Factory" can help optimize data processing and analysis across the bank, reducing costs by up to 70% by standardizing data preparation.
3) The factory would provide unified access and analysis of both traditional and non-traditional internal and external data sources to various departments to help with tasks like customer acquisition, risk management, and operations optimization.
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014Findwise
1) The document discusses IBM's Watson cognitive computing system and its applications. It provides an overview of IBM's Watson products and solutions for areas like decision making, discovery, customer engagement, and data exploration.
2) Key Watson applications highlighted include solutions for healthcare treatment, mechanical system repair, and airline management. Watson is also being applied to areas like pharmaceutical research, education, and publishing.
3) The document outlines IBM's vision of integrating cognitive computing capabilities like Watson throughout its data and analytics portfolio to enable insights from all types of data.
Data quality - The True Big Data ChallengeStefan Kühn
The document discusses data quality challenges, especially with big data. It notes that data quality starts at data creation and production, and that both data producers and consumers play a role. With big data, quality issues like redundancy, lack of resolution, and noise are exacerbated due to diverse sources of data, lack of documentation and standards, and increasing volumes of data. The document recommends treating data as a product and implementing quality standards, detection of problems, and root cause analysis to improve quality rather than just collecting more raw data. A shared responsibility approach between business and IT is needed to develop a common understanding of data.
Data Science Applications | Data Science For Beginners | Data Science Trainin...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science Applications" PPT takes you through the various domains in which data science is being deployed today, along with some potential applications of this technology. The world today runs on data and this PPT shows exactly that.
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Evaluating Big Data Predictive Analytics PlatformsTeradata Aster
Mike Gualtieri, Principal Analyst, Forrester Research, presents at the Big Analytics Roadshow, 2012 in New York City on December 12, 2012
Presentation title: Evaluating Big Data Predictive Analytics Platforms
Abstract: Great. You have Big Data. Now what? You have to analyze it to find game-changing predictive models that you can use to make smart decisions, reduce risk, or deliver breakthrough customer experiences. Big Data Predictive Analytics solutions are software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources. In this session, Forrester Principal Analyst Mike Gualtieri will discuss the key criteria you should use to evaluate Big Data Predictive Analytics platforms to meet your specific needs.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
General Data Protection Regulation - BDW Meetup, October 11th, 2017Caserta
Caserta Presentation:
General Data Protection Regulation (GDPR) is a business and technical challenge for companies worldwide - and the deadlines are coming fast! American institutions that do business in the EU or have customers from the EU will have their data practices affected. With this in mind, Caserta – joined by Waterline Data, Salt Recruiting, and Squire Patton Boggs – hosted a BDW Meetup on the GDPR, which is perhaps the most controversial data legislation that has been passed to date.
Joe Caserta, Founding President, Caserta, spoke on the basics of the GDPR, how it will impact data privacy around the world, and some techniques geared towards compliance.
Jump start into 2013 by exploring how Big Data can transform your business. Listen to Infochimps Director of Product, Tim Gasper, cover the leading use cases for 2013, sharing where the data comes from, how the systems are architected and most importantly, how they drive business insights for data-driven decisions.
Slides: Case Study — How J.B. Hunt is Driving Efficiency with AI and Real-Tim...DATAVERSITY
J.B. Hunt, one of the leading providers of transportation and logistics services in North America, recognizes the criticality of customer responsiveness, service quality, and operational efficiency for its success. However, with its data spread across multiple sources, including legacy mainframe systems, the organization was struggling to meet data requirements from multiple departments. They struggled to troubleshoot operational issues and respond to customers quickly.
Join this webinar to hear about the optimized solution J. B. Hunt implemented, which automates real-time data pipelines for a reliable cloud data lake and provides multiple user groups an in-the-moment view of data without overwhelming internal operational systems. Discover how J.B. Hunt now leverages a modernized data environment to accelerate data delivery and drive various AI and analytics initiatives such as real-time service-pricing, competitive counterbidding, and improving their customer experience.
Learn how you can:
• Ingest data in real-time from legacy mainframe systems, enterprise applications, and more
• Create a reliable cloud data lake to accelerate AI and Analytic Initiatives
• Catalog, prepare, and provision data to empower data consumers
• Drive operational efficiency and customer experience with AI-augmented insights
Unlock your Big Data with Analytics and BI on Office 365Brian Culver
Companies have huge amounts of data waiting to be explored. With Azure HDInsights you can realize the value of your data. With Microsoft Excel 2013 and Office 365, you have a complete platform for BI solutions and services. Power BI allows companies to manipulate and study a variety of data points, gain actionable insights and share their insights. PowerPivot, Power View, Power Query, Power Map and Power BI Sites let users analyze and make decisions using structured and unstructured data.
Attendee Takeaways:
1. Learn to setup and configure HDInsights on Microsoft Azure.
2. Understand how to use Excel for BI capabilities.
3. Build a BI Dashboard in Office365.
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
1) Anonymizing data is difficult and often destroys the utility of the data. High-dimensional data like images are nearly impossible to anonymize effectively.
2) Synthetic data generated by AI can provide a statistical representation of real private data while preserving over 99% of the value for analysis. It allows unrestricted sharing and use of data.
3) Case studies on public datasets like the US Census and mobility data show synthetic data matches the real data distribution and statistics closely while being fully private. Synthetic data enables new use cases for data sharing, analytics and innovation.
The document discusses the rise of big data and how organizations are adopting big data solutions. It describes how data has exploded in terms of volume, velocity, and variety. This includes new types of structured, semi-structured, and unstructured data from sources like sensors, social media, and machine logs. Common big data platforms are Hadoop, HBase, MongoDB and data is stored and analyzed in data lakes. The adoption of big data is driven by needs for social intelligence, predictive analytics, complex queries and integrating new data sources. Organizations are adopting big data platforms for archiving, offloading from data warehouses, and advanced analytics on new data types.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
By definition, “big data” involves large volumes of diverse data sources.
Considering all the data that your activities generate and that 99% of this data is irrelevant “noise,” business users and stakeholders have to struggle to understand your company’s status.
See how a business perspective on your big, small or just complex data will generate business value.
This document provides an overview of managing big data projects for business results. It discusses introducing big data and the project lifecycle, including planning, development, operation and support, and evaluation phases. Key activities, roles and deliverables are outlined for each phase. The document also covers determining big data opportunities, defining a team structure, types of analytics, key success factors, and concludes with thanks.
Idiro Analytics - Social Network Analysis for Online GamingIdiro Analytics
Idiro is a company that specializes in social network analysis (SNA) and advanced analytics to help online gaming companies. They can use SNA to detect social communities within games and predict player behavior. This allows gaming companies to increase revenue, reduce player defection, and identify cross-sell opportunities. Idiro's SNA tools can analyze player social data to predict which players may stop playing, segment the player base, identify players likely to purchase additional items or services, and find influential players. Understanding relationships between players is key as many players select games based on friends' recommendations. Idiro can help gaming companies apply SNA to better understand players and maximize retention and monetization.
The document defines and discusses the key characteristics of data quality: accuracy, precision, relevance, completeness, consistency, transparency, and timeliness. It provides examples to illustrate each characteristic, defining them as the degree to which data matches reality (accuracy), the specificity of data values (precision), how closely data meets the needs of its consumers (relevance), how fully the needs of consumers are met (completeness), how synchronized data is across systems (consistency), the ability to trace data back to its origin (transparency), and the availability of data when it is needed (timeliness).
1) Large banks are challenged by the vast amounts of data they hold as their most valuable asset, but few know how to effectively analyze and leverage this data.
2) Setting up a "Big Data Factory" can help optimize data processing and analysis across the bank, reducing costs by up to 70% by standardizing data preparation.
3) The factory would provide unified access and analysis of both traditional and non-traditional internal and external data sources to various departments to help with tasks like customer acquisition, risk management, and operations optimization.
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014Findwise
1) The document discusses IBM's Watson cognitive computing system and its applications. It provides an overview of IBM's Watson products and solutions for areas like decision making, discovery, customer engagement, and data exploration.
2) Key Watson applications highlighted include solutions for healthcare treatment, mechanical system repair, and airline management. Watson is also being applied to areas like pharmaceutical research, education, and publishing.
3) The document outlines IBM's vision of integrating cognitive computing capabilities like Watson throughout its data and analytics portfolio to enable insights from all types of data.
Data quality - The True Big Data ChallengeStefan Kühn
The document discusses data quality challenges, especially with big data. It notes that data quality starts at data creation and production, and that both data producers and consumers play a role. With big data, quality issues like redundancy, lack of resolution, and noise are exacerbated due to diverse sources of data, lack of documentation and standards, and increasing volumes of data. The document recommends treating data as a product and implementing quality standards, detection of problems, and root cause analysis to improve quality rather than just collecting more raw data. A shared responsibility approach between business and IT is needed to develop a common understanding of data.
Data Science Applications | Data Science For Beginners | Data Science Trainin...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science Applications" PPT takes you through the various domains in which data science is being deployed today, along with some potential applications of this technology. The world today runs on data and this PPT shows exactly that.
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Evaluating Big Data Predictive Analytics PlatformsTeradata Aster
Mike Gualtieri, Principal Analyst, Forrester Research, presents at the Big Analytics Roadshow, 2012 in New York City on December 12, 2012
Presentation title: Evaluating Big Data Predictive Analytics Platforms
Abstract: Great. You have Big Data. Now what? You have to analyze it to find game-changing predictive models that you can use to make smart decisions, reduce risk, or deliver breakthrough customer experiences. Big Data Predictive Analytics solutions are software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources. In this session, Forrester Principal Analyst Mike Gualtieri will discuss the key criteria you should use to evaluate Big Data Predictive Analytics platforms to meet your specific needs.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
General Data Protection Regulation - BDW Meetup, October 11th, 2017Caserta
Caserta Presentation:
General Data Protection Regulation (GDPR) is a business and technical challenge for companies worldwide - and the deadlines are coming fast! American institutions that do business in the EU or have customers from the EU will have their data practices affected. With this in mind, Caserta – joined by Waterline Data, Salt Recruiting, and Squire Patton Boggs – hosted a BDW Meetup on the GDPR, which is perhaps the most controversial data legislation that has been passed to date.
Joe Caserta, Founding President, Caserta, spoke on the basics of the GDPR, how it will impact data privacy around the world, and some techniques geared towards compliance.
Jump start into 2013 by exploring how Big Data can transform your business. Listen to Infochimps Director of Product, Tim Gasper, cover the leading use cases for 2013, sharing where the data comes from, how the systems are architected and most importantly, how they drive business insights for data-driven decisions.
Slides: Case Study — How J.B. Hunt is Driving Efficiency with AI and Real-Tim...DATAVERSITY
J.B. Hunt, one of the leading providers of transportation and logistics services in North America, recognizes the criticality of customer responsiveness, service quality, and operational efficiency for its success. However, with its data spread across multiple sources, including legacy mainframe systems, the organization was struggling to meet data requirements from multiple departments. They struggled to troubleshoot operational issues and respond to customers quickly.
Join this webinar to hear about the optimized solution J. B. Hunt implemented, which automates real-time data pipelines for a reliable cloud data lake and provides multiple user groups an in-the-moment view of data without overwhelming internal operational systems. Discover how J.B. Hunt now leverages a modernized data environment to accelerate data delivery and drive various AI and analytics initiatives such as real-time service-pricing, competitive counterbidding, and improving their customer experience.
Learn how you can:
• Ingest data in real-time from legacy mainframe systems, enterprise applications, and more
• Create a reliable cloud data lake to accelerate AI and Analytic Initiatives
• Catalog, prepare, and provision data to empower data consumers
• Drive operational efficiency and customer experience with AI-augmented insights
Unlock your Big Data with Analytics and BI on Office 365Brian Culver
Companies have huge amounts of data waiting to be explored. With Azure HDInsights you can realize the value of your data. With Microsoft Excel 2013 and Office 365, you have a complete platform for BI solutions and services. Power BI allows companies to manipulate and study a variety of data points, gain actionable insights and share their insights. PowerPivot, Power View, Power Query, Power Map and Power BI Sites let users analyze and make decisions using structured and unstructured data.
Attendee Takeaways:
1. Learn to setup and configure HDInsights on Microsoft Azure.
2. Understand how to use Excel for BI capabilities.
3. Build a BI Dashboard in Office365.
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
1) Anonymizing data is difficult and often destroys the utility of the data. High-dimensional data like images are nearly impossible to anonymize effectively.
2) Synthetic data generated by AI can provide a statistical representation of real private data while preserving over 99% of the value for analysis. It allows unrestricted sharing and use of data.
3) Case studies on public datasets like the US Census and mobility data show synthetic data matches the real data distribution and statistics closely while being fully private. Synthetic data enables new use cases for data sharing, analytics and innovation.
The document discusses the rise of big data and how organizations are adopting big data solutions. It describes how data has exploded in terms of volume, velocity, and variety. This includes new types of structured, semi-structured, and unstructured data from sources like sensors, social media, and machine logs. Common big data platforms are Hadoop, HBase, MongoDB and data is stored and analyzed in data lakes. The adoption of big data is driven by needs for social intelligence, predictive analytics, complex queries and integrating new data sources. Organizations are adopting big data platforms for archiving, offloading from data warehouses, and advanced analytics on new data types.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Human in the loop: Bayesian Rules Enabling Explainable AIPramit Choudhary
The document provides an overview of a presentation on enabling explainable artificial intelligence through Bayesian rule lists. Some key points:
- The presentation will cover challenges with model opacity, defining interpretability, and how Bayesian rule lists can be used to build naturally interpretable models through rule extraction.
- Bayesian rule lists work well for tabular datasets and generate human-understandable "if-then-else" rules. They aim to optimize over pre-mined frequent patterns to construct an ordered set of conditional statements.
- There is often a tension between model performance and interpretability. Bayesian rule lists can achieve accuracy comparable to more opaque models like random forests on benchmark datasets while maintaining interpretability.
Part of the ongoing effort with Skater for enabling better Model Interpretation for Deep Neural Network models presented at the AI Conference.
https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
Purpose of this presentation is to highlight how end to end machine learning looks like in real world enterprise. This is to provide insight to aspiring data scientist who have been through courses or education in ML that mostly focus on ML algorithms and not end to end pipeline.
Architecture and components mentioned in Slide 11 will be discussed in detailed in series of post on LinkedIn over the course of next few month
To get updates on this follow me on LinkedIn or search/follow hashtag #end2endDS. Post will be active in August 2019 and will be posted till September 2019
AlgoAnalytics is an analytics consultancy that uses advanced mathematical techniques and machine learning to solve business problems for clients across various industries. It has over 30 data scientists with expertise in mathematics, engineering, and cutting-edge methodologies like deep learning. AlgoAnalytics works closely with domain experts to effectively model problems and develop predictive analytics solutions using structured, text, image, sound, and other types of data. Some of its service offerings include contracts management, document decomposition, sentiment analysis, and predictive maintenance. The company is led by CEO and founder Aniruddha Pant, who has over 20 years of experience applying machine learning and analytics to academic and enterprise challenges.
This document is an overview for an online test series program called "We Create Problems" that aims to help learners develop problem-solving skills and prepare for technical hiring tests. The 10-week test series includes tests on topics like logic, data structures, algorithms, system design and cognitive skills. It provides practice with real hiring test questions, analysis of test papers, and interactions with industry professionals to help learners gain experience and clarity on technical problem-solving.
This document provides an overview and curriculum for an online test series to prepare for technical hiring tests. The 10-week test series includes 8 topic-based tests and 2 combined tests covering topics like logic, algorithms, data structures, system design, problem solving and cognitive skills. The goal is to help learners clear a tech hiring test in 3 attempts by gaining experience with questions commonly asked in online hiring drives and learning essential problem-solving skills. Weekly tests are followed by revision sessions with professionals and webinars bring graduates and hiring managers together.
Explainable AI with H2O Driverless AI's MLI moduleMartin Dvorak
H2O Driverless AI's machine learning interpretability (MLI) module provides explainable AI capabilities. It employs techniques like surrogate models, Shapley values, and LIME to explain both Driverless AI and external models. The MLI module fits into the end-to-end Driverless AI workflow and allows for global and local explanations of model behavior and feature importance to build more human-centered, low-risk models.
The document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
Sara Nash and Urmi Majumder, Principal Consultants at Enterprise Knowledge, presented on April 19, 2023 at KM World in Washington D.C. on the topic of Scaling Knowledge Graph Architectures with AI.
In this presentation, Sara and Urmi defined a Knowledge Graph architecture and reviewed how AI can support the creation and growth of Knowledge Graphs. Drawing from their experience in designing enterprise Knowledge Graphs based on knowledge embedded in unstructured content, Sara and Urmi defined approaches for entity and relationship extraction depending on Enterprise AI maturity and highlighted other key considerations to incorporate AI capabilities into the development of a Knowledge Graph.
View presentation below in order to learn about how:
Assess entity and relationship extraction readiness according to EK’s Extraction Maturity Spectrum and Relationship Extraction Maturity Spectrum.
Utilize knowledge extraction from content to gather important insights into organizational data.
Extract knowledge with three approaches:
RedEx Rule, Auto-Classification Rule, Custom ML Model
Examine key factors such as how to leverage SMEs, iterate AI processes, define use cases, and invest in establishing robust AI models.
Using the power of OpenAI with your own data: what's possible and how to start?Maxim Salnikov
This document provides an overview of a talk by Maxim Salnikov and Jon Jahren at Oslo Spektrum from November 7-9. It discusses using OpenAI with your own data and how to get started. Examples of enterprise use cases for generative AI are presented, such as chatbots, document indexing, and financial analysis. Tools for prompt engineering like LangChain and Semantic Kernel are introduced. Best practices for fine-tuning models on proprietary data are covered, including data formatting, training data size, and an iterative tuning process. Responsible AI techniques like grounding responses and maintaining a positive tone are also discussed.
AI for Customer Service: How to Improve Contact Center Efficiency with Machin...Skyl.ai
About the webinar
It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost, and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity.
Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat, and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value.
What you will learn
- How organizations are building engaging interactions that deliver value to customers
- Best practices to automate AI/ML models
- Demo: How to route customer queries to the right department or professional
Want to learn data analytics or just grab the information about data analytics and its future? https://coursedekho.com/data-analytics-courses-in-surat/
The significance of Data Science has impressively increased over recent years. The contemporary period is the intersection of data analytics with emerging technologies that involve artificial intelligence (AI), machine learning (MI), and automation. And these three things have an ocean of career opportunities. In this post, I am sharing with you some best Data Analytics Courses in Surat, with a detailed course curriculum and placements guarantee.
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This document provides guidance on becoming a data analyst. It defines a data analyst as someone who analyzes raw data to produce meaningful results that help businesses. It recommends learning Excel, SQL, and practicing with sample scenarios like analyzing customer data for banks, insurance companies, schools, and e-commerce sites. The key is to learn Excel commands, SQL basics, and practice regularly with real data sets. With experience using Excel and SQL, one can start as a data analyst intern even without a specific degree.
The need for sophistication in modern search engine implementationsBen DeMott
The need for more sophisticated search implementations is often at odds with the limited feature set available in modern out of the box open source search engines.
This presentation discusses the challenges associated with properly modeling information within a domain and why it's critically needed.
Salesforce Architect Group, Frederick, United States July 2023 - Generative A...NadinaLisbon1
Joined our community-led event to dive into the world of Artificial Intelligence (AI)! Whether you were just starting your AI journey or already familiar with its concepts, one thing was certain: AI was reshaping the future of work. This enablement session was your chance to level up your skills and stay ahead in that rapidly evolving landscape.
As AI news continues to dominate headlines, it's natural to have questions and concerns about its impact on our lives. Will AI take over human jobs? Will it render us obsolete? Rest assured, the outlook is far brighter than you may think. Rather than replacing humans, AI is designed to enhance our capabilities and work alongside us. It won't be replacing marketers, service representatives, or salespeople—it will be empowering them to achieve even greater results. Companies across industries recognize this potential and are embracing AI to unlock new levels of performance.
During this enablement session, you'll have the opportunity to explore how AI advancements can positively influence your professional journey and daily life. We'll debunk common misconceptions, address fears, and showcase real-world examples of how successful AI implementation leads to workforce augmentation rather than replacement. Be prepared to gain valuable insights and practical knowledge that will help you navigate the AI landscape with confidence.
The document provides an introduction to machine learning concepts for product managers, covering topics like supervised learning algorithms, model evaluation metrics, explainability of models, important machine learning terms, and potential applications of machine learning like predictive analytics, anomaly detection, and churn prediction. It also discusses challenges like data cleaning, bias, hyperparameter optimization, overfitting, and data leakage.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
Similar to Big Data LDN 2017: Cognitive Search & Analytics – Bringing the Power of AI to Enterprise Search (20)
Blueprint Series: Banking In The Cloud – Ultra-high Reliability ArchitecturesMatt Stubbs
This document discusses the challenges of building reliable banking architectures in the cloud and how Starling Bank addressed this issue. It introduces some key concepts like distributed architectures, self-contained systems, and the DITTO architecture which focuses on idempotency and eventual consistency. The benefits of this approach for Starling Bank included safe instance termination, continuous delivery of backend changes up to 5 times a day using chat-ops releases, and the ability to "chaos test" to ensure reliability.
Speed Up Your Apache Cassandra™ Applications: A Practical Guide to Reactive P...Matt Stubbs
Speaker: Cedrick Lunven, Developer Advocate, DataStax
Speaker Bio: Cedrick is a Developer Advocate at DataStax where he finds opportunities to share his passions by speaking about developing distributed architectures and implementing reference applications for developers. In 2013, he created FF4j, an open source framework for Feature Toggle which he still actively maintains. He is now contributor in JHipster team.
Talk Synopsis: We have all introduced more or less functional programming and asynchronous operations into our applications in order to speed up and distribute treatments (e.g., multi-threading, future, completableFuture, etc.). To build truly non-blocking components, optimize resource usage, and avoid "callback hell" you have to think reactive—everything is an event.
From the frontend UI to database communications, it’s now possible to develop Java applications as fully reactive with frameworks like Spring WebFlux and Reactor. With high throughput and tunable consistency, applications built on top of Apache Cassandra™ fit perfectly within this pattern.
DataStax has been developing Apache Cassandra drivers for years, and in the latest version of the enterprise driver we introduced reactive programming.
During this session we will migrate, step by step, a vanilla CRUD Java service (SpringBoot / SpringMVC) into reactive with both code review and live coding. Bring home a working project!
Filmed at Skills Matter/Code Node London on 9th May 2019 as part of the Big Data LDN Meetup Blueprint Series.
Meetup sponsored by DataStax.
Blueprint Series: Expedia Partner Solutions, Data PlatformMatt Stubbs
Join Anselmo for an engaging overview of the new end-to-end data architecture at Expedia Group, taking a journey through cloud and on-prem data lakes, real-time and batch processes and streamlined access for data producers and consumers. Find out how the new architecture unifies a complex mix of data sources and feeds the data science development cycle. Expedia might appear to be a market-leading travel company – in reality, it’s a highly successful technology and data science company.
Blueprint Series: Architecture Patterns for Implementing Serverless Microserv...Matt Stubbs
Richard Freeman talks about how the data science team at JustGiving built KOALA, a fully serverless stack for real-time web analytics capture, stream processing, metrics API, and storage service, supporting live data at scale from over 26M users. He discusses recent advances in serverless computing, and how you can implement traditionally container-based microservice patterns using serverless-based architectures instead. Deploying Serverless in your organisation can dramatically increase the delivery speed, productivity and flexibility of the development team, while reducing the overall running, DevOps and maintenance costs.
Big Data LDN 2018: DATABASE FOR THE INSTANT EXPERIENCEMatt Stubbs
Date: 14th November 2018
Location: Customer Experience Theatre
Time: 12:30 - 13:00
Speaker: David Maitland
Organisation: Redis Labs
About: This session will cover the technology underpinning at the software infrastructure level required to deliver the instant experience to the end user and enterprises alike. Use cases and value derived by major brands will be shared in this insightful session based the world's most loved database REDIS.
Big Data LDN 2018: BIG DATA TOO SLOW? SPRINKLE IN SOME NOSQLMatt Stubbs
Date: 14th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Perry Krug
Organisation: Couchbase
About: Who wants to see an ad today for the shoes they bought last week? Everyone knows that customer experience is driven by data: don't waste an opportunity to get them the right data at the right time. Real-time results are critical, but raw speed isn't everything: you need power and flexibility to react to changes on the fly. Come learn how market-leading enterprises are using Couchbase as their speed layer for ingestion, incremental view and presentation layers alongside Kafka, Spark and Hadoop to liberate their data lakes.
Big Data LDN 2018: ENABLING DATA-DRIVEN DECISIONS WITH AUTOMATED INSIGHTSMatt Stubbs
Date: 13th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Charlotte Emms
Organisation: seenit
About: How do you get your colleagues interested in the power of data? Taking you through Seenit’s journey using Couchbase's NoSQL database to create a regular, fully automated update in an easily digestible format.
Big Data LDN 2018: DATA MANAGEMENT AUTOMATION AND THE INFORMATION SUPPLY CHAI...Matt Stubbs
Date: 14th November 2018
Location: Governance and MDM Theatre
Time: 10:30 - 11:00
Speaker: Mike Ferguson
Organisation: IBS
About: For most organisations today, data complexity has increased rapidly. In the area of operations, we now have cloud and on-premises OLTP systems with customers, partners and suppliers accessing these applications via APIs and mobile apps. In the area of analytics, we now have data warehouse, data marts, big data Hadoop systems, NoSQL databases, streaming data platforms, cloud storage, cloud data warehouses, and IoT-generated data being created at the edge. Also, the number of data sources is exploding as companies ingest more and more external data such as weather and open government data. Silos have also appeared everywhere as business users are buying in self-service data preparation tools without consideration for how these tools integrate with what IT is using to integrate data. Yet new regulations are demanding that we do a better job of governing data, and business executives are demanding more agility to remain competitive in a digital economy. So how can companies remain agile, reduce cost and reduce the time-to-value when data complexity is on the up?
In this session, Mike will discuss how companies can create an information supply chain to manufacture business-ready data and analytics to reduce time to value and improve agility while also getting data under control.
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 12:30 - 13:00
Organisation: Immuta
About: Artificial intelligence is rising in importance, but it’s also increasingly at loggerheads with data protection regimes like the GDPR—or so it seems. In this talk, Sophie will explain where and how AI and GDPR conflict with one another, and how to resolve these tensions.
Big Data LDN 2018: REALISING THE PROMISE OF SELF-SERVICE ANALYTICS WITH DATA ...Matt Stubbs
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 11:50 - 12:20
Speaker: Mark Pritchard
Organisation: Denodo
About: Self-service analytics promises to liberate business users to perform analytics without the assistance of IT, and this in turn promises to free IT to focus on enhancing the infrastructure.
Join us to learn how data virtualization will allow you to gain real-time access to enterprise-wide data and deliver self-service analytics. We will explore how you can seamlessly unify fragmented data, replace your high-maintenance and high cost data integrations with a single, low-maintenance data virtualization layer; and how you can preserve your data integrity and ensure data lineage is fully traceable.
Big Data LDN 2018: TURNING MULTIPLE DATA LAKES INTO A UNIFIED ANALYTIC DATA L...Matt Stubbs
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 11:10 - 11:40
Organisation: TIBCO
About: The big data phenomenon continues to accelerate, resulting in multiple data lakes at most organisations. However, according to Gartner, “Through 2019, 90% of the information assets from big data analytic efforts will be siloed and unusable across multiple business processes.”
Are you ready to unleash this data from these silos and deliver the insights your organisation needs to drive compelling customer experiences, innovative new products and optimized operations? In this session you will learn how to apply data virtualisation to: - Access, transform and deliver data from across your lakes, clouds and other data sources - Empower a range of analytic users and tools with all the data they need - Move rapidly to a modern and flexible data architecture for the long run In addition, you will see a demonstration of data virtualisation in action.
Big Data LDN 2018: MICROSOFT AZURE AND CLOUDERA – FLEXIBLE CLOUD, WHATEVER TH...Matt Stubbs
Microsoft and Cloudera have partnered to help customers realize insights from big data using cloud services. With Cloudera Enterprise deployed on Azure, customers can visualize data with Power BI and gain insights within minutes. Cloudera provides solutions for data warehousing, data science, and hybrid deployments that fulfill enterprise requirements around flexibility, manageability, and security on Azure.
Big Data LDN 2018: CONSISTENT SECURITY, GOVERNANCE AND FLEXIBILITY FOR ALL WO...Matt Stubbs
The document discusses Cloudera's Shared Data Experience (SDX) which provides consistent security, governance and flexibility for workloads both on-premises and in the cloud. SDX offers a common set of services including security, governance, lifecycle management and data cataloging that can be shared across different workloads regardless of deployment location. This addresses challenges of managing multiple isolated clusters and allows for easier data sharing and reuse across applications. SDX provides a single source of truth for data through its shared services.
Big Data LDN 2018: MICROLISE: USING BIG DATA AND AI IN TRANSPORT AND LOGISTICSMatt Stubbs
Date: 14th November 2018
Location: Data-Driven Ldn Theatre
Time: 11:10 - 11:40
Organisation: Microlise
About: Microlise are a leading provider of technology solutions to the transport and logistics industry worldwide. Discover how, with over 400,000 connected assets generating billions of messages a day, Microlise is evolving its platform to bring real-time analytics to its customers to improve safety, security and efficiency outcomes.
Big Data LDN 2018: EXPERIAN: MAXIMISE EVERY OPPORTUNITY IN THE BIG DATA UNIVERSEMatt Stubbs
Date: 14th November 2018
Location: Data-Driven Ldn Theatre
Time: 10:30 - 11:00
Speaker: Anna Matty
Organisation: Experian
About: Today there is a widespread focus on the 'how' in relation to problem solving. How can we gain better knowledge of what consumers want, or need? How can we be more efficient, reduce the cost to serve, or grow the lifetime value of a customer? But, how do you move to a place where you are not only solving a problem, you are redesigning the entire strategic potential of that problem? You are being armed with insight on what the problem is.
Data and innovation offer huge potential to revolutionise all markets. There is an opportunity to be one step ahead of the need, to redesign journeys and enhance enterprise strategies. To do this you need access to the most advanced analytics but also the best quality, including variations and types of data, and then the technology that can act on this insight. Data science can present a unique opportunity for uncovered growth and accelerate your business through strategic innovation – fast. In this session you will hear more about how today's analytics can move from a single task, to an ongoing strategic opportunity. An opportunity that helps you move at the speed of the market and helps you maximise every opportunity.
Big Data LDN 2018: A LOOK INSIDE APPLIED MACHINE LEARNINGMatt Stubbs
Date: 13th November 2018
Location: Data-Driven Ldn Theatre
Time: 13:10 - 13:40
Speaker: Brian Goral
Organisation: Cloudera
About: The field of machine learning (ML) ranges from the very practical and pragmatic to the highly theoretical and abstract. This talk describes several of the challenges facing organisations that want to leverage more of their data through ML, including some examples of the applied algorithms that are already delivering value in business contexts.
Big Data LDN 2018: DEUTSCHE BANK: THE PATH TO AUTOMATION IN A HIGHLY REGULATE...Matt Stubbs
Date: 13th November 2018
Location: Data-Driven Ldn Theatre
Time: 12:30 - 13:00
Speaker: Paul Wilkinson, Naveen Gupta
Organisation: Cloudera
About: Investment banks are faced with some of the toughest regulatory requirements in the world. In a market where data is increasing and changing at extraordinary rates the journey with data governance never ends.
In this session, Deutsche Bank will share their journey with big data and explain some of the processes and techniques they have employed to prepare the bank for today’s challenges and tomorrow’s opportunities.
Brought to you by Naveen Gupta, VP Software Engineering, Deutsche Bank and Paul Wilkinson, Principal Solutions Architect, Cloudera.
Big Data LDN 2018: FROM PROLIFERATION TO PRODUCTIVITY: MACHINE LEARNING DATA ...Matt Stubbs
Date: 14th November 2018
Location: Self-Service Analytics Theatre
Time: 13:50 - 14:20
Speaker: Stephanie McReynolds
Organisation: Alation
About: Raw data is proliferating at an enormous rate. But so are our derived data assets - hundreds of dashboards, thousands of reports, millions of transformed data sets. Self-service analytics have ensured that this noise is making it increasingly hard to understand and trust data for decision-making. This trust gap is holding your organisation back from business outcomes.
European analytics leaders have found a way to close the gap between data and decision-making. From MunichRe to Pfizer and Daimler, analytics teams are adopting data catalogues for thousands of self-service analytics users.
Join us in this session to hear how data catalogues that activate data by incorporating machine learning can:
• Increase analyst productivity 20-40%
• Boost the understanding of the nuances of data and
• Establish trust in data-driven decisions with agile stewardship
Big Data LDN 2018: DATA APIS DON’T DISCRIMINATEMatt Stubbs
Date: 13th November 2018
Location: Self-Service Analytics Theatre
Time: 15:50 - 16:20
Speaker: Nishanth Kadiyala
Organisation: Progress
About: The exploding API economy, combined with an advanced analytics market projected to reach $30 billion by 2019, is forcing IT to expose more and more data through APIs. Business analysts, data engineers, and data scientists are still not happy because their needs never really made it into the existing API strategies. This is because most APIs are designed for application integration, but not for the data workers who are looking for APIs that facilitate direct data access to run complex analytics. Data APIs are specifically designed to provide that frictionless data access experience to support analytics across standard interoperable interfaces such as OData (REST) or ODBC/JDBC (SQL). Consider expanding your API strategy to service the developers with open analytics in this $30 billion market.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Open Source Contributions to Postgres: The Basics POSETTE 2024
Big Data LDN 2017: Cognitive Search & Analytics – Bringing the Power of AI to Enterprise Search
1. 1
COGNITIVE SEARCH & ANALYTICS
BRINGING THE POWER OF AI
TO ENTERPRISE SEARCH
Gengis BIRSEN, Senior Solution Consultant
BIRSEN Gengis, Senior Solutions Consultant
The Cognitive Search and Analytics Platform
6. 6
And in the business world…
Identifying Business Experts Detect Money laundering schemes Recommendation System
7. 7
How do we use AI at business level?
DATA
LOTS OF IT
SUITABLE
8. 8
Using enterprise data can be daunting
• Connect to data,
• Understand it,
• Index it,
• Secure it,
• Clean it,
• Enrich it,
• Search it,
• Analyse it.
To leverage it we need to
9. 9
Cognitive search can tackle the data challenge
Find, extract
Connect to various
sources of data
Refine
Structure unstructured
data using NLP and ML
Distribute
Immediate and
secure
Distribute
On all devices
Connect to all Data
Using the 150+ connectors for
structured and unstructured
data sources
Analyze the data
132 languages supported, 21 with advanced
NLP developed over 20 years, augmented
by Machine Learning
Get a unique perspective
Sinequa UI or Search APIs
Quick Time to value
Quickly deployed & Highly
scalable
10. 10
The Combination makes the difference!
Technologies are applied in combination –
not simply in parallel
Each technology enriches the others, so the
end result is more than the sum of its parts
11. 11
Platform
COGNITIVE
ANALYTICS
Natural Language Processing
Statistical Analysis Semantic Extractors
Machine Learning
Sinequa Algorithms
150+
SMART
CONNECTORS
Directories
Social Networks
Archives
Cloud Sources
Websites/Intranet
Databases
BI/Data Lake E-mails
CMS/ERP/CRMApplications
LOGICAL DATA WAREHOUSE
INSIGHT
GENERATION
SBA Studio Global Business API Global Analytics API
Data
Scientists
End
Users
Enterprise
Applications
External SPARK
Cluster
HADOOP
Data Lake
12. 12
Language Recognition
Part-of-speech Tagging / Lemmatization
Concept Extraction
Named Entities (people, places, e-mails, etc.)
Text Mining Agents (date, plate #, amount, phone, etc.)
Natural Language Processing
• struck (strike, verb)
• insured (insure, adj.)
• client (noun)
• Adam (first name)
• Johnson (unknown)
Insurance reference: A 45 65 45
Insuree: Adam Johnson
Dear Adjuster,
On October 15, 2005, my 2001 Honda Civic, license plate VML085,
was struck by your insured client Adam Johnson’s 2002 Volkswagen
Jetta, license plate ED386K, at the corner of (…) in New York City.
My medical bills totaled $3,450 as follows (copies of bills attached).
(...)
I have lost wages in the amount of $1000. I have had considerable
pain and suffering as a result of this accident and continue to suffer
from neck and back pain. I demand settlement of my claim in the
amount of $25,000.
Please respond to this demand with an offer to settle within 15 days.
Thank you.
Sincerely,
Joe Smith
GSM: 1(404) 456 123
joe.smith@mail.mail
• struck (strike, verb)
• insured (insure, adj.)
• client (noun)
• Adam (first name)
• Johnson (unknown)
Insurance reference: A 45 65 45
Insuree: Adam Johnson
Dear Adjuster,
On October 15, 2005, my 2001 Honda Civic, license plate VML085,
was struck by your insured client Adam Johnson’s 2002 Volkswagen
Jetta, license plate ED386K, at the corner of (…) in New York City.
My medical bills totaled $3,450 as follows (copies of bills attached).
(...)
I have lost wages in the amount of $1000. I have had considerable
pain and suffering as a result of this accident and continue to suffer
from neck and back pain. I demand settlement of my claim in the
amount of $25,000.
Please respond to this demand with an offer to settle within 15 days.
Thank you.
Sincerely,
Joe Smith
GSM: 1(404) 456 123
joe.smith@mail.mail
13. 13
Machine learning: Pros and Cons, When to Use
You have a defined task
to perform
A good set of logical rules will always do
what I want
ML is a good option when you have a defined use
case, such as automating a process or a human
task (Image recognition, categorization, etc.)
You have large, clean
data sets, and time to
experiment
You already have a set of
rules which can perform
the desired task
You do not have time to
learn a model or need
explanation of results
If there is no set of rules or if the set
of rules is too complex, then ML is
recommended
ML requires large and clean data sets
to efficiently train a model.
ML involves training a model to perform
a task. This requires experimentation
and testing
ML is good to accomplish something
specific. You cannot “want to use ML”
Training a ML model takes many
iterations and each training iteration
requires large amount of time and
computing resources
ML models are not easily understandable by humans.
They rely on huge amount of dimensions and weights.
There is no logical rule which can be explained
Supervised algorithms require a
training set which is representative of
the entire corpus
14. 14
Sinequa is a Natural Fit for Machine Learning
While ML naturally enhances Sinequa native functionalities, Sinequa indexing and NLP capabilities also naturally fit with ML
projects
Sinequa
Enriched Index
Sinequa
ML Platform
Sinequa Cognitive
Functionalities
Algorithms feed from indexes content and Sinequa
NLP & indexing functions:
• Multi Languages Tokenizers & Stop word
removers
• Lemmas & Part of Speech tagging
ML enhances Sinequa native functionalities:
• Learning to Rank
• Collaborative filtering
• Query Expansion
• Auto-completion
ML algorithms’ output further enhances Sinequa
indexes:
• New Hierarchies (Auto Classification,
Clustering)
• New Concepts/Entities (Topic Detection, NER)
Usage feedback further enhances ML capabilities
• Search queries, filtering activities
• User content ratings, labeling
• User reinforcement feedback
15. 15
Sinequa ML Packaged Algorithms
Sinequa embeds algorithms out-of-the-box, ready for use on your data
Requires a curated training data set
Classification
Model learns to auto-classify
documents, from a labelled
training set
Clustering
Model identifies several document
clusters and place each document in
one cluster
Topic detection
Model identifies a fixed number of
topics from text, each described by
keywords. Model then distribute
documents across topics
Regression
Model identifies correlation and
patterns in a learning data set and
later apply these to predict variables
Learning to Rank
Re-organizes the engine’s results
by learning a user-specific
relevancy
Custom Algorithms
Data scientist can create their own
custom model in the language of
their choice (Python, Scala, Java,
etc.), using Sinequa native features
(Tokenizers, Lemmas, Stop Word
detection, etc.)
Key-Phrase
Extraction
Uses a corpus coherency to extract
key-words and key-sentences from
any document
Query expansion
and auto-completion
Analyzes the logs and users profile to
suggest query expansions and / or
auto-completion
Recommends related document by
content (content-based) or by
examining users’ interactions
(collaborative filtering)
Recommendation
Engine
Named Entity
Recognition
Model learns textual patterns
associated to entities from a tagged
training set. Model can then detect
new entities candidates
Relevance Feedback
Model
Model learns from user’s
activity and use M-L computed
document similarity, to fine tune
documents relevance
Similarity
Algorithm identifies documents
with similar features
Extends Existing Sinequa
Functionality
Provides New Capabilities
16. 16
Evolution of a cognitive insight platform
Connect data and extract
entities (ontologies)
Tune relevancy/retrieval
on business purpose
(configuration)
Further enhance relevancy
on user feedback
Leverage Insight via
Analytics (i.e Data
Science)
Access information from
different channels/UIs
(public web, Q&A -
chatbot, …)
Uncover additional value
(i.e Find the Expert)
18. 18
Bank Fraud
Question : Can we use Banking transaction history
and data to identify abnormal activity (outliers)?
Data:
• Structured: transactions, ammounts, deposits,…
• Unstructured: Labels, Accounts details
Approach:
Clustering of account behavior over a time window