Presented at the annual petrophysics software (SPWLA) show in Houston, TX, by Mark Kerzner. How Oil & Gas should approach Big Data, and how Elephant Scale can help in training and implementation.
Big Data is regularly in the news with claims that that it will improve decision making and support the development of artificial intelligence.
The defence training and simulation community could also exploit these advances, but the data that it does have is typically locked away in disparate unconnected proprietary systems and as such is not “big”.
What might the opportunities and challenges be if such stovepiping was overcome?
Disaster Tech: What is working and what is comingguestf8e7a8
Twitter and Google Maps are being used in mainstream emergency management, and projects like InSTEDD will push them even farther. This session shows you what is working, what isn't, and what's next in Disaster Tech.
ExcelR is a proud partner of Universiti Malaysia Saravak (UNIMAS), Malaysia’s 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings.
ExcelR is a proud partner of Universiti Malaysia Saravak (UNIMAS), Malaysia’s 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings.
ExcelR is a proud partner of University Malaysia Sarawak (UNIMAS), Malaysia's 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings. Participants will be awarded Data Science International Certification from UNIMAS after successfully clearing the online examination.
Big Data is regularly in the news with claims that that it will improve decision making and support the development of artificial intelligence.
The defence training and simulation community could also exploit these advances, but the data that it does have is typically locked away in disparate unconnected proprietary systems and as such is not “big”.
What might the opportunities and challenges be if such stovepiping was overcome?
Disaster Tech: What is working and what is comingguestf8e7a8
Twitter and Google Maps are being used in mainstream emergency management, and projects like InSTEDD will push them even farther. This session shows you what is working, what isn't, and what's next in Disaster Tech.
ExcelR is a proud partner of Universiti Malaysia Saravak (UNIMAS), Malaysia’s 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings.
ExcelR is a proud partner of Universiti Malaysia Saravak (UNIMAS), Malaysia’s 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings.
ExcelR is a proud partner of University Malaysia Sarawak (UNIMAS), Malaysia's 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings. Participants will be awarded Data Science International Certification from UNIMAS after successfully clearing the online examination.
A short presentation indicating LR Senergy's proposed workflow for quality assurance and quality control of mercury injection capillary pressure data and its subsequent interpretation for input to static reservoir models.
Core analysis data results are often an interpretation of the measured data, not a direct result of the measurements themselves. Interpretation is a subjective process and as such, data should never be merely accepted and implemented. It must always be considered and interpreted by the end user, to determine agreement with the original interpretation.
This short slide show provides LR Senergy's suggested generic approach to QA/QC of MICP data.
Operationalizing Big Data to Reduce Risk of High Consequence Decisions in Com...OAG Analytics
This white paper presents compelling alternatives to bivariate analysis, i.e. XY or scatter plots, for generating data-driven insights that can reduce risk in complex systems. It explores under what conditions businesses maximize value by relying on computers to make decisions versus using computers to help humans make better and/or faster decisions. The main body of the paper attempts to create a holistic view of why and how to use contemporary data technologies to create actionable insights from large and complex data. The Technical Appendix elaborates on the requisite capabilities of an end-to-end workflow to transform raw data into actionable insights using advanced analytics.
Workflow for evaluation and interpretation of MICP data.
Data should not be merely accepted and entered to models without prior considered quality assurance and quality control of the data. MICP data, likewise, must be controlled be use in models
BE&GG, Agnis Noviani Noor, Hapzi Ali, Business Ethics & Good Governance ; Ph...Agnis Noviani Noor
Implementasi Philosophical Ethics and Business Di Indonesia Serta Kaitannya Dengan Business Ethics & GG dan Resume / Rekomendasi dari tema "Philosophical Etics and Business"
Top 10 Data Science Practitioner PitfallsSri Ambati
Top 10 Data Science Practitioner Pitfalls Meetup with Erin LeDell and Mark Landry on 09.09.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
A short presentation indicating LR Senergy's proposed workflow for quality assurance and quality control of mercury injection capillary pressure data and its subsequent interpretation for input to static reservoir models.
Core analysis data results are often an interpretation of the measured data, not a direct result of the measurements themselves. Interpretation is a subjective process and as such, data should never be merely accepted and implemented. It must always be considered and interpreted by the end user, to determine agreement with the original interpretation.
This short slide show provides LR Senergy's suggested generic approach to QA/QC of MICP data.
Operationalizing Big Data to Reduce Risk of High Consequence Decisions in Com...OAG Analytics
This white paper presents compelling alternatives to bivariate analysis, i.e. XY or scatter plots, for generating data-driven insights that can reduce risk in complex systems. It explores under what conditions businesses maximize value by relying on computers to make decisions versus using computers to help humans make better and/or faster decisions. The main body of the paper attempts to create a holistic view of why and how to use contemporary data technologies to create actionable insights from large and complex data. The Technical Appendix elaborates on the requisite capabilities of an end-to-end workflow to transform raw data into actionable insights using advanced analytics.
Workflow for evaluation and interpretation of MICP data.
Data should not be merely accepted and entered to models without prior considered quality assurance and quality control of the data. MICP data, likewise, must be controlled be use in models
BE&GG, Agnis Noviani Noor, Hapzi Ali, Business Ethics & Good Governance ; Ph...Agnis Noviani Noor
Implementasi Philosophical Ethics and Business Di Indonesia Serta Kaitannya Dengan Business Ethics & GG dan Resume / Rekomendasi dari tema "Philosophical Etics and Business"
Top 10 Data Science Practitioner PitfallsSri Ambati
Top 10 Data Science Practitioner Pitfalls Meetup with Erin LeDell and Mark Landry on 09.09.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Machine Learning on Big Data with HADOOPEPAM Systems
Machine learning is definitely an exciting application
that helps you to tap on the power of big
data. As for corporate data continues to grow
bigger and more complex, machine learning will
become even more attractive. The industry has
come up elegant solutions to help corporations
to solve this problem. Let’s get ready; it is just a
matter time this problem arrives at your desk.
What you need to know to start an AI company?Mo Patel
An overview of why AI and Deep Learning are hot now? Overview f Machine Intelligence startups. What are the key ingredients for AI startup? How can AI startups compete with big tech companies and areas to focus on for differentiation?
DataRobot 머신러닝 자동화 플랫폼은 전 세계 Top Data Scientist 들의 지식, 경험 및 모범 사례를 바탕으로 최고 수준의 자동화와 사용 편리성을 확보한 가장 진보된 머신러닝 자동화 솔루션 입니다. DataRobot을 통해 비즈니스 관계자, 분석가 및 데이터 과학자 등 기술 수준과 관계 없이 모든 사용자가 기존 모델링 기법에 비해 아주 빠르게, 매우 정확한 예측 모델을 수립하고 구축, 관리할 수 있습니다.
Smart Data - The Foundation for Better Business OutcomesDATAVERSITY
This webinar will explore emerging technologies that enable a new generation of intelligent applications and enterprise systems. It will also act as a roadmap for evaluating and integrating these technologies and practices, and set the stage for our 2016 series of Smart Data webinars.
In the last few years, we have witnessed an AI renaissance with significant advances in areas such as machine-learning/deep learning, natural language processing, and biologically-inspired processor architectures. Simultaneously, the rise of the Industrial Internet of Things - which together with the “traditional” Internet form the Internet of Everything – foreshadows a connected world of smarter homes, cities, and even business relationships.
These “cognitive connections” are supported by advanced analytics and smart data. Join the discussion to see how you and your organization can benefit from getting started now.
Paperspace is the cloud AI-platform built for the future. Tens of thousands of individuals, startups and enterprises use Paperspace to power a range of next-generation applications. Gradient° by Paperspace is a deep learning platform built for developers. From exploration to production deployment, Gradient° enables individuals and teams to quickly develop and collaborate on deep learning models. Join over a hundred thousand developers on the platform and enjoy 1-click
Jupyter notebooks, prebuilt templates, a python library, and powerful low-cost GPUs.
This is an intermediate level AI discussion. We'll be discussing what Artificial Intelligence is, the problems that AI is trying to solve, and some use cases for deploying the technologies. We will then discuss tools and methodologies for AI including IBM PowerAI Enterprise and H2O AI software. Also discussed will be some of the challenges organizations might experience getting AI initiatives off the ground, and software and hardware requirements for getting started.
Introduction to Machine Learning on Mobile: Mobile Week SFAmazon Web Services
AWS Mobile Week at the San Francisco Loft
Introduction to Machine Learning on Mobile
Level: Beginner
Speaker: Dennis Hills - Developer Advocate, AWS Mobile Applications
New way to learn Machine Learning with AWS DeepLens & Daniel ZivKovicDaniel Zivkovic
Heavily modified & personalized AWS re:Invent 2017 DeepLens workshop slide deck. Prepared for AWS User Group Toronto, presented on September 27, 2018 meetup.
Solve for X with AI: a VC view of the Machine Learning & AI landscapeEd Fernandez
What you'll get from this deck
1. The M&A race for AI: by the numbers
2. Watch out! hype ahead: definitions & disclaimers
3. Machine Learning drivers: why is Machine Learning a ‘thing’ now (vs before)
4. Venture Capital: forming an industry, the AI/ML landscape
5. The One Hundred (+13) AI startups to watch in the Enterprise
6. The great Enterprise pivot: applying Machine Learning at scale
7. - where to go next -
This is a course in development. Here is a webinar about it: https://www.youtube.com/watch?v=7vsoZLOtSdY&t=773s.
Our next step is to prepare a "Teacher's Companion" set of slides so that anybody could teach it, to any audience.
Here are some tips on hiring and retaining top Big Data talent. Features : how to source candidates, how to interview them, interview techniques and mistakes.
Listen to video of presentation and download slides here : http://elephantscale.com/2017/03/building-successful-big-data-team-demand-webinar/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...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 the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
17. Machine learning and AI
Machine Learning "is an algorithm that learns from data"
Usually improves its performance with more data.
Uses statistical / mathematical techniques to build a model
from observed data rather than relying on explicit instructions
“More data usually beats better algorithms”
– Anant Rajaraman said it first (?)
• Amazon Retail Platform (25% US transactions)
• WalmartLabs/Kosmix
• Etc.
17
18. What is deep learning?
– Neural networks with more than one hidden layer
Rebranded neural net with some twists
Reemerging due to cluster computing and GPU
Steps towards Artificial Intelligence (AI)
Examples (all world titles)
– Facebook Deep Face
– Google Translate
– Google DeepMind playing GO game
– IBM Deep Blue winning Jeopardy
Latest: Deep Learning
18
(c)
Elephant
Scale.co
m 2016.
All
rights
reserved
19. Modeled loosely after the human brain
Designed to recognize patterns
Input comes from sensory data
– machine perception
– labeling
– clustering raw input
Recognized patterns
– Numerical
– Contained in vectors
– Translated from real-world data
Images, Sound, Text, Time series
Popular in 80s
Fell out of favor in 90s in 2000s as statistical based methods
yielded better results
Came back with a vengeance
Neural Networks
19
21. Our publications
Hadoop illuminated book
HBase Design Patterns book
O’Reilly Data Analytics course
(c) ElephantScale.com 2016. All rights reserved. 21
