The document summarizes Dean Abbott's presentation on interpreting health club member surveys. It describes using factor analysis to reduce 57 survey questions into 10 factors, then regression analysis to identify the 5 most predictive factors for member satisfaction. Rather than using the full factors, the final model used the top-loading question from each factor for improved performance and interpretability. This provided actionable insights for understanding member satisfaction.
A More Transparent Interpretation of Health Club Surveys (YMCA)Salford Systems
The document summarizes Dean Abbott's presentation on interpreting health club member surveys. It describes two approaches: 1) A traditional statistical analysis using frequencies, tests, and measures of central tendency, and 2) Abbott's recommended solution using data mining techniques like factor analysis and predictive modeling. Factor analysis reduced the dimensionality of the survey data by identifying underlying key factors. Predictive models were then created to link these factors to membership satisfaction, renewal likelihood, and recommendations.
This document discusses principles for software effort estimation. It begins with an introduction stating that successful estimation is critical but often projects are over or under estimated. It then discusses 12 principles related to questions about effort estimation. The principles include using domain experts, outlier pruning, combining superior solo methods, and using relevancy filtering when local data is lacking. The document advocates experimentation to determine the best practices and notes that size attributes are not always necessary. It promotes methods to reduce data needs through outlier and synonym pruning.
April Webinar: Sample Balancing in 2012Research Now
How to set and manage your sample balancing options to ensure quality data and happy clients.
Presentation by: Carter Cathey, Vice President, Excellence Initiatives
The document discusses a study that examined differences in motivation and computer proficiency between daily computer users. The study hypothesized that extrinsically motivated proficient users would have more difficulty with unfamiliar computer tasks compared to intrinsically motivated users. The study involved administering a motivation inventory to over 130 participants from various countries and ages. Based on inventory scores, 16 participants were observed performing unfamiliar computer tasks. The observations found that extrinsically motivated users stumbled, fell, persisted, quit, and resisted unfamiliar tasks significantly more than intrinsically motivated users. The study provides insights into how motivation styles impact adaptation to unfamiliar technologies.
AI TESTING: ENSURING A GOOD DATA SPLIT BETWEEN DATA SETS (TRAINING AND TEST) ...ijsc
This document discusses challenges in effectively splitting a dataset into training and test sets for machine learning models. It proposes using k-means clustering followed by decision tree analysis to improve the split. K-means clustering groups the data points into clusters to ensure each cluster is well-represented in both the training and test sets. Then a decision tree is used to split the clustered data, aiming to maximize purity within each subset and minimize overlap between training and test data. This approach aims to capture the full domain of the dataset and avoid underrepresenting any parts of the data in either the training or test sets.
PACE Tech Talk 14-Nov-12 - Why Model Ensembles Win Data Mining CompetitionsDean Abbott
This document discusses how ensembles of predictive models, which combine the predictions from multiple models, often outperform individual models in data mining competitions and predictive analytics problems. It provides motivation for using ensembles and describes common techniques for building ensembles, including bagging, boosting, and random forests. The document also explains how ensembles can help reduce prediction errors by achieving diversity, independence, decentralization and aggregation among the constituent models.
A More Transparent Interpretation of Health Club Surveys (YMCA)Salford Systems
The document summarizes Dean Abbott's presentation on interpreting health club member surveys. It describes two approaches: 1) A traditional statistical analysis using frequencies, tests, and measures of central tendency, and 2) Abbott's recommended solution using data mining techniques like factor analysis and predictive modeling. Factor analysis reduced the dimensionality of the survey data by identifying underlying key factors. Predictive models were then created to link these factors to membership satisfaction, renewal likelihood, and recommendations.
This document discusses principles for software effort estimation. It begins with an introduction stating that successful estimation is critical but often projects are over or under estimated. It then discusses 12 principles related to questions about effort estimation. The principles include using domain experts, outlier pruning, combining superior solo methods, and using relevancy filtering when local data is lacking. The document advocates experimentation to determine the best practices and notes that size attributes are not always necessary. It promotes methods to reduce data needs through outlier and synonym pruning.
April Webinar: Sample Balancing in 2012Research Now
How to set and manage your sample balancing options to ensure quality data and happy clients.
Presentation by: Carter Cathey, Vice President, Excellence Initiatives
The document discusses a study that examined differences in motivation and computer proficiency between daily computer users. The study hypothesized that extrinsically motivated proficient users would have more difficulty with unfamiliar computer tasks compared to intrinsically motivated users. The study involved administering a motivation inventory to over 130 participants from various countries and ages. Based on inventory scores, 16 participants were observed performing unfamiliar computer tasks. The observations found that extrinsically motivated users stumbled, fell, persisted, quit, and resisted unfamiliar tasks significantly more than intrinsically motivated users. The study provides insights into how motivation styles impact adaptation to unfamiliar technologies.
AI TESTING: ENSURING A GOOD DATA SPLIT BETWEEN DATA SETS (TRAINING AND TEST) ...ijsc
This document discusses challenges in effectively splitting a dataset into training and test sets for machine learning models. It proposes using k-means clustering followed by decision tree analysis to improve the split. K-means clustering groups the data points into clusters to ensure each cluster is well-represented in both the training and test sets. Then a decision tree is used to split the clustered data, aiming to maximize purity within each subset and minimize overlap between training and test data. This approach aims to capture the full domain of the dataset and avoid underrepresenting any parts of the data in either the training or test sets.
PACE Tech Talk 14-Nov-12 - Why Model Ensembles Win Data Mining CompetitionsDean Abbott
This document discusses how ensembles of predictive models, which combine the predictions from multiple models, often outperform individual models in data mining competitions and predictive analytics problems. It provides motivation for using ensembles and describes common techniques for building ensembles, including bagging, boosting, and random forests. The document also explains how ensembles can help reduce prediction errors by achieving diversity, independence, decentralization and aggregation among the constituent models.
Seven steps for Use Routine Information to Improve HIV/AIDS Program_Snyder_5....CORE Group
This document outlines a 7 step approach to using routine data to improve HIV/AIDS programs. The 7 steps are: 1) identify questions of interest, 2) prioritize key questions, 3) identify data needs and sources, 4) transform data into information, 5) interpret information and draw conclusions, 6) craft solutions and take action, and 7) continue to monitor key indicators. The approach aims to facilitate using existing data to answer important questions and inform decision making through collaborative work between data users and producers. Overall, the 7 step approach provides a framework to strategically use routine monitoring data to strengthen HIV/AIDS programs and policies.
This document provides an introduction to a course on business analytics. It outlines the course agenda, which covers topics like digital economics, data science techniques, and case studies. The objective is for students to build knowledge of applying analytics in various industries. Guidelines are provided for participating in case discussions and completing the final exam. The fundamentals section defines business analytics and data science, and covers essential techniques like data gathering, cleaning, and transformation.
This document outlines an agenda for a course on business analytics. It introduces the module facilitator and their background working with major technology companies. The objective of the course is to build knowledge of applying business analytics in various industries. The administrative details section outlines a 10-day schedule covering topics like digital economics, education, financial services, and health analytics. It also describes the grading criteria which is 50% participation and 50% final exam. The fundamentals section defines business analytics and data science, and discusses techniques like data gathering, cleaning, and transformation. The case methodology section provides questions to guide case study analysis in areas like an organization's business model and use of data science.
This document provides an extract from a statistical thinking course offered by Red Olive, including an overview of the course contents and modeling techniques. The 2-day course covers topics such as the CRISP-DM process, reporting versus modeling, basic statistical analysis, and best practices for sharing results. Attendees will learn how to get their data to speak through statistical analysis and translating findings into actionable business insights.
Improving the customer experience using big data customer-centric measurement...Vishal Kumar
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data.
For More, please visit http://www.tcelab.com
Improving the customer experience using big data customer-centric measurement...Business Over Broadway
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data
Experiences with Data Feedback - Better Software 2004 - Ben LindersBen Linders
Good data feedback of software measurements is critical when analyzing measurement data, for drawing conclusions, and as the basis for taking action. Feedback to those involved in the activities being measured helps validate the data as well. In this presentation Ben Linders shows examples of how Ericsson Telecommunications delivers feedback at two levels: projects and the total development center. Although the basics are similar, the application differs, and the key success factors depend on the level and the audience. At the project level, you will see how the team reviews defect data, including defect classifications and test matrices. For development center feedback, you will see how line management and technical engineers review data and analyze information based on a balanced score card approach with measurable goals. Finally, Ben Linders shows examples, data summaries, and suggested action items that management teams from the project and development center levels review.
• Techniques used in data feedback reporting and key success factors
• Close the feedback loop with different levels in the organization
• Human factors that play a role in feedback sessions
Improving the customer experience using big data customer-centric measurement...TCELab LLC
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data.
For More, please visit http://www.tcelab.com
I am a data integration expert who has worked his way up from a data warehouse analyst to an integrations solutions lead over several years. The document discusses challenges of data integration such as dealing with irrelevant or heterogeneous data from different sources, ensuring data quality, unexpected costs, and needing expertise. It provides best practices like understanding requirements and end goals, establishing standards, thorough data analysis, and extensive testing.
When analyzing millions of data points from the world's largest agile assessment database, it's clear that certain team practices and behaviors are highly correlated with positive business outcomes. What are these concrete behaviors and why is it that they - consistently - are associated with better business outcomes across enterprises in virtually all industries? Conversely, what are some of the patterns that tend to correlate with negative results?
Key Takeaways:
Understand how to instill a culture of data-driven continuous improvement
Go through a simple end-to-end exercise so you can start improving how you work right away
Recognize the key factors that are critical for creating high-performance teams.
Authored by Jorgen Hesselberg
CRISP-DM: a data science project methodologySergey Shelpuk
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides an overview of the key steps and asks questions to determine readiness to move to the next phase of the project. The overall goal is to successfully apply a standard data science methodology to gain business value from data.
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектовGeeksLab Odessa
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides examples of the types of activities and questions that should be addressed to successfully complete that phase of the project.
This document provides an overview of a two-day analytics training course. Day one covers topics like the CRISP-DM process, reporting versus modeling, statistical concepts, and techniques for determining if there is an effect or single cause. Day two focuses on working together, sharing results, and planning for future projects. The goal is to help participants get their data to speak through statistical thinking and analysis.
Slide deck from 2008 Symposium "Developing an Expert-System for Health Promotion: An Experimental E-Learning Platform" from the APA-NIOSH International Conference on Work, Stress, and Health
Data Granularity and Business Decisions by VCare Insurance CompanyDILIP KUMAR
VCare Case Study shows how data can be analysed based on providing two solutions, one based on aggregate data and other based on granular level of data.
How to sustain analytics capabilities in an organizationSAS Canada
This presentation is part of Analytics Management Series that is designed to suggest paths towards effective decision-making in order to help sustain and grow analytical capabilities. It features thought leaders who actively manage complex analytical environments who share their best practices. How to sustain analytics capabilities in an organization features Daymond Ling, Senior Director, Modelling & Analytics (CIBC) on how organizations who want better performance and less problems can use data to their advantage.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Seven steps for Use Routine Information to Improve HIV/AIDS Program_Snyder_5....CORE Group
This document outlines a 7 step approach to using routine data to improve HIV/AIDS programs. The 7 steps are: 1) identify questions of interest, 2) prioritize key questions, 3) identify data needs and sources, 4) transform data into information, 5) interpret information and draw conclusions, 6) craft solutions and take action, and 7) continue to monitor key indicators. The approach aims to facilitate using existing data to answer important questions and inform decision making through collaborative work between data users and producers. Overall, the 7 step approach provides a framework to strategically use routine monitoring data to strengthen HIV/AIDS programs and policies.
This document provides an introduction to a course on business analytics. It outlines the course agenda, which covers topics like digital economics, data science techniques, and case studies. The objective is for students to build knowledge of applying analytics in various industries. Guidelines are provided for participating in case discussions and completing the final exam. The fundamentals section defines business analytics and data science, and covers essential techniques like data gathering, cleaning, and transformation.
This document outlines an agenda for a course on business analytics. It introduces the module facilitator and their background working with major technology companies. The objective of the course is to build knowledge of applying business analytics in various industries. The administrative details section outlines a 10-day schedule covering topics like digital economics, education, financial services, and health analytics. It also describes the grading criteria which is 50% participation and 50% final exam. The fundamentals section defines business analytics and data science, and discusses techniques like data gathering, cleaning, and transformation. The case methodology section provides questions to guide case study analysis in areas like an organization's business model and use of data science.
This document provides an extract from a statistical thinking course offered by Red Olive, including an overview of the course contents and modeling techniques. The 2-day course covers topics such as the CRISP-DM process, reporting versus modeling, basic statistical analysis, and best practices for sharing results. Attendees will learn how to get their data to speak through statistical analysis and translating findings into actionable business insights.
Improving the customer experience using big data customer-centric measurement...Vishal Kumar
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data.
For More, please visit http://www.tcelab.com
Improving the customer experience using big data customer-centric measurement...Business Over Broadway
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data
Experiences with Data Feedback - Better Software 2004 - Ben LindersBen Linders
Good data feedback of software measurements is critical when analyzing measurement data, for drawing conclusions, and as the basis for taking action. Feedback to those involved in the activities being measured helps validate the data as well. In this presentation Ben Linders shows examples of how Ericsson Telecommunications delivers feedback at two levels: projects and the total development center. Although the basics are similar, the application differs, and the key success factors depend on the level and the audience. At the project level, you will see how the team reviews defect data, including defect classifications and test matrices. For development center feedback, you will see how line management and technical engineers review data and analyze information based on a balanced score card approach with measurable goals. Finally, Ben Linders shows examples, data summaries, and suggested action items that management teams from the project and development center levels review.
• Techniques used in data feedback reporting and key success factors
• Close the feedback loop with different levels in the organization
• Human factors that play a role in feedback sessions
Improving the customer experience using big data customer-centric measurement...TCELab LLC
This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data.
For More, please visit http://www.tcelab.com
I am a data integration expert who has worked his way up from a data warehouse analyst to an integrations solutions lead over several years. The document discusses challenges of data integration such as dealing with irrelevant or heterogeneous data from different sources, ensuring data quality, unexpected costs, and needing expertise. It provides best practices like understanding requirements and end goals, establishing standards, thorough data analysis, and extensive testing.
When analyzing millions of data points from the world's largest agile assessment database, it's clear that certain team practices and behaviors are highly correlated with positive business outcomes. What are these concrete behaviors and why is it that they - consistently - are associated with better business outcomes across enterprises in virtually all industries? Conversely, what are some of the patterns that tend to correlate with negative results?
Key Takeaways:
Understand how to instill a culture of data-driven continuous improvement
Go through a simple end-to-end exercise so you can start improving how you work right away
Recognize the key factors that are critical for creating high-performance teams.
Authored by Jorgen Hesselberg
CRISP-DM: a data science project methodologySergey Shelpuk
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides an overview of the key steps and asks questions to determine readiness to move to the next phase of the project. The overall goal is to successfully apply a standard data science methodology to gain business value from data.
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектовGeeksLab Odessa
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides examples of the types of activities and questions that should be addressed to successfully complete that phase of the project.
This document provides an overview of a two-day analytics training course. Day one covers topics like the CRISP-DM process, reporting versus modeling, statistical concepts, and techniques for determining if there is an effect or single cause. Day two focuses on working together, sharing results, and planning for future projects. The goal is to help participants get their data to speak through statistical thinking and analysis.
Slide deck from 2008 Symposium "Developing an Expert-System for Health Promotion: An Experimental E-Learning Platform" from the APA-NIOSH International Conference on Work, Stress, and Health
Data Granularity and Business Decisions by VCare Insurance CompanyDILIP KUMAR
VCare Case Study shows how data can be analysed based on providing two solutions, one based on aggregate data and other based on granular level of data.
How to sustain analytics capabilities in an organizationSAS Canada
This presentation is part of Analytics Management Series that is designed to suggest paths towards effective decision-making in order to help sustain and grow analytical capabilities. It features thought leaders who actively manage complex analytical environments who share their best practices. How to sustain analytics capabilities in an organization features Daymond Ling, Senior Director, Modelling & Analytics (CIBC) on how organizations who want better performance and less problems can use data to their advantage.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
42. Index Construction and Scaling
Begin with Factor Analysis
Cluster attribute groupings to be managerially meaningful
Z-normalize the variables, cast all in units of variance
Run tests for deviation from Standard Normal by variable and
factor
Create z-index for each factor
Re-scale to nation-wide percentile
43. Analysis of Hierarchy Claim
Pearson Correlations Existing Order
Facilities Support Value Engagement Impact Involvement
Facilities 1.00 0.53 0.65 0.37 0.25 0.04
Support 1.00 0.72 0.78 0.46 0.16
Value 1.00 0.65 0.55 0.25
Engagement 1.00 0.61 0.53
Impact 1.00 0.53
Involvement 1.00
n=425
Pearson Correlations Reverse Value and Support
Facilities Value Support Engagement Impact Involvement
Facilities 1.00 0.65 0.53 0.37 0.25 0.04
Value 1.00 0.72 0.65 0.55 0.25
Support 1.00 0.78 0.46 0.16
Engagement 1.00 0.61 0.53
Impact 1.00 0.53
Involvement 1.00