I gave a talk at North California Business Marketing Association on Customer Data Platform with examples ranging from Uber Grayballing to Zoom's customer retention email and "dogs or muffins".
Key Considerations While Rolling Out Denodo PlatformDenodo
Watch full webinar here: https://bit.ly/3zaPGLO
Our approach for data virtualization advisory takes the following 3 dimensions/areas into consideration:
- Technology / Architecture
- Business User Groups (your clients)
- IT Organization
To deliver quick results, Q-PERIOR uses a multitude of accelerators in predefined topics within these three dimensions. In our presentation we will elaborate on client examples why such an exercise makes sense before rolling out Denodo and what kind of risks you can avoid doing so.
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption.
In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
I gave a talk at North California Business Marketing Association on Customer Data Platform with examples ranging from Uber Grayballing to Zoom's customer retention email and "dogs or muffins".
Key Considerations While Rolling Out Denodo PlatformDenodo
Watch full webinar here: https://bit.ly/3zaPGLO
Our approach for data virtualization advisory takes the following 3 dimensions/areas into consideration:
- Technology / Architecture
- Business User Groups (your clients)
- IT Organization
To deliver quick results, Q-PERIOR uses a multitude of accelerators in predefined topics within these three dimensions. In our presentation we will elaborate on client examples why such an exercise makes sense before rolling out Denodo and what kind of risks you can avoid doing so.
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption.
In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
Detailed description of big data, with the characteristics of it. What are the limitations of the traditional systems? Where we are using big data? And also the applications of big data.
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo
This is the first in a series of five webinars that look 'under the covers' of Denodo's industry leading Data Virtualization Platform. The webinar will provide an overview of the architecture and key modules of the Denodo Platform - subsequent webinars in the series will take a deeper look at some of the key modules and capabilities of the platform, including performance, scalability, security, and so on.
More information and FREE registrations to this webinar: http://goo.gl/fLi2bC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
The Denodo Platform
Platform Architecture
Key Modules
Connectors
Data Services and APIs
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Data Science and Machine Learning for the EnterpriseCloudera, Inc.
Overview of Machine Learning and how the Cloudera Data Science Workbench provides full access to data while supporting IT SLAs. The presentation includes details on Fast Forward Labs and The Value of Interpretability in Models.
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The traditional data warehouse has served us well for many years, but new trends are causing it to break in four different ways: data growth, fast query expectations from users, non-relational/unstructured data, and cloud-born data. How can you prevent this from happening? Enter the modern data warehouse, which is able to handle and excel with these new trends. It handles all types of data (Hadoop), provides a way to easily interface with all these types of data (PolyBase), and can handle “big data” and provide fast queries. Is there one appliance that can support this modern data warehouse? Yes! It is the Analytics Platform System (APS) from Microsoft (formally called Parallel Data Warehouse or PDW) , which is a Massively Parallel Processing (MPP) appliance that has been recently updated (v2 AU1). In this session I will dig into the details of the modern data warehouse and APS. I will give an overview of the APS hardware and software architecture, identify what makes APS different, and demonstrate the increased performance. In addition I will discuss how Hadoop, HDInsight, and PolyBase fit into this new modern data warehouse.
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
From Insights to Action, How to build and maintain a Data Driven Organization...Amazon Web Services Korea
데이터는 혁신과 변혁의 토대입니다. 비즈니스 혁신을 이끄는 혁신은 특정 시점의 전략이나 솔루션이 아니라 성장을 위한 반복적이고 집단적인 계획입니다. 혁신에 이러한 접근 방식을 채택하는 기업은 전략과 비즈니스 문화에서 데이터를 기반으로 하는 경우가 많습니다. 이러한 접근 방식을 개발하려면 리더가 데이터를 조직의 자산처럼 취급하고 조직이 더 나은 비즈니스 성과를 위해 데이터를 활용할 수 있도록 권한을 부여해야 합니다. AWS와 Amazon이 어떻게 데이터와 분석을 활용하여 확장 가능한 비즈니스 효율성을 창출하고 고객의 가장 복잡한 문제를 해결하는 메커니즘을 개발했는지 알아보십시오.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Democratizing AI/ML with GCP - Abishay Rao (Google) at GoDataFest 2019GoDataDriven
Every company today is talking about AI/ML, but when most companies talk about AI/ML in their transformation journey, you hear terms like Proof of Concept, Feasibility Study, Pilot, A/B Test. We are at the peak of AI's hype, but only 12% of enterprises have deployed AI in production. Google aims to make big data processing available for everyone, the possiblities of Big Query ML are endless: Marketing, retail, industrial and IoT, media, gaming, and so fort.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Yelp has operated our connector ecosystem to feed vital data to domain-specific teams and data stores. We share some of our learning and experiences on operating such system. We will touch on what is the next phase of the system evolution.
A survey by Schneider Electric in the US revealed that predictive maintenance services can lead to 25% reduction in cost. Learn about industrial IoT framework that enables PdM
4 Advantages Artificial Intelligence Can Offer Industry 4.pptxArpitGautam20
Here are a few advantages that Ai brings to the table for Industry 4.0. These can change the way industries leverage Ai in the days to come. https://arsr.tech/4-advantages-artificial-intelligence-can-offer-industry-4-0/
Detailed description of big data, with the characteristics of it. What are the limitations of the traditional systems? Where we are using big data? And also the applications of big data.
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo
This is the first in a series of five webinars that look 'under the covers' of Denodo's industry leading Data Virtualization Platform. The webinar will provide an overview of the architecture and key modules of the Denodo Platform - subsequent webinars in the series will take a deeper look at some of the key modules and capabilities of the platform, including performance, scalability, security, and so on.
More information and FREE registrations to this webinar: http://goo.gl/fLi2bC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
The Denodo Platform
Platform Architecture
Key Modules
Connectors
Data Services and APIs
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Data Science and Machine Learning for the EnterpriseCloudera, Inc.
Overview of Machine Learning and how the Cloudera Data Science Workbench provides full access to data while supporting IT SLAs. The presentation includes details on Fast Forward Labs and The Value of Interpretability in Models.
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The traditional data warehouse has served us well for many years, but new trends are causing it to break in four different ways: data growth, fast query expectations from users, non-relational/unstructured data, and cloud-born data. How can you prevent this from happening? Enter the modern data warehouse, which is able to handle and excel with these new trends. It handles all types of data (Hadoop), provides a way to easily interface with all these types of data (PolyBase), and can handle “big data” and provide fast queries. Is there one appliance that can support this modern data warehouse? Yes! It is the Analytics Platform System (APS) from Microsoft (formally called Parallel Data Warehouse or PDW) , which is a Massively Parallel Processing (MPP) appliance that has been recently updated (v2 AU1). In this session I will dig into the details of the modern data warehouse and APS. I will give an overview of the APS hardware and software architecture, identify what makes APS different, and demonstrate the increased performance. In addition I will discuss how Hadoop, HDInsight, and PolyBase fit into this new modern data warehouse.
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
From Insights to Action, How to build and maintain a Data Driven Organization...Amazon Web Services Korea
데이터는 혁신과 변혁의 토대입니다. 비즈니스 혁신을 이끄는 혁신은 특정 시점의 전략이나 솔루션이 아니라 성장을 위한 반복적이고 집단적인 계획입니다. 혁신에 이러한 접근 방식을 채택하는 기업은 전략과 비즈니스 문화에서 데이터를 기반으로 하는 경우가 많습니다. 이러한 접근 방식을 개발하려면 리더가 데이터를 조직의 자산처럼 취급하고 조직이 더 나은 비즈니스 성과를 위해 데이터를 활용할 수 있도록 권한을 부여해야 합니다. AWS와 Amazon이 어떻게 데이터와 분석을 활용하여 확장 가능한 비즈니스 효율성을 창출하고 고객의 가장 복잡한 문제를 해결하는 메커니즘을 개발했는지 알아보십시오.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Democratizing AI/ML with GCP - Abishay Rao (Google) at GoDataFest 2019GoDataDriven
Every company today is talking about AI/ML, but when most companies talk about AI/ML in their transformation journey, you hear terms like Proof of Concept, Feasibility Study, Pilot, A/B Test. We are at the peak of AI's hype, but only 12% of enterprises have deployed AI in production. Google aims to make big data processing available for everyone, the possiblities of Big Query ML are endless: Marketing, retail, industrial and IoT, media, gaming, and so fort.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Yelp has operated our connector ecosystem to feed vital data to domain-specific teams and data stores. We share some of our learning and experiences on operating such system. We will touch on what is the next phase of the system evolution.
A survey by Schneider Electric in the US revealed that predictive maintenance services can lead to 25% reduction in cost. Learn about industrial IoT framework that enables PdM
4 Advantages Artificial Intelligence Can Offer Industry 4.pptxArpitGautam20
Here are a few advantages that Ai brings to the table for Industry 4.0. These can change the way industries leverage Ai in the days to come. https://arsr.tech/4-advantages-artificial-intelligence-can-offer-industry-4-0/
What is Machine Vision?
History of Machine Vision
Introduction of Machine Vision
What are its advantages and disadvantages?
What are its applications?
What are the com[onents of Machine Vision?
Machine vision v/s Human inspectors
Capgemini’s Smart Plant Supervision (SPS) solution can improve plant and facilities management using the Watson IoT Platform to leverage data from a wide range of digital and analog sensors. The end-to-end solution for data aggregation, analytics and action—in near real time—is offered only by Capgemini on Bluemix. By leveraging our IOT Platform Service running on Bluemix, SPS enables predictive and preventive maintenance, higher machine uptime and maximum efficiency.
How Computer Vision Is Defined In the Fast-Paced World of TechnologyAssert AI
Computer vision uses computer technology and expertise to provide visual information or insights. The field of computer vision deals with developing algorithms, software, and hardware to obtain, process, interpret, and utilize visual information in various applications. Computer vision has many applications in automatic flight control, autonomous driving, mapping, surveillance, image manipulation, and more. For more details, please visit our website:
https://www.assertai.com/
Platforming the Major Analytic Use Cases for Modern EngineeringDATAVERSITY
We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management.
In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX).
Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure.
Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes.
Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.
Meeting the challenges to adopt visual production management systems hms-whit...Ariel Lerer
This White Paper will provide an essential understanding of different initiatives towards having a Visual Production Management system, (VPMS), in a manufacturing environment. Also insights about why? and how? to implement a VPMS, highlighting the benefits of taking these actions, and further across your environment creating a learning organization.
Download from www.hmswebsite.com/vpms-white-paper/
recent white paper written by LCC Asia Pacific's Nicholas Assef on the growing adoption of Artificial Intelligence and Machine Learning in the Mining Wear Parts Industry.
In the era of digital transformation, the concept of Digital Twins has emerged as a revolutionary approach to managing and optimizing the lifecycle of physical assets, systems, and processes. This talk delves into the transformative potential of Digital Maintenance in the Digital Twin Era, highlighting the seamless integration of digital replicas with real-world operations to foster unprecedented levels of efficiency, predictability, and sustainability in maintenance practices. We will explore how Digital Twins serve as dynamic, real-time reflections of physical assets, allowing for meticulous monitoring, analysis, and simulation. Through vivid examples, we'll demonstrate the benefits of this paradigm, such as predictive maintenance, which leverages data analytics and machine learning to anticipate failures and optimize maintenance schedules, thereby reducing downtime and extending asset lifespan. Further, the talk will showcase the role of Digital Twins in facilitating remote maintenance operations. By providing a comprehensive, virtual view of assets, maintenance professionals can perform diagnostics and identify issues without being physically present, enhancing safety and reducing response times. We'll also explore the environmental benefits of Digital Maintenance within the Digital Twin framework. By optimizing maintenance schedules and operations, organizations can significantly reduce their carbon footprint and resource consumption, contributing to more sustainable industrial practices. Finally, the presentation will highlight case studies from various industries, including manufacturing, energy, and transportation, where the adoption of Digital Twins has led to substantial cost savings, improved operational efficiency, and enhanced decision-making processes. These examples will illustrate the tangible value and competitive advantage that Digital Maintenance in the Digital Twin Era offers to forward-thinking organizations.
Digital Transformation and Process Optimization in ManufacturingBigML, Inc
Keyanoush Razavidinani, Digital Services Consultant at A1 Digital, a BigML Partner, highlights why it is important to identify and reduce human bottlenecks that optimize processes and let you focus on important activities. Additionally, Guillem Vidal, Machine Learning Engineer at BigML completes the session by showcasing how Machine Learning is put to use in the manufacturing industry with a use case to detect factory failures.
This interactive course aims to equip students with an in-depth comprehension of
data science principles and methodologies, with a strong emphasis on practical
applications.
Global AI Festival and Future is a digital broadcast of thought-provoking discussions and insights from world AI leaders. The event covers global and regional streams, helping you learn about the latest technological improvements, practical use cases, and industry trends.
Key Outcomes of the Event
- Gain knowledge about Latest Technology Trends
- Networking Opportunity with Technical Leaders
- Opportunities to become a thought leader
- Learn and Understand Industry based AI Solutions
- Getting Access to world super fast GPU compute
- International Internship opportunities
- Quiz Competition to win prizes and placements
Object Automation, a technology company based in California,
has been concentrating on latest technologies and emerging
tech partnerships. These include research and solution
development, the development of onshore and offshore
technology projects, the establishment of tech centers of
excellence in AI, quantum, and chip design, Technology
workshops and boot camps for corporates, special labs for
universities, and cutting-edge industry projects.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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 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
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
3. Predictive maintenance
• The largest use case for industrial AI is “Predictive Maintenance”.
Predictive Maintenance makes use of advanced analytics (e.g.,
Machine Learning) to determine the condition of a single asset or an
entire set of assets (e.g., a factory).
• The goal: Predict when maintenance should be performed. Predictive
maintenance usually combines various sensor readings, sometimes
external data sources, and performs predictive analytics on thousands
of logged events. Predicting the remaining useful life of an asset using
supervised ML is the most common technique in Predictive
maintenance
4. Predictive maintenance
• One of the biggest challenges with Predictive Maintenance is the
elimination of data imbalances as there is often not enough failure
data for all the assets. Data is called imbalanced, when failure events
represent less than some specific required share of the dataset. To
make accurate predictions on the data, such imbalances have to be
eliminated first. There are 2 main methods to achieve balanced data:
data sampling and cost-sensitive learning algorithms.
5. Predictive maintenance
• Example 1: Deutsche Bahn, the German railway operator, leverages
data from railway switches to predict failures, thus decreasing
unexpected delays at scale. Source: Konux Case Study
• Example 2: Nissan runs an AI Predictive maintenance platform to do
RUL prognostication on 7,500 assets. The company claims an
unplanned downtime reduction of 50% and a payback period of < 3
months. Nissan scaled the solution from 20 critical assets to
thousands without increasing the workload of the on-site PdM team.
Source: Manufacturing.net
7. Quality inspection & assurance
• Automated optical inspection is a technique where a camera
autonomously scans the device under test for catastrophic failure
(e.g. missing component) and/or quality defects (e.g. fillet size or
shape or component skew). Computer vision is the foundation of
optical inspection. Once the images are recognized, semi-supervised
ML is the most effective technique to classify images into failure
classes. The main benefit of this use case is cost reduction, and the
main potential beneficiaries are large manufacturing facilities, where
a small reduction in scrap or test time can yield very large savings.
8. Quality inspection & assurance
Example: A major German automotive company is introducing a deep
learning-enabled system for quality control. This system is fully
integrated into the flow of the final assembly process and thereby gets
rid of the need to have a separate testing area in a controlled
environment. The solution is expected to soon replace conventional
camera booths that are used today and according to the OEM will
result in “immense savings”.
9. Manufacturing process optimization
• Perhaps the most obvious but still one of the most difficult to implement AI
use cases is automated manufacturing process optimization. One
implementation of this optimization is through Autonomous machines or
robots.
• The idea behind those autonomous assets is that they replicate
monotonous human tasks in the manufacturing process, thus saving costs.
Before being put into production, the autonomous machines/robots
perform the same task over and over again, learning each time until they
achieve sufficient accuracy. The reinforcement learning technique is often
used to train robots and autonomous machinery. Under this technique, a
robot can relatively quickly teach itself to do a task under the supervision
of a human. The “brains” of such a robot/machine are usually neural
networks.
10. Manufacturing process optimization
• Example: ABB is investing $150M to build an “advanced, automated
and flexible” robotics factory in Shanghai. In this plant, ABB will
manufacture robots using robots. According to ABB, these robots will
have autonomous and collaborative elements. The robots’ autonomy
is built with the use of AI and digital twin. Source: ABB.
12. Supply chain optimization
• 8% of all industrial AI implementations are improvements to
industrial supply chains. Using AI tools to improve inventory
management is one of the key applications.
• Predictive inventory management leverages predictive analytics for a
variety of inventory-related tasks including to reduce inventory
planning time, minimize inventory cost, optimize repairments, and
find optimal reorder points. For these tasks, techniques such as time-
series analysis, probabilistic modeling (Markov and Bayesian models)
as well as simulations (e.g., Monte-Carlo simulation) are most
commonly used.
13. Supply chain optimization
• Example: Continental has built software to predict the optimal points
for tire changes on its fleet. The underlying model predicts the overall
running mileage and compares it to the baseline, to generate actions.
By that, Continental is reducing its stock of tires, also improving safety
on the road.
15. AI-driven cybersecurity & privacy
• AI-driven cybersecurity & privacy relates to aspects such as cyber
threat detection. It typically involves observing the network
infrastructure and detecting threats of cyberattacks in real time. It
also often includes such activities as network traffic analysis, endpoint
detection and response, malware sandboxes, etc. AI-powered cyber
threat detection is often part of a larger cybersecurity solution that
also uses a number of prevention measures (e.g., firewalls).
17. Automated physical security
• Surveillance & physical threat detection entails real-time surveillance
of manufacturing sites or workers in order to automatically detect
physical security threats and/or potential safety hazards.
18. Automated data management
• With data often stored in multiple systems and multiple places, it is
hard to access and analyze the data quickly and holistically. Therefore,
some industrial companies start to employ data management
solutions that perform tasks such as data acquisition, data filtering,
data cleaning & integration, etc. in real-time.
20. Smart assistants
• Voice assistant is one of the examples of smart assistants in
manufacturing settings. Integrating voice assistant technology into its
real-time industrial monitoring systems allows workers to gain
insights without coding the explicit commands or printing long status
reports.
21. AI-driven research & development
• Automated component design is the leading use case in AI-driven
R&D.
• The goal: Letting software independently develop dozens of different
designs in short periods, given a set of predefined constraints. The
optimal design is chosen afterwards. For this task, digital twins and
simulations often complement the AI techniques.
22. Autonomous resource exploration
• Especially relevant in Mining & Quarrying and Oil & Gas industries,
autonomous resource exploration is a technique of analysis and
processing of the massive volume of images (e.g., radar, satellite or
drone images) to detect the optimal point for resource extraction. AI
can be especially useful in the detection of captivities in difficult-to-
access areas, such as the ocean subsurface or mountains.
23. Thank you.
We look forward to working together.
www.object-automation.com
www.object-automation.com
Object Automation Software Solutions Pvt Ltd (India
operations):
Chennai
And
Object Automation Inc
New York
Contact US
Using hr@object-automation.com
USA : 914 204 2581
India : +91 7397784815