As the volume of observability data explodes, relying solely on human analysis can lead to undesired impacts on apps and infrastructure, as well as unsustainable SRE and developer workload. Learn how machine learning features embedded in Elastic Observability workflows enable reliability, efficiency, and sustainability outcomes for enterprise IT teams — no data scientists required.
Data may be the new gold. Can we say the same about data centres?Colliers Asia
Presentation by Lynus Pook, Senior Director, Regional Industrial Advisory, Occupier Services | Asia, at the ASEAN Data Centre & Cloud Summit 2021 on September 29, 2021.
Traversing the Digital Vortex, Lux Rao, Director & Leader, Digital Transforma...Rahul Neel Mani
This document discusses how digital disruption is transforming industries and the need for companies to build digital business agility. It notes that digital technologies like the Internet of Things, cloud, mobile and big data are enabling new digital business models and increasing competitive threats. The document advocates that companies simplify processes, automate operations, use data insights to optimize decisions and build a digital ready infrastructure in order to gain agility and counter digital disruption. It provides examples of how companies in various industries can create better customer experiences through digital transformation.
Connected barrels_IoT in Oil and Gas_deloitteAnshu Mittal
In the oil and gas industry, the promise of IoT applications lies not with managing existing assets, supply chains, or customer relationships but, rather, in creating new value in information about these. An integrated deployment strategy is key for O&G companies looking to find value in IoT technology.
AIOps & Observability to Lead Your Digital TransformationSensu Inc.
This document discusses how AIOps and observability can help lead digital transformation efforts. It notes that customer expectations are increasingly focused on digital experiences and on-demand availability. However, IT complexity is also growing rapidly due to factors like process change, increasing demand, and lack of skill sets. AIOps uses techniques like anomaly detection, root cause analysis, and knowledge recycling to help automate incident resolution and ensure high-quality customer experiences despite this complexity. This helps improve operator efficiency, allow teams to focus more on development, and deliver on the vision of guaranteeing availability, agility and performance for digital services worldwide.
1) The digital industrial revolution is in full force, driven by factors like the growth of smart meters, internet-connected devices, and data generated by machines.
2) GE aims to be the leading digital industrial company by developing cloud-based Predix platform and using industrial data to increase reliability, reduce costs, and enable profitable growth for customers across industries like aviation, healthcare, transportation, and oil and gas.
3) GE is working with customers through collaborations and pilots to develop customized Predix applications that optimize assets and operations using industrial internet concepts like digital twins, predictive maintenance, and analytics.
How to apply machine learning into your CI/CD pipelineAlon Weiss
A quick introduction to AIOps, the business reasons why the CI/CD pipeline needs to constantly improve, and how this can be accomplished with data that's already available with existing Machine Learning and other algorithms.
The presentation summarizes TRC's business and financial performance. TRC provides engineering, consulting and construction management services to the energy, environmental and infrastructure industries. It has transformed its business through acquisitions, cost reductions and a focus on higher-growth markets. TRC has a diversified revenue base across business segments and clients. It is pursuing organic growth and acquisitions in the utility/power and oil & gas industries. TRC has strengthened its balance sheet and is demonstrating improved financial metrics as it leverages its business model.
Artificial Intelligence Application in Oil and GasSparkCognition
Visit http://sparkcognition.com for more information.
To access and listen to the on-demand version of the webinar, go here:
http://sparkcognition.com/ai-oil-and-gas-webinar-video/
Learn how Artificial Intelligence and Machine Learning are being effectively applied in Oil & Gas right now, how they will become even more prevalent, and how they can impact your bottom line and transform your business.
We'll cover:
• Fundamentals of Artificial Intelligence and Machine Learning
• Understanding of why Artificial Intelligence and Machine Learning are revolutionary in how they can help the Oil & Gas industry. This technology is already being used to prevent downhole tool failures or events like stuck pipes, pinpointing the ideal drilling locations during exploration and discovery, predicting pipeline pump failures, identify frack truck pump failures, etc.
• Real world examples of how other clients are using AI/ML today
The document discusses selecting an IoT platform. It notes that 2017 will see a shift towards IoT deployments and monetization. There were many IoT platform announcements in 2016 and acquisitions totaling over $100 billion. However, there are still too many platforms and the market is ready for consolidation. When selecting a platform, considerations include features, managing devices, connectivity, security, analytics and other capabilities. Using an IoT platform can accelerate innovation by focusing on differentiating aspects of solutions.
IoT - Data Management Trends, Best Practices, & Use CasesCloudera, Inc.
With billions of new devices, IoT is transforming how businesses capitalize on data. Data driven organizations are using IoT as as a means to improve their customer experience, drive operational efficiencies, and enable new business models. However, without the right data management strategy and tools, investments in IoT can yield limited results.
Join Cloudera and 451 Research for a joint webinar to learn more about some of the data management best practices and how organizations are using advanced analytics and machine learning to enable IoT use cases.
This document discusses how Splunk can be used to analyze industrial and Internet of Things (IoT) data. It describes how Splunk provides secure data collection, real-time dashboards and reporting, powerful search and analytics capabilities, and scalable time-series storage. The document outlines Splunk's capabilities for various industries like oil and gas, manufacturing, transportation, and utilities. It also provides examples of how Splunk has been used by customers for remote freight train monitoring, understanding customer behavior through vending machine data, and saving over $1 billion through energy efficiency calculations and recommendations.
Quantum Computing (IBM Q) - Hive Think Tank Event w/ Dr. Bob Sutor - 02.22.18The Hive
The document introduces quantum computing and IBM's efforts in the field, including the IBM Q Experience launched in 2016 which allows users to run algorithms and experiments on quantum computers via the cloud. It discusses IBM's goals of building universal fault-tolerant quantum computers and the IBM Q Network, a global community to advance quantum computing.
Autonomous subsea systems are expanding their capabilities to include production treatment and downhole operations. Advanced analytics and business simulation tools are being used to monitor field equipment, facilities, personnel and inventories across the oil and gas landscape. These tools combine sensor data with other data sources to enable reliable predictions and scenario testing. Asset integrity and performance management solutions are spreading rapidly due to factors like big data, the internet of things, cloud computing, and mobile technologies.
Integrating AI into IoT networks is becoming a prerequisite for success in today’s data-driven digital ecosystems. The only way to keep up with IoT-generated data and gain the hidden insights it holds is using AI as the catalyst of IoT. Join this webinar to understand how IoT and AI may work together.
Splunk for Industrial Data and the Internet of Thingsaliciasyc
The IoT is a natural evolution of the world’s networks. Just as people became more connected by devices and applications during the explosion of the social media revolution, devices, sensors and industrial equipment are also becoming more connected—and are consuming and generating data at an unprecedented pace. Disparate and deployed connected devices can provide a unique touchpoint to real-world operations and conditions. Only few architectures and applications are designed to handle the constant streams of real-time events, sensor readings, user interactions and application data produced by massive numbers of connected devices. Use Splunk to collect, index and harness the power of the machine data generated by connected devices and machines deployed on your local network or around the world.
Intelligent APIs for Big Data & IoT Create customized data views for mobile,...CA API Management
The document discusses how APIs can help manage big data from the Internet of Things (IoT). It notes that IoT will generate large amounts of diverse data from many sources, and APIs are needed to integrate data from different systems and sources, and regulate access and sharing of data through data marketplaces and lenses. The document also discusses challenges around ensuring proper governance and ownership when data is shared through APIs.
DataArt Financial Services and Capital MarketsDataArt
DataArt is a global software engineering firm that takes a uniquely human approach to solving problems. With over 20 years of experience, teams of highly-trained engineers around the world, deep industry sector knowledge, and ongoing technology research, we help clients create custom software that improves their operations and opens new markets. Powered by our People First principle, we work with clients at any scale and on any platform, and adapt alongside them as they evolve.
We integrate our engineering excellence with deeply human values that drive our business and our approach to relationships: curiosity, empathy, trust, honesty, and intuition. These qualities help us deliver high-value, high-quality solutions that our clients depend on, and lifetime partnerships they believe in.
DataArt has earned the trust of some of the world’s leading brands and most discerning clients, including Nasdaq, Travelport, Ocado, Centrica/Hive, Paddy Power Betfair, IWG, Univision, Meetup and Apple Leisure Group among others. DataArt brings together expertise of over 3000 professionals in 20 locations in the US, Europe, and Latin America.
Experian is a leading global information services company with over $15 billion in revenue. It uses advanced analytics and machine learning to drive innovation and embed new techniques into its business. This includes using web data, transactional data, and voice data to improve risk scoring, fraud detection, and customer insights. Experian develops products like its Web Data Insights and Transactional Data for Fraud Insights to provide these advanced analytics capabilities to its clients.
Why you should use Elastic for infrastructure metricsElasticsearch
Widely known for full-text search and logging, the Elastic Stack has evolved into a compelling solution for infrastructure metrics use cases. From fast and efficient time series datastore to integrations for onboarding common service metrics and dedicated UIs for visual exploration, see the many reasons to start using Elastic for your infrastructure metrics use case today.
Machine learning and the Elastic Stack: Everywhere you need itElasticsearch
This document discusses machine learning and the Elastic Stack. It begins with some forward-looking statements about future offerings, expected performance, and uncertainties. It then discusses how machine learning can be used for anomaly detection in time series data to identify patterns and unusual behavior. Different types of time series anomaly detection models are described, including single metric, multi-metric, and population analysis models. The presentation concludes with a brief demo of machine learning in the Elastic Stack.
See the vision for the future of Elastic solutions, from new features to GovCloud availability and FedRAMP authorization. Find out what’s in store for Elastic Enterprise Search, Observability, and Security and see how they’re evolving to help users better mitigate risk, reduce costs, and modernize infrastructures. Plus, see the honorees for this year’s Elastic Search Public Sector Awards and learn about their projects.
Monitor multi-cloud deployments with Elastic ObservabilityElasticsearch
Enterprises are adopting multi-cloud or hybrid infrastructures to avoid vendor lock-in, reduce latencies, maximize ROI, and add redundancy. While these new deployment patterns have many benefits, centrally monitoring such a mixed system is challenging. Learn how the Elastic Stack can provide a single pane of glass to monitor applications and services deployed across multiple public clouds such as Azure, AWS, and Google Cloud.
Monitor every app, in every stage, with free and open Elastic APMElasticsearch
Elastic APM helps you deliver better digital experiences by providing complete visibility into the health of your apps — no matter how they are built, where they run, or which dev stage they’re in. Learn about the evolution of Elastic APM, the problems it solves, and where it’s headed. See how it connects traces, logs, and metrics to help you quickly get to the root cause. Plus, hear from customers using Elastic APM to improve their applications.
Mappy hour: Uncovering insights with Elastic Maps and location dataElasticsearch
Learn how Elastic Maps helps you uncover location insights in your Elasticsearch data. Using examples from logs analysis, APM, and SIEM, you’ll see how easy it is to build rich geospatial analyses designed to enhance both observability and security use cases.
This presentation by InterCloud outlines its business strategy and financial projections. It describes InterCloud as a cloud integrator that provides IT solutions through proprietary cloud platforms and professional services. The presentation highlights InterCloud's growth strategy, industry trends, value propositions for customers and investors, case studies, and financial projections showing increasing revenue and earnings. It introduces the experienced management team and argues InterCloud is well-positioned in expanding cloud computing markets.
Cybersecurity: Intelligence, innovation, and information warfareElasticsearch
This presentation discusses cybersecurity, intelligence, innovation, and information warfare. It contains forward-looking statements about future offerings and performance that are subject to risks and uncertainties. The presentation provides 10 observations about the current state of security and how to succeed in this environment. It describes how security has become a data problem and how the Elastic stack uses data analytics, visualization, and operations across Elasticsearch, Logstash, and Kibana to power security solutions. Examples are given of how Elastic solutions are used for security orchestration, automation, response, and incident response.
This document contains forward-looking statements and disclaimers about InterCloud's financial projections and business strategies. It notes that actual results could differ from what is presented due to various risk factors. It also states that the pro forma financial information provided is constructed from separate financial statements of the companies involved and does not necessarily represent what the combined financials would be. The document provides an overview of InterCloud, describing its cloud platforms and services, growth strategies, key metrics like revenue and EBITDA, value propositions for investors and customers, examples of professional services case studies, comparative financial statements, and backgrounds of the key executives.
Datadog held its 2024 Investor Day on February 15, 2024. The presentation included a safe harbor statement noting that the presentation contained forward-looking statements subject to risks and uncertainties. The agenda covered Datadog's strategy, growth opportunities, platform innovations, go-to-market execution, and financial goals. Olivier Pomel, Datadog's CEO and co-founder, discussed the problems Datadog solves through its unified platform approach and how it has expanded into new product categories while maintaining its platform-first philosophy.
How Zebra Technologies delivers business intelligence with Elastic on Google ...Elasticsearch
Zebra Technologies builds tracking technology and solutions by giving physical things a digital voice. Zebra VisibilityIQTM is hosted on Google Cloud, and uses Google and Elastic as a big data store and analytics engine, delivering search, aggregations, scripted metrics, and map-reduce calculations. Learn how Zebra Technologies uses Elastic and Google Cloud to provide the speed, caching, and reliability needed to analyze huge data sets and deliver real-time KPIs.
Elastic Security: Unified protection for everyoneElasticsearch
1. Elastic Security provides unified protection for everyone through its security solutions including SIEM, endpoint security, threat hunting, and more.
2. It is powered by the Elastic Stack and can be deployed anywhere including Elastic Cloud on Kubernetes.
3. Elastic Security differentiates itself through its fast and scalable search engine, rich visualizations, fully operationalized machine learning, field-proven detection library, and vibrant community ecosystem.
Get tips directly from the experts at Elastic about planning for, monitoring, and troubleshooting the Elastic Stack at scale. Elastic experts will share the tools, strategies, and architectures that can be used to ensure cluster health and performance. Learn about using tools like automated alerting to identify and remediate issues rapidly. Walk away armed with best practices for how to ensure both cluster and data resiliency.
The document discusses tactics for optimizing costs on Elasticsearch Service. It outlines various strategies like utilizing optimized deployment templates, adjusting availability zones, implementing data rollups, setting up ILM policies to rollover data and reduce shards, applying ILM and snapshot management policies, and preparing for an annual commitment by optimizing deployment size based on use case. Implementing these different optimization techniques could help reduce costs by up to half depending on the deployment and use case.
Creating stellar customer support experiences using searchElasticsearch
Customers, now more than ever, want to solve support issues on their own using websites and mobile applications. And self-service customer support translates to reduced support costs and higher customer satisfaction. Learn how Elastic Enterprise Search helps you achieve all this and more.
The importance of normalizing your security data to ECSElasticsearch
The Elastic Common Schema (ECS) can be used for SIEM, logging, APM, and more. See the different paths to adopting ECS for security and why data normalization is so critical. Learn how to map custom sources so they can be used by Elastic Security and how to implement custom pipelines that may require additional fields. We'll provide concrete examples and give pointers to relevant resources to help you get going.
Learn about the key system and data modeling challenges for performing anomaly detection on large complex systems in near real time. You'll see how Elastic approaches these problems as we illustrate how the specific modeling choices affect the quality of results. We'll also briefly survey other popular time series modeling techniques and assess their suitability for anomaly detection.
Elastic Observability is helping organizations drive their mean time to resolution toward zero with end-to-end visibility in a single platform. Hear about the latest features and capabilities at all layers — from ingest to insight — and get a glimpse into where we are headed.
How CACI and Elastic support the Department of DefenseElasticsearch
CACI is a strategic partner of the Defense Logistics Agency (DLA) that maintains and enhances the Procurement Integrated Enterprise Environment (PIEE), a suite of procure-to-pay applications that support the US Department of Defense (DOD). Learn how the Elastic Stack supports PIEE and how CACI uses Elastic to continuously improve the applications.
How South Dakota's BIT defends against cyber threatsElasticsearch
Learn how the Security Operations Center within the South Dakota Bureau of Information and Telecommunications (BIT) uses the Elastic Stack to defend the state's expanding remote work force against threats — from leveraging endpoint visibility data to searching across multiple platforms to see an investigation all the way to the end and beyond using automated Kibana detection rules.
Similar to Realizing your AIOps goals with machine learning in Elastic (20)
An introduction to Elasticsearch's advanced relevance ranking toolboxElasticsearch
The hallmark of a great search experience is always delivering the most relevant results, quickly, to every user. The difficulty lies behind the scenes in making that happen elegantly and at a scale. From App Search’s intuitive drag and drop interface to the advanced relevance capabilities built into the core of Elasticsearch — Elastic offers a range of tools for developers to tune relevance ranking and create incredible search experiences. In this session, we’ll explore some of Elasticsearch’s advanced relevance ranking features, such as dense vector fields, BM25F, ranking evaluation, and more. Plus we’ll give you some ideas for how these features are being used by other Elastic users to create world-class, category defining search experiences.
Eze Castle Integration is a managed service provider (MSP), cloud service provider (CSP), and internet service provider (ISP) that delivers services to more than 1,000 clients around the world. Different departments within Eze Castle have devised their own log aggregation solutions in order to provide visibility, meet regulatory compliance requirements, conduct cybersecurity investigations, and help engineers with troubleshooting infrastructure issues. In 2019, they partnered with Elastic to consolidate the data generated from different systems into a single pane of glass. And thanks to the ease of deployment on Elastic Cloud, professional consultation services from Elastic engineers, and on-demand training courses available on Elastic Learning, Eze Castle was able to go from proof-of-concept to a fully functioning ""Eze Managed SIEM"" product within a month!
Learn about Eze Castle's journey with Elastic and how they grew Eze Managed SIEM from zero to 100 customers In less than 14 months.
Cómo crear excelentes experiencias de búsqueda en sitios webElasticsearch
Descubre lo fácil que es crear búsquedas relevantes y enriquecidas en sitios web de cara al público para impulsar las conversiones, incrementar el consumo de contenido y ayudar a los visitantes a encontrar lo que necesitan. Realiza un recorrido por las herramientas de Elastic a las que puedes sacar partido para transformar con facilidad tu sitio web, lo que incluye nuestro nuevo y potente rastreador web.
Te damos la bienvenida a una nueva forma de realizar búsquedas Elasticsearch
1) The document introduces ElasticON Solution Series, which provides out-of-the-box personalized, centralized, and secure organizational search across internal and external sources.
2) It discusses how Elastic Enterprise Search can improve productivity, satisfaction, collaboration, and decision making by connecting all applications and content with a single scalable search platform.
3) The solution achieves this through intuitive search features, powerful analytics and visualization tools, simplified administration, and security certifications to ensure data protection.
Tirez pleinement parti d'Elastic grâce à Elastic CloudElasticsearch
Découvrez pourquoi Elastic Cloud est la solution idéale pour exploiter toutes les offres d'Elastic. Bénéficiez d'une flexibilité d'achat et de déploiement au sein de Google Cloud, de Microsoft Azure, d'Amazon Web Services ou des trois à la fois. Apprenez quels avantages vous apporte une offre de service géré et déterminez la solution qui vous permet de la gérer par vous-même grâce à des outils intégrés d'automatisation et d'orchestration. Et ce n'est pas tout ! Familiarisez-vous avec les fonctionnalités qui peuvent vous aider à scaler vos opérations au fur et à mesure de l'évolution de votre déploiement, à stocker vos données d'une manière rentable et à optimiser vos recherches. Ainsi, vous n'aurez plus à abandonner de données et obtiendrez les informations exploitables dont vous avez besoin pour assurer le fonctionnement de votre entreprise.
Comment transformer vos données en informations exploitablesElasticsearch
Découvrez des fonctionnalités stratégiques de la Suite Elastic, notamment Elasticsearch, un moteur de données incomparable, et Kibana, véritable fenêtre ouverte sur la Suite Elastic.
Dans cette session, vous apprendrez à :
injecter des données dans la Suite Elastic ;
stocker des données ;
analyser des données ;
exploiter des données.
Plongez au cœur de la recherche dans tous ses états.Elasticsearch
À l'instar de la plupart des entreprises modernes, vos équipes utilisent probablement plus de 10 applications hébergées dans le cloud chaque jour, mais passent aussi bien trop de temps à chercher les informations dont elles ont besoin dans ces outils. Grâce aux fonctionnalités prêtes à l'emploi d'Elastic Workplace Search, découvrez combien il est facile de mettre le contenu pertinent à portée de la main de vos équipes grâce à une recherche unifiée sur l'ensemble des applications qu'elles utilisent pour faire leur travail.
Modernising One Legal Se@rch with Elastic Enterprise Search [Customer Story]Elasticsearch
Knowledge management needs in the legal sector, why Linklaters decided to move away from its legacy KM search engine, Kin+Carta's management of the migration process, and how the switch revitalised a well-established system and opened up new possibilities for its future development.
An introduction to Elasticsearch's advanced relevance ranking toolboxElasticsearch
The hallmark of a great search experience is always delivering the most relevant results, quickly, to every user. The difficulty lies behind the scenes in making that happen elegantly and at a scale. From App Search’s intuitive drag and drop interface to the advanced relevance capabilities built into the core of Elasticsearch — Elastic offers a range of tools for developers to tune relevance ranking and create incredible search experiences. In this session, we’ll explore some of Elasticsearch’s advanced relevance ranking features, such as dense vector fields, BM25F, ranking evaluation, and more. Plus we’ll give you some ideas for how these features are being used by other Elastic users to create world-class, category defining search experiences.
Like most modern organizations, your teams are likely using upwards of 10 cloud-based applications on a daily basis, but spending far too many hours a day searching for the information they need across all of them. With the out-of-the-box capabilities of Elastic Workplace Search, see how easy it is to put relevant content right at your teams’ fingertips with unified search across all the apps they rely on to get work done.
Building great website search experiencesElasticsearch
Discover how easy it is to create rich, relevant search on public facing websites that drives conversion, increases content consumption, and helps visitors find what they need. Get a tour of the Elastic tools you can leverage to easily transform your website, including our powerful new web crawler.
Keynote: Harnessing the power of Elasticsearch for simplified searchElasticsearch
Get an overview of the innovation Elastic is bringing to the Enterprise Search landscape, and learn how you can harness these capabilities across your technology landscape to make the power of search work for you.
Cómo transformar los datos en análisis con los que tomar decisionesElasticsearch
Descubre las áreas de características estratégicas de Elastic Stack: Elasticsearch, un motor de datos inigualable y Kibana, la ventana que da acceso a Elastic Stack.
En la sesión hablaremos sobre:
Cómo incorporar datos a Elastic Stack
Almacenamiento de datos
Análisis de los datos
Actuar en función de los datos
Explore relève les défis Big Data avec Elastic Cloud Elasticsearch
Spécialisée dans le développement et la gestion de solutions de veille documentaire et commerciale, Explore offre à ses clients une lecture précise et organisée de l’actualités des marchés et projets sur leurs territoires d'intervention. Afin de rendre leur offre plus agile et performante, Explore a choisi l’offre Elastic Cloud hébergée sur Microsoft Azure. Découvrez comment les équipes de production et de développement sont désormais en mesure de mieux exploiter les données pour les clients d’Explore et gagnent du temps sur la gestion de leur infrastructure.
Comment transformer vos données en informations exploitablesElasticsearch
Découvrez des fonctionnalités stratégiques de la Suite Elastic, notamment Elasticsearch, un moteur de données incomparable, et Kibana, véritable fenêtre ouverte sur la Suite Elastic.
Dans cette session, vous apprendrez à :
injecter des données dans la Suite Elastic ;
stocker des données ;
analyser des données ;
exploiter des données.
Transforming data into actionable insightsElasticsearch
Learn about the strategic feature areas of the Elastic Stack—Elasticsearch, a data engine like no other, and Kibana, the window into the Elastic Stack.
The session will cover:
Bringing data into the Elastic Stack
Storing data
Analyzing data
Acting on data
Empowering agencies using Elastic as a Service inside GovernmentElasticsearch
It has now been four years since the beta release of Elastic Cloud Enterprise which kicked off a wave of the Elastic public sector community running Elastic as a service within Government rather than utilizing purely hosted solutions. Fast forward to 2021 and we have multiple options for multiple mission needs. Learn top tips from Elastic architects and their experience enabling their teams with the automation and provisioning of Elastic tech to change the game in how government delivers solutions.
The opportunities and challenges of data for public goodElasticsearch
The document discusses data for public good and the opportunities and challenges involved. It notes that data infrastructure is needed to deliver public good through data. There are almost endless opportunities to use data for public services, policy, and citizen benefits. However, challenges include legacy systems, data silos, unclear governance, and risk aversion. As a case study, it outlines how the UK Census 2021 addressed index faced challenges but showed progress on using data better, with lessons for continued public sector transformation.
What's new at Elastic: Update on major initiatives and releasesElasticsearch
The first technical talk of the event will highlight the latest releases at Elastic with specific insight into how those changes impact public sector projects. See the inside view of the most important capabilities and hear predictions on the developments that will be most applicable in our industry.
Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Em...Erasmo Purificato
Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)
Scaling Connections in PostgreSQL Postgres Bangalore(PGBLR) Meetup-2 - MydbopsMydbops
This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization.
Key Takeaways:
* Understand why connection pooling is essential for high-traffic applications
* Explore various connection poolers available for PostgreSQL, including pgbouncer
* Learn the configuration options and functionalities of pgbouncer
* Discover best practices for monitoring and troubleshooting connection pooling setups
* Gain insights into real-world use cases and considerations for production environments
This presentation is ideal for:
* Database administrators (DBAs)
* Developers working with PostgreSQL
* DevOps engineers
* Anyone interested in optimizing PostgreSQL performance
Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services
Navigating Post-Quantum Blockchain: Resilient Cryptography in Quantum Threatsanupriti
In the rapidly evolving landscape of blockchain technology, the advent of quantum computing poses unprecedented challenges to traditional cryptographic methods. As quantum computing capabilities advance, the vulnerabilities of current cryptographic standards become increasingly apparent.
This presentation, "Navigating Post-Quantum Blockchain: Resilient Cryptography in Quantum Threats," explores the intersection of blockchain technology and quantum computing. It delves into the urgent need for resilient cryptographic solutions that can withstand the computational power of quantum adversaries.
Key topics covered include:
An overview of quantum computing and its implications for blockchain security.
Current cryptographic standards and their vulnerabilities in the face of quantum threats.
Emerging post-quantum cryptographic algorithms and their applicability to blockchain systems.
Case studies and real-world implications of quantum-resistant blockchain implementations.
Strategies for integrating post-quantum cryptography into existing blockchain frameworks.
Join us as we navigate the complexities of securing blockchain networks in a quantum-enabled future. Gain insights into the latest advancements and best practices for safeguarding data integrity and privacy in the era of quantum threats.
AI_dev Europe 2024 - From OpenAI to Opensource AIRaphaël Semeteys
Navigating Between Commercial Ownership and Collaborative Openness
This presentation explores the evolution of generative AI, highlighting the trajectories of various models such as GPT-4, and examining the dynamics between commercial interests and the ethics of open collaboration. We offer an in-depth analysis of the levels of openness of different language models, assessing various components and aspects, and exploring how the (de)centralization of computing power and technology could shape the future of AI research and development. Additionally, we explore concrete examples like LLaMA and its descendants, as well as other open and collaborative projects, which illustrate the diversity and creativity in the field, while navigating the complex waters of intellectual property and licensing.
In this follow-up session on knowledge and prompt engineering, we will explore structured prompting, chain of thought prompting, iterative prompting, prompt optimization, emotional language prompts, and the inclusion of user signals and industry-specific data to enhance LLM performance.
Join EIS Founder & CEO Seth Earley and special guest Nick Usborne, Copywriter, Trainer, and Speaker, as they delve into these methodologies to improve AI-driven knowledge processes for employees and customers alike.
INDIAN AIR FORCE FIGHTER PLANES LIST.pdfjackson110191
These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.
this resume for sadika shaikh bca studentSadikaShaikh7
I am a dedicated BCA student with a strong foundation in web technologies, including PHP and MySQL. I have hands-on experience in Java and Python, and a solid understanding of data structures. My technical skills are complemented by my ability to learn quickly and adapt to new challenges in the ever-evolving field of computer science.
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
Are you interested in learning about creating an attractive website? Here it is! Take part in the challenge that will broaden your knowledge about creating cool websites! Don't miss this opportunity, only in "Redesign Challenge"!
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.
AC Atlassian Coimbatore Session Slides( 22/06/2024)apoorva2579
This is the combined Sessions of ACE Atlassian Coimbatore event happened on 22nd June 2024
The session order is as follows:
1.AI and future of help desk by Rajesh Shanmugam
2. Harnessing the power of GenAI for your business by Siddharth
3. Fallacies of GenAI by Raju Kandaswamy
How Netflix Builds High Performance Applications at Global ScaleScyllaDB
We all want to build applications that are blazingly fast. We also want to scale them to users all over the world. Can the two happen together? Can users in the slowest of environments also get a fast experience? Learn how we do this at Netflix: how we understand every user's needs and preferences and build high performance applications that work for every user, every time.
What Not to Document and Why_ (North Bay Python 2024)Margaret Fero
We’re hopefully all on board with writing documentation for our projects. However, especially with the rise of supply-chain attacks, there are some aspects of our projects that we really shouldn’t document, and should instead remediate as vulnerabilities. If we do document these aspects of a project, it may help someone compromise the project itself or our users. In this talk, you will learn why some aspects of documentation may help attackers more than users, how to recognize those aspects in your own projects, and what to do when you encounter such an issue.
These are slides as presented at North Bay Python 2024, with one minor modification to add the URL of a tweet screenshotted in the presentation.
How to Avoid Learning the Linux-Kernel Memory ModelScyllaDB
The Linux-kernel memory model (LKMM) is a powerful tool for developing highly concurrent Linux-kernel code, but it also has a steep learning curve. Wouldn't it be great to get most of LKMM's benefits without the learning curve?
This talk will describe how to do exactly that by using the standard Linux-kernel APIs (locking, reference counting, RCU) along with a simple rules of thumb, thus gaining most of LKMM's power with less learning. And the full LKMM is always there when you need it!
How to Avoid Learning the Linux-Kernel Memory Model
Realizing your AIOps goals with machine learning in Elastic
1. Realizing your AIOps goals with
Elastic Machine Learning
Mukesh Gadiya | Sr. Manager, Product Management
Tom Grabowski | Principal Product Manager
Jim Avazpour | Director of Infrastructure @Cerner
2. This presentation and the accompanying oral presentation contain forward-looking statements, including statements
concerning plans for future offerings; the expected strength, performance or benefits of our offerings; and our future
operations and expected performance. These forward-looking statements are subject to the safe harbor provisions
under the Private Securities Litigation Reform Act of 1995. Our expectations and beliefs in light of currently
available information regarding these matters may not materialize. Actual outcomes and results may differ materially
from those contemplated by these forward-looking statements due to uncertainties, risks, and changes in
circumstances, including, but not limited to those related to: the impact of the COVID-19 pandemic on our business
and our customers and partners; our ability to continue to deliver and improve our offerings and successfully
develop new offerings, including security-related product offerings and SaaS offerings; customer acceptance and
purchase of our existing offerings and new offerings, including the expansion and adoption of our SaaS offerings;
our ability to realize value from investments in the business, including R&D investments; our ability to maintain and
expand our user and customer base; our international expansion strategy; our ability to successfully execute our
go-to-market strategy and expand in our existing markets and into new markets, and our ability to forecast customer
retention and expansion; and general market, political, economic and business conditions.
Additional risks and uncertainties that could cause actual outcomes and results to differ materially are included in
our filings with the Securities and Exchange Commission (the “SEC”), including our Annual Report on Form 10-K for
the most recent fiscal year, our quarterly report on Form 10-Q for the most recent fiscal quarter, and any
subsequent reports filed with the SEC. SEC filings are available on the Investor Relations section of Elastic’s
website at ir.elastic.co and the SEC’s website at www.sec.gov.
Any features or functions of services or products referenced in this presentation, or in any presentations, press
releases or public statements, which are not currently available or not currently available as a general availability
release, may not be delivered on time or at all. The development, release, and timing of any features or functionality
described for our products remains at our sole discretion. Customers who purchase our products and services
should make the purchase decisions based upon services and product features and functions that are currently
available.
All statements are made only as of the date of the presentation, and Elastic assumes no obligation to, and does not
currently intend to, update any forward-looking statements or statements relating to features or functions of services
or products, except as required by law.
Forward-Looking Statements
3. By 2023, 40% of DevOps teams will augment
app and infra monitoring tools with AIOps
platform capabilities to decrease mean time
to problem resolution and the resultant
operational costs.
Gartner Market Guide for AIOps Platform,
November 2019
4. 4
What constitutes an AIOps platform?*
• Ingesting data from various sources for cross-domain analysis
• Real time anomaly detection based on historical data analysis
• Storing and providing access to the raw data
• Suggesting prescriptive responses to analysis
• Initiating an action or next step based on the prescription
* Gartner Market Guide for AIOps Platform, November 2019
5. 5
* Gartner Market Guide for AIOps Platform, November 2019
What constitutes an AIOps platform?*
• Ingesting data from various sources for cross-domain analysis
• Real time anomaly detection based on historical data analysis
• Storing and providing access to the raw data
• Suggesting prescriptive responses to analysis
• Initiating an action or next step based on the prescription
6. 6
Machine Learning
Anomaly Detection
10 years development & industry
leading technology
Unsupervised machine learning
Automatically detect anomalies, outliers from
group, and rare events
Sophisticated ML Job UI
Interactive views of model and anomaly
scoring
Root cause analysis
Report on factors influencing anomalies
On-Demand Forecasting
Forecast out time series metrics
7. AIOps outcomes enabled by Elastic Machine Learning
• Reduce MTTR for SREs (Demo)
– Alert noise reduction
– Anomalies correlation
– Root cause analysis
– Log categories
• Reduce time to value for Dev teams
– Auto-grok for custom log parsing
– One click ML integration in APM, Logs, Infra metrics and Synthetics
9. • Cerner Corporation is a
supplier of healthcare
information technology
HCIT systems, services,
devices and hardware
– 29,000 employees
in 30 countries
• Cerner’s Millennium Service
provides Electronic Medical
Record (EMR access to
27,000 customers
(hospitals, doctors, etc.) in
26 countries
• Cerner Network
– 19 Data Centers (11 in US
– Carrier Grade Network
– 170,000 servers
– 1,900 circuits
– Nationwide fiber rings
– 560,000 network ports
– 500 Remote Hosted Clients
– 260,000 Peak Concurrent Users
Who is Cerner?
10. Large Scale Infrastructure Monitoring Challenges
• Data Volume
– Require cost-effective, scalable and resilient ingestion platforms
• Gaps In Monitoring Resolutions
– Data Feed 1 DF1 industry standard alert intervals are set too high to reduce noise
– Data Feed 2 DF2 every violation must generate an alert
– DF1 vs DF2 not all alerts need to be console bound
• Lower MTTK & MTTR
– Alert tagging for service to resource mapping
– Grouping and categorizing service-related violations
• Utilizing Machine Learning
– Baselining, Deviation from normal
– Identifying abnormalities
11. Thank You!
• Sign up on Elastic Cloud and try the power of Elastic ML
○ https://cloud.elastic.co/registration
• Elastic ML Case studies
○ Cerner, TMobile, Sky, PostBank, ETrade, IHG
• Elastic ML Forum
○ https://discuss.elastic.co/tag/stack-machine-learning
13. Delivering ML solutions throughout the data science process
Machine Learning end-to-end methodology
Define a ML
problem and
propose a
solution
Construct your
dataset
Transform data Train a model
Use the model
to make
predictions
Elastic Stack delivers an end-to-end machine
learning pipeline providing the path from raw
data to building, testing, and deploying
machine learning models in production
14. 14
Which customers are likely to churn?
Machine Learning end-to-end methodology
{ "customer_id": "028fa21e", "session_id": "MA0l6PC5", "@timestamp":
"2019-05-08T18:46:22", "request_type": "streaming_tv", "channel": "bbc",
"title": "Line of Duty" }
{ "customer_id": "a4ca7c7c", "session_id": "LMSXQXHg", "@timestamp":
"2019-05-08T18:49:34", "request_type": "streaming_film", "channel":
"ziggo", "title": "Glass" }
{ "customer_id": "avad97s3", "session_id": "LMSXQXHg", "@timestamp":
"2019-05-08T18:50:34", "request_type": "streaming_film", "channel":
"ziggo", "title": "Glass" }
{ "customer_id": "dce909a0", "session_id": "MA0l6PC5", "@timestamp":
"2019-05-08T18:51:23", "request_type": "streaming_film", "channel":
"ziggo", "title": "Glass" }
{ "customer_id": "vfva09a09", "session_id": "LMSXQXHg", "@timestamp":
"2019-05-08T18:52:14", "request_type": "streaming_film", "channel":
"ziggo", "title": "Glass" }
{ "customer_id": "sdfd9s90", "session_id": "MA0l6PC5", "@timestamp":
"2019-05-08T18:54:51", "request_type": "streaming_film", "channel":
"ziggo", "title": "Glass" }
...
Data is often raw logs
Define a ML
problem and
propose a
solution
Construct your
dataset
Transform data Train a model
Use the model
to make
predictions
Customer behavior is often described
by aggregate features
15. 15
Transform raw data to a feature index
Machine Learning end-to-end methodology
Define a ML
problem and
propose a
solution
Construct your
dataset
Transform
data
Train a model
Use the model
to make
predictions
{
"customer_id": "028fa21e",
"session_id": "MA0l6PC5",
"@timestamp": "2019-05-08T18:46:22",
"request_type": "streaming_tv",
"channel": "bbc",
"title": "Line of Duty"
},
{
"customer_id": "a4ca7c7c",
"session_id": "LMSXQXHg",
"@timestamp": "2019-05-08T18:49:34",
"request_type": "streaming_film",
"channel": "ziggo",
"title": "Glass"
},
...
PUT _transform/customer_behaviour
{
"source": {
"index": ["viewing_logs"]
},
"description": "Pivot viewing logs to customer-centric index",
"dest": {"index": "customer_behaviour"},
"pivot": {
"group_by": {
"customer_id": {"terms":{"field": "customer_id"}
}
},
"aggregations": {
"total_tv_shows": {...},
"total_films": {...},
...
}
}
}
{
"customer_id": "028fa21e",
"total_tv_shows": 10,
"total_films": 2,
"total_watching_duration": 72123,
"last_active": "019-05-08T18:46:22",
...
},
{
"customer_id": "a4ca7c7c",
"total_tv_shows": 23,
"total_films": 8,
"total_watching_duration": 184212,
"last_active": "2019-05-08T18:49:34",
...
},
...
RAW Data Customer Index
16. Build a model on historical data that has a churn indicator
Machine Learning end-to-end methodology
Define a ML
problem and
propose a
solution
Construct your
dataset
Transform data Train a model
Use the model
to make
predictions
customer a customer b
total duration
of customer
sessions 80:21:07 1:01:11
tv episodes
watched 24 1
films watched
in last month 5 0
newness of
titles watched
in last month 9.8 1.2
Change in
duration 6:22:17 16:43:29
subscription
plan gold platinum
customer tenure 32 26
has churned? no yes
ML Supervised
Model
train/validate/test
Model Name: churn_e2r21
Model Precision: 96.3%
Model Recall: 95.7%
Model F1 score: 96.0%
17. 17
Use model inference to make predictions on streaming data
Machine Learning end-to-end methodology
Define a ML
problem and
propose a
solution
Construct your
dataset
Transform data Train a model
Use the model
to make
predictions
customer c
total duration of
customer sessions 10:10:06
tv episodes
watched 2
films watched in
last month 1
newness of titles
watched in last
month 1.6
change in duration
this month 17:22:17
customer plan gold
customer tenure 5
customer c
Feature
Influence
total duration of
customer sessions 10:10:06 0.1
tv episodes
watched 2 0.8
films watched in
last month 1 0.8
newness of titles
watched in last
month 1.6 0.01
change in
duration of this
month 17:22:17 0.6
customer plan gold 0.01
customer tenure 5 0.1
will churn? p(churn) = 97%
ML Supervised
Model
predict