These are the Linked Data Applications slides that we presented at the Consuming Linked Data tutorial at WWW2010 in Raleigh, NC on April 26, 2010.
This slide set was not part of our tutorial that was presented at ISWC2009
This document provides information and advice about applying for the National Science Foundation Graduate Research Fellowship. It discusses key details of the fellowship such as eligibility requirements, funding amounts, and required application materials. The fellowship is highly competitive, so applicants are advised to spend 20 hours per week preparing their application, which must demonstrate both intellectual merit of the proposed research and its potential broader impacts. Strong letters of recommendation, personal and research statements, and proposing a feasible research plan are essential. Overall, the document offers guidance on crafting a competitive application by being specific, tying different parts together, and focusing on uniqueness.
The document discusses the Semantic Web and linked data. It defines the current web as consisting of documents linked by hyperlinks that are readable by humans but difficult for computers to understand. The Semantic Web aims to publish structured data on the web using common standards like RDF so that data can be linked, queried, and integrated across sources. Key points include:
- The Semantic Web uses RDF to represent data as a graph so that data from different sources can be linked together.
- Linked data follows principles like using URIs to identify things and including links to other related data.
- Query languages like SPARQL allow searching and integrating linked data from multiple sources.
- There are now
El documento habla sobre la Web Sem¨¢ntica. Explica que la Web Sem¨¢ntica publica datos estructurados usando RDF para que los datos puedan vincularse y ser integrados entre s¨ª. Tambi¨¦n describe c¨®mo grandes empresas como Google, Facebook y el gobierno est¨¢n usando RDF y c¨®mo la Web Sem¨¢ntica permitir¨¢ buscar y encontrar informaci¨®n de una manera m¨¢s efectiva en el futuro.
Consuming Linked Data by Machines - WWW2010Juan Sequeda
?
These are the Consuming Linked Data by Machines slides that we presented at the Consuming Linked Data tutorial at WWW2010 in Raleigh, NC on April 26, 2010. These slides are originally by Patrick Sinclair from BBC
Welcome to Linked Data 0/5 Semtech2011Juan Sequeda
?
This document discusses creating, publishing and consuming linked data. It introduces key concepts related to linked data including HTML, CSS, HTTP, XML, JSON, API, URL, URI, RDF, RDFa, RDFS, OWL, RIF and SPARQL. The document includes a schedule but provides no further details.
The document provides an overview of the Semantic Web and linked data. It defines the Semantic Web as publishing structured data on the web in a format that computers can understand, rather than just documents. Linked data follows principles like using URIs to identify things and linking data across sources to integrate information. Query languages like SPARQL can then be used to search across linked data. Examples show how data can be published as RDF and linked to create a global database. Applications that consume and combine linked data from multiple sources are discussed.
Drupal 7 and Semantic Web Hands-on TutorialJuan Sequeda
?
This document outlines the schedule and details for a seminar on using Drupal 7 for the Semantic Web. The day-long event includes sessions on rich snippets, an introduction to the Semantic Web, and hands-on advanced topics using Semantic Web technologies with Drupal. The schedule also lists times for registration, breaks, lunch, and a happy hour reception. Background is provided on one of the speakers, St¨¦phane Corlosquet, who has significantly contributed RDF and Semantic Web capabilities to Drupal.
Linked Data is a set of best practices for publishing data on the Web using standardized data models (RDF) and access methods (HTTP), enabling easier integration of data from different sources compared to proprietary APIs. The Linked Data architecture is open and allows discovery of new data sources at runtime, allowing applications to take advantage of new available data. When publishing Linked Data, considerations include linking to other datasets, and providing provenance, licensing, and access metadata using common vocabularies. Linked Data principles can also be applied within intranets for data integration.
Virtualizing Relational Databases as Graphs: a multi-model approachJuan Sequeda
?
Talk given at Smart Data 2017
Relational Databases are inflexible due to the rigid constraints of the relational data model. If you have new data that doesn¡¯t fit your schema, you will need to alter your schema (add a column or a new table). This is a task that is not always possible. IT departments don't have time, or they won't allow it - just more nulls that can lead to query performance degradation, etc.
A goal of graph databases is to address this problem with their schema-less graph data model. However, many businesses have large investments in commercial RDBMSs and their associated applications and can't expect to move all of their data to a graph database.
In this talk, I will present a multi-model graph/relational architecture solution. Keep your relational data where it is, virtualize it as a graph, and then connect it with additional data stored in a graph database. This way, both graph and relational technologies can seamlessly interact together.
Consuming Linked Data by Humans - WWW2010Juan Sequeda
?
This document discusses different ways that humans can consume linked data on the web. It describes HTML browsers that can render RDFa embedded in web pages. It also discusses linked data browsers that allow users to view RDF triples in a tabular format. Faceted browsers provide a way to explore linked data through interactive facets. On-the-fly mashups dynamically combine data from multiple sources. The document encourages the development of new and innovative interfaces for interacting with linked data.
Presentation at Data/Graph Day Texas Conference.
Austin, Texas
January 14, 2017
This talk grew out Juan Sequeda's office hours following the Seattle Graph Meetup. Some of the questions posed were: How do I recognize problem best solved with a graph solution? How do I determine the best type of graph to solve the problem? How do I manage the data where both graph and relational operations will be performed? Juan did such a great job of explaining the options, we asked him to develop his responses into a formal talk.
Graph Query Languages: update from LDBCJuan Sequeda
?
The Linked Data Benchmark Council (LDBC) is a non-profit organization dedicated to establishing benchmarks, benchmark practices and benchmark results for graph data management software. The Graph Query Language task force of LDBC is studying query languages for graph data management systems, and specifically those systems storing so-called Property Graph data. The goals of the GraphQL task force are to:
Devise a list of desired features and functionalities of a graph query language.
Evaluate a number of existing languages (i.e. Cypher, Gremlin, PGQL, SPARQL, SQL), and identify possible issues.
Provide a better understanding of the design space and state-of-the-art.
Develop proposals for changes to existing query languages or even a new graph query language.
This query language should cover the needs of the most important use-cases for such systems, such as social network and Business Intelligence workloads.
This talk will present an update of the work accomplished by the LDBC GraphQL task force. We also look for input from the graph community.
Publishing Linked Data 3/5 Semtech2011Juan Sequeda
?
This document summarizes techniques for publishing linked data on the web. It discusses publishing static RDF files, embedding RDF in HTML using RDFa, linking to other URIs, generating linked data from relational databases using RDB2RDF tools, publishing linked data from triplestores and APIs, hosting linked data in the cloud, and testing linked data quality.
Open Research Problems in Linked Data - WWW2010Juan Sequeda
?
These are the Open Research Problems of Linked Data slides that we presented at the Consuming Linked Data tutorial at WWW2010 in Raleigh, NC on April 26, 2010
WTF is the Semantic Web and Linked DataJuan Sequeda
?
This document provides an overview of the Semantic Web and Linked Data. It begins by explaining some of the limitations of the current web, which treats all content as unstructured documents rather than structured data. It then introduces the Semantic Web and its data model, RDF, which allows publishing structured data on the web in a standardized way using graph-based representations. This enables linking different data sources on the web, addressing the problem of data silos. The document provides examples of representing bibliographic data about books in RDF and linking it to other datasets, demonstrating how the Semantic Web enables integrating and finding related information on the web.
My Linked Data tutorial presentation that I presented at Semtech 2012.
http://semtechbizsf2012.semanticweb.com/sessionPop.cfm?confid=65&proposalid=4724
This document provides an introduction to linked data and the semantic web. It discusses how the current web contains documents that are difficult for computers to understand, but linked data publishes structured data on the web using common standards like RDF and URIs. This allows data to be interlinked and queried using SPARQL. Publishing data as linked data makes the web appear as one huge global database. There are now many incentives for organizations to publish their data as linked data, as it enables data sharing and integration in addition to potential benefits like semantic search engine optimization. Linked data is a growing trend with many large organizations and governments now publishing data.
This document discusses various approaches for building applications that consume linked data from multiple datasets on the web. It describes characteristics of linked data applications and generic applications like linked data browsers and search engines. It also covers domain-specific applications, faceted browsers, SPARQL endpoints, and techniques for accessing and querying linked data including follow-up queries, querying local caches, crawling data, federated query processing, and on-the-fly dereferencing of URIs. The advantages and disadvantages of each technique are discussed.
This document discusses Linked Data and the best practices for publishing and interlinking data on the web. It covers four main principles:
1) Use URIs as names for things and identify real-world objects with HTTP URIs.
2) Use HTTP URIs so that people can look up those names by dereferencing the URIs.
3) Provide useful RDF information when URIs are dereferenced, using formats like RDF/XML, RDFa, N3, or Turtle.
4) Include links to other URIs to discover more related things and connect isolated data silos. This allows data to be interlinked on the Web.
This document introduces linked data and discusses how publishing data as linked RDF triples on the web allows for a global linked database. It explains that linked data uses HTTP URIs to identify things and links data from different sources to be queried using SPARQL. Publishing linked data provides benefits like being able to integrate and discover related data on the web. Tools are available to convert existing data or publish new data as linked open data.
The document discusses the evolution of the web from a "Web of Documents" to a "Semantic Web." It argues that while the vision of a Semantic Web proposed in 2001 has yet to be fully realized, the pieces are falling into place. Examples of linked open data projects show how structured data from sources like Wikipedia, the BBC, and government data is being interconnected using semantic web technologies. The use of semantics on the web brings value by sometimes solving narrow problems and linking data beyond specific applications.
The 5th AIS SigPrag International Pragmatic Web Conference Track (ICPW 2010) at the International Conference on Semantic Systems (i-Semantics 2010), 1 - 3 September 2010, Messecongress|Graz, Austria.
This document summarizes a project to mine and analyze over 1.3 million legal texts from the Brazilian Supreme Court. It involved web scraping the documents, parsing the HTML, storing the data in MySQL and MongoDB databases, applying natural language processing and pattern matching techniques, and visualizing the results using tools like Matplotlib, Ubigraph and Gource. The goal was to better understand the information and relationships within the large corpus of legal texts.
1. The document discusses the history and future of semantic web technologies, including lessons learned and trends. It notes that semantic web's strength is in data aggregation rather than data management.
2. Two scenarios involving expressing claims in RDFa and linking from a homepage are presented, showing how trust can come from linked information.
3. Recent and emerging trends in user interfaces, search engines, and services are moving towards a more machine-readable web where pages make claims and datasets are interconnected.
Drupal 7 and Semantic Web Hands-on TutorialJuan Sequeda
?
This document outlines the schedule and details for a seminar on using Drupal 7 for the Semantic Web. The day-long event includes sessions on rich snippets, an introduction to the Semantic Web, and hands-on advanced topics using Semantic Web technologies with Drupal. The schedule also lists times for registration, breaks, lunch, and a happy hour reception. Background is provided on one of the speakers, St¨¦phane Corlosquet, who has significantly contributed RDF and Semantic Web capabilities to Drupal.
Linked Data is a set of best practices for publishing data on the Web using standardized data models (RDF) and access methods (HTTP), enabling easier integration of data from different sources compared to proprietary APIs. The Linked Data architecture is open and allows discovery of new data sources at runtime, allowing applications to take advantage of new available data. When publishing Linked Data, considerations include linking to other datasets, and providing provenance, licensing, and access metadata using common vocabularies. Linked Data principles can also be applied within intranets for data integration.
Virtualizing Relational Databases as Graphs: a multi-model approachJuan Sequeda
?
Talk given at Smart Data 2017
Relational Databases are inflexible due to the rigid constraints of the relational data model. If you have new data that doesn¡¯t fit your schema, you will need to alter your schema (add a column or a new table). This is a task that is not always possible. IT departments don't have time, or they won't allow it - just more nulls that can lead to query performance degradation, etc.
A goal of graph databases is to address this problem with their schema-less graph data model. However, many businesses have large investments in commercial RDBMSs and their associated applications and can't expect to move all of their data to a graph database.
In this talk, I will present a multi-model graph/relational architecture solution. Keep your relational data where it is, virtualize it as a graph, and then connect it with additional data stored in a graph database. This way, both graph and relational technologies can seamlessly interact together.
Consuming Linked Data by Humans - WWW2010Juan Sequeda
?
This document discusses different ways that humans can consume linked data on the web. It describes HTML browsers that can render RDFa embedded in web pages. It also discusses linked data browsers that allow users to view RDF triples in a tabular format. Faceted browsers provide a way to explore linked data through interactive facets. On-the-fly mashups dynamically combine data from multiple sources. The document encourages the development of new and innovative interfaces for interacting with linked data.
Presentation at Data/Graph Day Texas Conference.
Austin, Texas
January 14, 2017
This talk grew out Juan Sequeda's office hours following the Seattle Graph Meetup. Some of the questions posed were: How do I recognize problem best solved with a graph solution? How do I determine the best type of graph to solve the problem? How do I manage the data where both graph and relational operations will be performed? Juan did such a great job of explaining the options, we asked him to develop his responses into a formal talk.
Graph Query Languages: update from LDBCJuan Sequeda
?
The Linked Data Benchmark Council (LDBC) is a non-profit organization dedicated to establishing benchmarks, benchmark practices and benchmark results for graph data management software. The Graph Query Language task force of LDBC is studying query languages for graph data management systems, and specifically those systems storing so-called Property Graph data. The goals of the GraphQL task force are to:
Devise a list of desired features and functionalities of a graph query language.
Evaluate a number of existing languages (i.e. Cypher, Gremlin, PGQL, SPARQL, SQL), and identify possible issues.
Provide a better understanding of the design space and state-of-the-art.
Develop proposals for changes to existing query languages or even a new graph query language.
This query language should cover the needs of the most important use-cases for such systems, such as social network and Business Intelligence workloads.
This talk will present an update of the work accomplished by the LDBC GraphQL task force. We also look for input from the graph community.
Publishing Linked Data 3/5 Semtech2011Juan Sequeda
?
This document summarizes techniques for publishing linked data on the web. It discusses publishing static RDF files, embedding RDF in HTML using RDFa, linking to other URIs, generating linked data from relational databases using RDB2RDF tools, publishing linked data from triplestores and APIs, hosting linked data in the cloud, and testing linked data quality.
Open Research Problems in Linked Data - WWW2010Juan Sequeda
?
These are the Open Research Problems of Linked Data slides that we presented at the Consuming Linked Data tutorial at WWW2010 in Raleigh, NC on April 26, 2010
WTF is the Semantic Web and Linked DataJuan Sequeda
?
This document provides an overview of the Semantic Web and Linked Data. It begins by explaining some of the limitations of the current web, which treats all content as unstructured documents rather than structured data. It then introduces the Semantic Web and its data model, RDF, which allows publishing structured data on the web in a standardized way using graph-based representations. This enables linking different data sources on the web, addressing the problem of data silos. The document provides examples of representing bibliographic data about books in RDF and linking it to other datasets, demonstrating how the Semantic Web enables integrating and finding related information on the web.
My Linked Data tutorial presentation that I presented at Semtech 2012.
http://semtechbizsf2012.semanticweb.com/sessionPop.cfm?confid=65&proposalid=4724
This document provides an introduction to linked data and the semantic web. It discusses how the current web contains documents that are difficult for computers to understand, but linked data publishes structured data on the web using common standards like RDF and URIs. This allows data to be interlinked and queried using SPARQL. Publishing data as linked data makes the web appear as one huge global database. There are now many incentives for organizations to publish their data as linked data, as it enables data sharing and integration in addition to potential benefits like semantic search engine optimization. Linked data is a growing trend with many large organizations and governments now publishing data.
This document discusses various approaches for building applications that consume linked data from multiple datasets on the web. It describes characteristics of linked data applications and generic applications like linked data browsers and search engines. It also covers domain-specific applications, faceted browsers, SPARQL endpoints, and techniques for accessing and querying linked data including follow-up queries, querying local caches, crawling data, federated query processing, and on-the-fly dereferencing of URIs. The advantages and disadvantages of each technique are discussed.
This document discusses Linked Data and the best practices for publishing and interlinking data on the web. It covers four main principles:
1) Use URIs as names for things and identify real-world objects with HTTP URIs.
2) Use HTTP URIs so that people can look up those names by dereferencing the URIs.
3) Provide useful RDF information when URIs are dereferenced, using formats like RDF/XML, RDFa, N3, or Turtle.
4) Include links to other URIs to discover more related things and connect isolated data silos. This allows data to be interlinked on the Web.
This document introduces linked data and discusses how publishing data as linked RDF triples on the web allows for a global linked database. It explains that linked data uses HTTP URIs to identify things and links data from different sources to be queried using SPARQL. Publishing linked data provides benefits like being able to integrate and discover related data on the web. Tools are available to convert existing data or publish new data as linked open data.
The document discusses the evolution of the web from a "Web of Documents" to a "Semantic Web." It argues that while the vision of a Semantic Web proposed in 2001 has yet to be fully realized, the pieces are falling into place. Examples of linked open data projects show how structured data from sources like Wikipedia, the BBC, and government data is being interconnected using semantic web technologies. The use of semantics on the web brings value by sometimes solving narrow problems and linking data beyond specific applications.
The 5th AIS SigPrag International Pragmatic Web Conference Track (ICPW 2010) at the International Conference on Semantic Systems (i-Semantics 2010), 1 - 3 September 2010, Messecongress|Graz, Austria.
This document summarizes a project to mine and analyze over 1.3 million legal texts from the Brazilian Supreme Court. It involved web scraping the documents, parsing the HTML, storing the data in MySQL and MongoDB databases, applying natural language processing and pattern matching techniques, and visualizing the results using tools like Matplotlib, Ubigraph and Gource. The goal was to better understand the information and relationships within the large corpus of legal texts.
1. The document discusses the history and future of semantic web technologies, including lessons learned and trends. It notes that semantic web's strength is in data aggregation rather than data management.
2. Two scenarios involving expressing claims in RDFa and linking from a homepage are presented, showing how trust can come from linked information.
3. Recent and emerging trends in user interfaces, search engines, and services are moving towards a more machine-readable web where pages make claims and datasets are interconnected.
RDA implementation is scheduled for March 31, 2013. Testers of RDA recommended improvements like rewriting instructions in plain English and ensuring community involvement. Differences from AACR2 include lack of abbreviations, more transcription of what is seen, and new fields in MARC like 336, 337, 338 for content/media/carrier types. Linked data and semantic web approaches may make relationships between works more explicit over time. Preparing for RDA involves decisions about cataloging workflows and training.
En esta charla compartiremos la experiencia del equipo de Bitnami en la mejora de la seguridad de nuestros Helm Charts y Contenedores utilizando Kubescape como herramienta principal de validaci¨®n. Exploraremos el proceso completo, desde la identificaci¨®n de necesidades hasta la implementaci¨®n de validaciones automatizadas, incluyendo la creaci¨®n de herramientas para la comunidad.
Compartiremos nuestra experiencia en la implementaci¨®n de mejoras de seguridad en Charts y Contenedores, bas¨¢ndonos en las mejores pr¨¢cticas del mercado y utilizando Kubescape como herramienta de validaci¨®n. Explicaremos c¨®mo automatizamos estas validaciones integr¨¢ndolas en nuestro ciclo de vida de desarrollo, mejorando significativamente la seguridad de nuestros productos mientras manten¨ªamos la eficiencia operativa.
Durante la charla, los asistentes aprender¨¢n c¨®mo implementar m¨¢s de 60 validaciones de seguridad cr¨ªticas, incluyendo la configuraci¨®n segura de contenedores en modo no privilegiado, la aplicaci¨®n de buenas pr¨¢cticas en recursos de Kubernetes, y c¨®mo garantizar la compatibilidad con plataformas como OpenShift. Adem¨¢s, demostraremos una herramienta de self-assessment que desarrollamos para que cualquier usuario pueda evaluar y mejorar la seguridad de sus propios Charts bas¨¢ndose en esta experiencia.
Mastering Azure Durable Functions - Building Resilient and Scalable WorkflowsCallon Campbell
?
The presentation aims to provide a comprehensive understanding of how Azure Durable Functions can be used to build resilient and scalable workflows in serverless applications. It includes detailed explanations, application patterns, components, and constraints of Durable Functions, along with performance benchmarks and new storage providers.
Building High-Impact Teams Beyond the Product Triad.pdfRafael Burity
?
The product triad is broken.
Not because of flawed frameworks, but because it rarely works as it should in practice.
When it becomes a battle of roles, it collapses.
It only works with clarity, maturity, and shared responsibility.
Most people might think of a water faucet or even the tap on a keg of beer. But in the world of networking, "TAP" stands for "Traffic Access Point" or "Test Access Point." It's not a beverage or a sink fixture, but rather a crucial tool for network monitoring and testing. Khushi Communications is a top vendor in India, providing world-class Network TAP solutions. With their expertise, they help businesses monitor, analyze, and secure their networks efficiently.
AI in Talent Acquisition: Boosting HiringBeyond Chiefs
?
AI is transforming talent acquisition by streamlining recruitment processes, enhancing decision-making, and delivering personalized candidate experiences. By automating repetitive tasks such as resume screening and interview scheduling, AI significantly reduces hiring costs and improves efficiency, allowing HR teams to focus on strategic initiatives. Additionally, AI-driven analytics help recruiters identify top talent more accurately, leading to better hiring decisions. However, despite these advantages, organizations must address challenges such as AI bias, integration complexities, and resistance to adoption to fully realize its potential. Embracing AI in recruitment can provide a competitive edge, but success depends on aligning technology with business goals and ensuring ethical, unbiased implementation.
Build Your Uber Clone App with Advanced FeaturesV3cube
?
Build your own ride-hailing business with our powerful Uber clone app, fully equipped with advanced features to give you a competitive edge. Start your own taxi business today!
More Information : https://www.v3cube.com/uber-clone/
Columbia Weather Systems offers professional weather stations in basically three configurations for industry and government agencies worldwide: Fixed-Base or Fixed-Mount Weather Stations, Portable Weather Stations, and Vehicle-Mounted Weather Stations.
Models include all-in-one sensor configurations as well as modular environmental monitoring systems. Real-time displays include hardware console, WeatherMaster? Software, and a Weather MicroServer? with industrial protocols, web and app monitoring options.
Innovative Weather Monitoring: Trusted by industry and government agencies worldwide. Professional, easy-to-use monitoring options. Customized sensor configurations. One-year warranty with personal technical support. Proven reliability, innovation, and brand recognition for over 45 years.
The Future of Materials: Transitioning from Silicon to Alternative Metalsanupriti
?
This presentation delves into the emerging technologies poised to revolutionize the world of computing. From carbon nanotubes and graphene to quantum computing and DNA-based systems, discover the next-generation materials and innovations that could replace or complement traditional silicon chips. Explore the future of computing and the breakthroughs that are shaping a more efficient, faster, and sustainable technological landscape.
Why Outsource Accounting to India A Smart Business Move!.pdfanjelinajones6811
?
Outsource Accounting to India to reduce costs, access skilled professionals, and streamline financial operations. Indian accounting firms offer expert services, advanced technology, and round-the-clock support, making it a smart choice for businesses looking to improve efficiency and focus on growth.
How Telemedicine App Development is Revolutionizing Virtual Care.pptxDash Technologies Inc
?
Telemedicine app development builds software for remote doctor consultations and patient check-ups. These apps bridge healthcare professionals with patients via video calls, secure messages, and interactive interfaces. That helps practitioners to provide care without immediate face-to-face interactions; hence, simplifying access to medical care. Telemedicine applications also manage appointment scheduling, e-prescribing, and sending reminders.
Telemedicine apps do not only conduct remote consultations. They also integrate with entire healthcare platforms, such as patient forums, insurance claims processing, and providing medical information libraries. Remote patient monitoring enables providers to keep track of patients' vital signs. This helps them intervene and provide care whenever necessary. Telehealth app development eliminates geographical boundaries and facilitates easier communication.
In this blog, we will explore its market growth, essential features, and benefits for both patients and providers.
Threat Modeling a Batch Job System - AWS Security Community DayTeri Radichel
?
I've been working on building a batch job framework for a few years now and blogging about it in the process. This presentation explains how and why I started building and writing about this system and the reason it changed from deploying one simple batch job to a much bigger project. I explore a number of recent data breaches, how they occurred, and what may have prevented them along the way. We consider how what make goes into an effective security architecture and well-designed security controls that avoid common pitfalls. There are friend links to many blog posts in the notes of the presentation that bypass the paywall. Topics include security architecture, IAM, encryption (KMS), networking, MFA, source control, separation of duties, supply chain attacks, and more.
Least Privilege AWS IAM Role PermissionsChris Wahl
?
RECORDING: https://youtu.be/hKepiNhtWSo
Hello innovators! Welcome to the latest episode of My Essentials Course series. In this video, we'll delve into the concept of least privilege for IAM roles, ensuring roles have the minimum permissions needed for success. Learn strategies to create read-only, developer, and admin roles. Discover tools like IAM Access Analyzer, Pike, and Policy Sentry for generating efficient IAM policies. Follow along as we automate role and policy creation using Pike with Terraform, and test our permissions using GitHub Actions. Enhance your security practices by integrating these powerful tools. Enjoy the video and leave your feedback in the comments!
GDG on Campus Monash hosted Info Session to provide details of the Solution Challenge to promote participation and hosted networking activities to help participants find their dream team
Smarter RAG Pipelines: Scaling Search with Milvus and FeastZilliz
?
About this webinar
Learn how Milvus and Feast can be used together to scale vector search and easily declare views for retrieval using open source. We¡¯ll demonstrate how to integrate Milvus with Feast to build a customized RAG pipeline.
Topics Covered
- Leverage Feast for dynamic metadata and document storage and retrieval, ensuring that the correct data is always available at inference time
- Learn how to integrate Feast with Milvus to support vector-based retrieval in RAG systems
- Use Milvus for fast, high-dimensional similarity search, enhancing the retrieval phase of your RAG model