Wolfgang Krause

Fellow in-house taxonomists, ontologists, metadata managers, and semantic wizards: what is the name of the team or business unit that you work within? I have the good fortune of getting to rename the "Content Standards" program I lead at Kaiser Permanente IT. My team has determined that the name is too vague to convey the core activities of the program, and leads to confusion with the cross-functional teams we collaborate with. I'm looking for a clear name that will resonate with stakeholders within my org. So I've got a very classic taxonomist question: What to call the program? 🤔 For some context... What my program is responsible for: - Defining and governing standards for metadata, structured data, taxonomy, and ontology for classifying/structuring digital content - Implementing processes and platforms to good metadata hygiene - Assessment and remediation of content metadata What my program doesn't cover: - Writing or editorial style guides - Content design - Content strategy - Site maps or navigational structure - Data governance Any ideas are most welcome!

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Jay is spot on here "One of the biggest barriers to AI innovation is access to clean and trustworthy data. The truth is, while you can have a great AI/ML model, without contextual, real-time data from your business, the model is useless for most applications. Connecting AI models to enterprise data in real-time is one of the more challenging problems to solve." Many may think they are "doing AI" but until this bigger, more challenging problem is solved, AI will have little impact in the enterprise.

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Readpaper’s AI agent infrastructure is evolving as well.

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Whether you’re a developer or a business practitioner — #GenAI impacts everyone. Join Varun Grover and Distinguished Engineer Jayant Thomas to get advice on starting your #AI journey. Watch now: https://vrt.as/4a5vLzB

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A few thoughts on #LLM usage in the small, prompted by my toying with #llama3 and #phi-3 on consumer hardware.

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Let’s face it, LLMs aren’t without limitations. In this discussion, Varun Grover shares the benefits of a RAG framework and provides an overview of how it works as well as practical use cases. Watch now: https://vrt.as/43MNQQx #AI #GenAI #LLM #RAG

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Ready for AI and Next Gen Workloads? Introducing Google Cloud NetApp Volumes Flex volumes, auto-tiering, and more

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ML Console was designed by Salesforce engineers with a singular focus: To increase developer productivity in AI model management. Learn how they persevered through technical challenges to do just that:

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At Intuit, ensuring resilience of our critical systems is paramount. In this blog post, my colleagues Deepthi Panthula, Emily Pike and I share insights into how we run 'gameday' simulations at scale. Learn about the key principles and elements behind our effective gameday program that drives continuous improvement across people, processes and tools to handle major incidents or outages seamlessly. Check it out and let us know what you think! #Reliability #Resilience #IncidentManagement

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Large Language Models (#LLMs) are evolving rapidly, and the demand for running them at scale is skyrocketing. #Ollama is stepping up to meet this need with its official container image on Docker Hub, excelling in local #deployments. However, there’s a catch for containerized environments: downloading #models requires the ollama serve command to be running within the #container. This extra step complicates automated deployments via #CI #CD #pipelines, making seamless model access challenging. To take your #model to production, it needs to be #container-ready—a feature Ollama has yet to fully support. The gap in #AIOps for smooth deployments gets blurred with the inference services available as an easy way to take it to production. Goal is to overcome obstacles and deliver solutions 😉 so here is to get you started on having your (O)#llama #production ready in a private #deployment from your #local to #cloud all in #oneshot . Enjoy Medium: https://lnkd.in/g9jqBQ8d dev.to: https://lnkd.in/gi_gGwam #AI #LLM #Ollama #llama3 #phi3 #mistral #S6overlay #AIOps #CI #CD #oneshot #container #docker #podmanEnjoy

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Sharing my conversation with Prashant Singh (Co-founder and COO of LeadSquared) on how LeadSquared accelerated chatbot deployments with generative AI using Amazon Bedrock and Amazon Aurora PostgreSQL. LeadSquared's innovative approach is revolutionizing chatbot technology, enhancing efficiency, and transforming customer interactions. #LeadSquared #AI #Chatbots #Innovation #Amazon Aurora

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We are excited to announce the launch of Ursa engine, our Kafka API-compatible data streaming engine that is built on top of Lakehouse. Ursa is not something entirely new; it is built on our many years of experience building and operating #ApachePulsar and #ApacheKafka. To learn more about the #Ursa engine, I recommend our #PulsarSummit keynote presentation and announcement blog post. Here, I’d like to delve deeper into some of its features: 1. Making Kafka API a first-class citizen: In #ApachePulsar, we developed a unified messaging model combining queuing and streaming semantics into one protocol, aimed at enhancing data sharing between business applications. Although powerful and increasingly adopted by enterprises and unicorns, the requirement for application rewrites is impractical in today’s economy. Instead of building data gravity around a protocol, we open up access to the robust stream storage layer by extending support to the Kafka protocol, transitioning from a single protocol to a multi-protocol platform, facilitating greater flexibility and integration. 2. Achieve Stream-Table duality via Lakehouse Storage: Data streams commonly end up in data lakehouses, where integrating and maintaining them can be complex and resource-intensive. Ursa simplifies this by storing data streams as lakehouse tables in object storage. This integration not only streamlines operations but also allows seamless access to operational data for analytical processes. Thanks to its multi-modal storage engine which fosters data sharing across teams and departments. 3. Object storage as the primary storage solution: To better align with cloud economics, particularly for large workloads, Ursa makes ZooKeeper and BookKeeper optional and utilizes a cost-optimized write-ahead log based solely on object storage with leaderless and stateless brokers. This architecture leverages object storage for both data replication and storage, minimizing inter-AZ data transfers and reducing costs for latency-relaxed, high-volume workloads. By integrating these features at the multi-tenant level rather than at isolated cluster levels, Ursa eliminates costly data duplication and movement across clusters, allowing teams to map directly to tenants and be able to share data across teams and departments more efficiently. We believe that the future of data streaming platforms lies in being open and multi-faceted, fundamentally different from traditional data platforms. They are designed not just to store and process data, but to enhance organizational capabilities by fostering data sharing across the organization and integration with other systems. More and more data streaming platforms will look more like Ursa, with multi-protocol, multi-tenant, and multi-modal capabilities. Check out our announcement blog post about Ursa: https://lnkd.in/ghEKTYE6 #ApacheKafka #Lakehouse #DataStreaming

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Now we have two first class data catalogs integrated with MS Fabric now available in Azure. Of course DataBricks is the heavy heavy favorite in the enterprise lakehouses as well as AI (as of the conference that just finished 🤓)

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Great read on our journey to securing your AI investments from Steph Hay and Umesh Shankar #1 - AI opens up a world of possibilities. Be proactive in securing your AI investments - data, models, applications, and infrastructure. #2 - Make Google part of your security team, and Security Command Center - Enterprise as your default platform to help secure your AI investments. #3 - New capabilities in Notebook Scanner (private preview) and Model Armor (previewing soon) to secure your AI investments. Shout out to Amit Patil, Bharath Chandrasekhar, Matt Driscoll, Bhaskar Annadata, Cfir Cohen, Rahul Venkatraj and many more for these new capabilities.

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Follow this tutorial to learn how to build a retrieval-augmented generation solution using #OCI #GenerativeAI. https://lnkd.in/ek3kSniK

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