Rik Van Bruggen’s Post

How to architect AI systems is not a very sexy topic, but is crucial to the success of AI projects. Modularity is a key mechanism for decomposing a system into independent components. Its flipside, composability, is also necessary to avoid microservices-hell. Then, there is the challenge of building batch AI systems, real-time AI systems, and LLM systems. Do you need separate platforms or architectures for all of these? Spoiler - you can do it all with a stateful layer to connect your AI pipelines. https://lnkd.in/dzGjrAY6

Supercharge your AI with a System of Experts! Discover how LLMs can tackle complex tasks with greater efficiency, scalability, and accuracy in Stephen Collin´s publication! https://lnkd.in/dhxgZZPc

You can utilize Retrieval-Augmented Generation (RAG) to reduce hallucinations and answer meaningful business questions with your own proprietary data and business context. With RAG, LLMs can gain deep understanding into specific areas without having to be retrained, which makes it efficient and cost-effective to keep LLMs current, relevant, accurate, and helpful in many different situations. With the benefits mentioned above, RAG architecture applications are very common in intelligent search engines and chatbots. Generative AI is an area of active research that produces frequent new findings that lead to increased capabilities in new models, as well as new methods to run and maintain existing models more efficiently. This offers value for generative AI solutions that have a flexible architecture because the same production workload will become cheaper and faster to run, and the same workload can be updated to achieve greater accuracy and capabilities. #AWS #AI #ML #LLMs #GenAI #RAG #Architecture

Vector search has arrived to Hazelcast version 5.5 to help accelerate #AI integrations and #ML workloads https://lnkd.in/enPDP9Cd

Just how good can a truly open source language/code generating AI model be? Turns out, highly promising thanks to platforms like Replicate, which allows anyone to interact with project models like Snowflake's Arctic-Instruct. Constantly impressed with the amount of work people are doing to keep knowledge and tools publicly auditable in a very competitive industry: https://lnkd.in/gFEJ_X3R

📣 I wrote about my take on data-aware, event-driven #orchestration for #AI and #ML product builders. Union.ai artifacts 📦 provide a way to manage AI/ML applications more naturally. Instead of strongly-coupled DAGs, which you can view of as "islands" of compute 🏝️, you can now build constellations of softly-coupled "archipelagos" of workflows 🗾 that work together to create end-to-end AI pipelines.

In the AI era, orchestration needs to treat data and models as first-class entities. Artifacts allows AI product-builders to create data-aware, event-driven workflows that help manage the AI/ML lifecycle of datasets, models, and deployments. Learn more in our latest blog post by Niels Bantilan where he dives into the ways in which artifacts can help automate data freshness, data partitioning, model backtesting, and human-in-the-loop model deployment. https://lnkd.in/gZ3G-Fvm

🔓 Unlock next-gen #AI and insights with Sumo Logic's new disruptive pricing model! Discover how we are driving a single source of truth for #DevSecOps. Read our CEO Joe Kim's blog for more. #loganalytics

🔓 Unlock next-gen #AI and insights with Sumo Logic's new disruptive pricing model! Discover how we are driving a single source of truth for #DevSecOps. Read our CEO Joe Kim's blog for more. #loganalytics

🔓 Unlock next-gen #AI and insights with Sumo Logic's new disruptive pricing model! Discover how we are driving a single source of truth for #DevSecOps. Read our CEO Joe Kim's blog for more. #loganalytics