In this article I compare how the combination of AIOps agents performs; it looks like we have a clear direction out of this run:
AIOps Evaluation Harness – Model X Telemetry X Tooling X Scope
Federated Learning & Model delta transfer
In this article I explore Edge architectures utilizing Federated Learning and model delta transfer to acknowledge typical edge connectivity and badnwidth issues.
Self-adjusting AIOps Solutions
Here’s a quick update on the LFEdge AIOps for Distributed Environments project:
https://medium.com/@andreas.spanner/towards-self-adjusting-aiops-systems-an-update-33e523d9686a
Headless Retail
Based on the discussions around the NRF – The Retail Big Show in Singapore this latest blog describes the future of Retail being Headless Retail and links also my two earlier blog posts to give the reader a cohesive and comprehensive view across technology, humans and the resulting user (UX) or better AI Agent Experience (AAX).
Based on the conversations during NRF and dedicated Retail dinners with startups and partners this is not a comprehensive list – there are more things coming, but Headless Retail is right in the center of it.
Omni-Channel is so Yesterday – Check out Agentic Retail
In this demo and associated blog post, I show how agentic Retail will change our shopping experience.
It covers things like:
- Autonomous negotiations and purchasing
- Comparing different shop and buyers agents’ performance
- Have you agent rendering product choices the way you like it best
- An evaluation that tests all permutations of shop and buyers agent, allowing to compare performances across:
- Store negotiation strategies eg relationship, competitive or premium
- Buyers personalities
- Buyers profiles
- Floor price
- Stock levels
- Margin pressures
- and more technical things like context engineering, RAG, time-to-first-token (TTFT), etc.
All experiments are tracked via MLFlow.
Please reach out and let me know what you think! Your feedback is a much appreciated gift.
Benchmarking LLM Agents for AIOps – The Chaos Engineering Way
In this article I am describing an Chaos Engineering way to test AIOps agents via an evaluation harnesses:
Consolidated Telemetry for AIOps
In this blog post I cover the current open source available storage backends for data ingestion and retrieval to support organisations toward AIOps for inference and training across anomaly detection, signal correlation, root-cause analysis and remediation as part of a codified bechmark harness under LFEdge/InfiniEdgeAI/AIOps: https://github.com/lfedgeai/AIOps
I am examing data ingestion performance
and data query performance:
Federated Learning (AI) for the Enterprise
In this article at redhat.com Yan Meng and I go through how kubernetes CRDs of the open source project Open Cluster Management (OCM) help to create, deploy and manage distributed and federated learning (AI) environments:
Let me know which FL use cases you are currently working on or would like to explore!
Thanks!
Andreas
OpenShift with GPU support setup with YAKKO
In this article I cover some pitfalls you need to be aware of if you want to ‘free’ your NVIDIA (or any other really) GPU from your HDMI port, because you want to use it for AI model work:
https://medium.com/@andreas.spanner/openshift-with-gpu-support-on-your-laptop-6552c3932b34
Geospatial use cases with Prithvi AI Models
In this post I showcase in collaboration with Michael Cawte (Principal at Section6) how to utilize geospatial AI capabilities based on the Granite Prithvi Model family.
Use cases based on IBM use cases are:
- Flood Prediction
- Earth Surface temperature prediction
- Canopy height prediction
https://medium.com/@andreas.spanner/geospatial-granite-models-on-openshift-4119e7c1b04d
All this is deployed on Red Hat OpenShift and was developed as a demo for a Fire & Emergency department.