Uploaded April 2026 | Updated September 2026, 2 weeks ago
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Automated pipelines have become an integral part of our daily workflow. As the pipelines become increasingly important, the demands placed on them rise proportionally.
As with many things, a great pipeline operates seamlessly in the background, while a poorly designed one becomes a constant irritation.
Are you publishing your artefacts every time the pipeline runs, running all steps in a sequence, or installing all the tools every time a new build starts?
In this talk, I will address these antipatterns and more I have encountered during my work as a consultant, explaining why I consider them such and what you should do instead.
After listening to this talk, you will better understand what makes a pipeline great and concrete things you can do to improve it and shorten the feedback loop.
Please subscribe to our YouTube channel @ youtube.com/@DevoxxForever
Subscribe to LinkedIn @ linkedin.com/company/voxxed-days-amsterdam
Follow us on Twitter @ twitter.com/voxxedamsterdam
Automated pipelines have become an integral part of our daily workflow. As the pipelines become increasingly important, the demands placed on them rise proportionally.
As with many things, a great pipeline operates seamlessly in the background, while a poorly designed one becomes a constant irritation.
Are you publishing your artefacts every time the pipeline runs, running all steps in a sequence, or installing all the tools every time a new build starts?
In this talk, I will address these antipatterns and more I have encountered during my work as a consultant, explaining why I consider them such and what you should do instead.
After listening to this talk, you will better understand what makes a pipeline great and concrete things you can do to improve it and shorten the feedback loop.
![[VDBUH2026] Abdel Sghiouar - Optimizing LLM Inference for the Rest of Us](https://i.ytimg.com/vi/G58PbxBXC8c/mqdefault.jpg "[VDBUH2026] Abdel Sghiouar - Optimizing LLM Inference for the Rest of Us
Not every organization operates with the hyperscale resources of Anthropic, Google, or OpenAI. For the majority of businesses integrating Large Language Models (LLMs) into their critical paths, the high costs and scarcity of GPU/TPU accelerators present a significant challenge. Striking the balance between performance, availability, scalability, and cost-efficiency is a must.
While Kubernetes is a ubiquitous runtime for modern workloads, deploying LLM inference effectively demands a specialized approach. This session dives deep into practical strategies for optimizing your Kubernetes clusters and LLM Inference workloads to run efficiently and cost effectively. We will explore:
– Container and Model Optimization
– Accelerator Management
– Data & Storage
– Network & Load Balancing
– Observability
Attendees will leave with practical techniques for maximizing cost/performance for LLM inference for their AI-powered applications on Kubernetes.")
