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infrastructure

18 posts with this tag

Your GPUs Are Everywhere. Your Robot-Learning Loop Shouldn't Be.Robotics compute is spreading across clouds and clusters. Learn how one portable pipeline layer can keep the robot-learning loop reproducible.Roboticsmlopsfoundationmodels+1Hamza Tahir2026
Checkpoint Replay, Worker Shape, and Where Durable Execution Is GoingArmin Ronacher's Absurd and Kitaru arrived at the same answers on replay semantics, ephemeral compute, and an agent-legible runtime. Here's why that matters.kitaruagentsopen-sourceHamza Tahir2026
The runtime layer underneath your agent stackWhat people call the agent stack is really four layers: model, harness, runtime, platform. Conflating them costs durability. The runtime layer, and one split inside it, gets the least attention.kitaruagentsThought LeadershipHamza Tahir2026
KAI Scheduler vs Run:ai: Which GPU Scheduling Tool Fits Your AI Infrastructure?We break down GPU scheduling, fractional GPU allocation, gang scheduling, integrations, and pricing to help you pick the right tool for your AI infrastructure.mlopsKubernetescloud+1Hamza Tahir2026
Run:ai vs ClearML: Which AI Infrastructure Platform Fits Your MLOps Stack?In this Run:ai vs ClearML comparison, we break down GPU orchestration, workload scheduling, resource policies, RBAC, integrations, and pricing to help you pick the right platform for your AI infrastructure.mlopsKubernetescloud+1Hamza Tahir2026
Introducing Kitaru: Open Source Infrastructure For Asynchronous Agents (Built by the ZenML Team)Meet Kitaru — open source durable execution for Python agents, built by the ZenML team. Crash recovery, human-in-the-loop, and replay from any checkpoint.kitaruagentsopen-source+2ZenML Team2026
Kitaru is open source and ready to useKitaru is live: open-source infrastructure platform for running Python agents in production.kitaruagentsopen-source+1Hamza Tahir2026
From ZenML to Kitaru: Why We Built a New ProductWe spent five years building ML pipeline infrastructure. Then agents showed up and we realized the next problem needed a new tool — not an extension of the old one.kitaruagentsopen-sourceHamza Tahir2026
Your Agents Need More Than Just TracesTracing shows you what went wrong. But what if you could go back, fix the input, and resume from where it failed — without re-running everything?kitaruagentsopen-sourceHamza Tahir2026
Why Kitaru Doesn't Use Journal Replay?Every durable execution engine today forces your code to be deterministic. Kitaru takes a different approach — and it matters more than you think.kitaruagentsThought LeadershipHamza Tahir2026
Why Your AI Agents Need Durable ExecutionAI agents fail — they timeout, hit rate limits, crash on bad API responses. Without durable execution, every failure means starting over from scratch.kitaruagentsopen-sourceHamza Tahir2026
Your Agents Are Not MicroservicesDurable execution engines were built for payment flows and order processing. AI agents need something different. Here's why.kitaruagentsThought LeadershipHamza Tahir2026
Why Retail MLOps Is Harder Than You ThinkAn in-depth analysis of retail MLOps challenges, covering data complexity, edge computing, seasonality, and multi-cloud deployment, with real-world examples from major retailers like Wayfair and Starbucks, and practical solutions including ZenML's impact in reducing deployment time from 8.5 to 2 weeks at Adeo Leroy Merlin.mlopsHamza Tahir2025
NVIDIA KAI Scheduler: Optimize GPU Usage in ZenML PipelinesDiscover how to optimize GPU utilization in Kubernetes environments by integrating NVIDIA's KAI Scheduler with ZenML pipelines, enabling fractional GPU allocation for improved resource efficiency and cost savings in machine learning workflows.Kuberneteszenml-projectorchestratorsAlex Strick van Linschoten2025
Building Advanced Search, Retrieval, and Recommendation Systems with LLMsDiscover how embeddings power modern search and recommendation systems with LLMs, using case studies from the LLMOps Database. From RAG systems to personalized recommendations, learn key strategies and best practices for building intelligent applications that truly understand user intent and deliver relevant results.best-practicesLLMOpsllmops-database+2Alex Strick van Linschoten2024
Cognitive Load in MLOps: Why Your Data Scientists Need Infrastructure AbstractionDiscover why cognitive load is the hidden barrier to ML success and how infrastructure abstraction can revolutionize your data science team's productivity. This comprehensive guide explores the real costs of infrastructure complexity in MLOps, from security challenges to the pitfalls of home-grown solutions. Learn practical strategies for creating effective abstractions that let data scientists focus on what they do best – building better models – while maintaining robust security and control. Perfect for ML leaders and architects looking to scale their machine learning initiatives efficiently.mlopsData Scientistsales-learning+1Jayesh Sharma2024
Empowering ZenML Pro Infrastructure Management: Our Journey from Spacelift to ArgoCDThe combination of ZenML and Neptune can streamline machine learning workflows and provide unprecedented visibility into experiments. ZenML is an extensible framework for creating production-ready pipelines, while Neptune is a metadata store for MLOps. When combined, these tools offer a robust solution for managing the entire ML lifecycle, from experimentation to production. The combination of these tools can significantly accelerate the development process, especially when working with complex tasks like language model fine-tuning. This integration offers the ability to focus more on innovating and less on managing the intricacies of your ML pipelines.zenmlgitopsKubernetes+1Andrei Vishniakov2024
ZenML + Kubernetes + Kubeflow: Leveraging your MLOps infrastructureKubernetesKubeflowzenmlRishabh Sharma2024

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zenml mlops evergreen agents LLMOps tooling llm bigger-picture open-source kitaru genai llmops-database All 116 tags →

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infrastructure

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