Building Evaluation Pipelines for LLM Applications
Learn how to build robust evaluation pipelines that measure LLM performance, catch regressions, and drive continuous improvement.
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Thoughts on quantum-classical computing, hybrid orchestration, and the future of compute infrastructure.
Shipping prompts without systematic testing leads to unpredictable behavior, wasted compute, and eroded user trust.
How fostering a culture of experimentation, iteration, and shared learnings can accelerate your AI team's impact.
Complete specification for Marqov Capsules - reproducible, portable compute workloads
A step-by-step guide to setting up Marqov and running your first hybrid quantum-classical workload. From installation to job submission in minutes.
Marqov is the missing orchestration layer for hybrid quantum-classical workloads. Manage complex computational jobs across GPU, QPU, and classical infrastructure from a single interface.
A comprehensive guide to the Marqov CLI and REST API for orchestrating quantum-classical workloads. Learn how to initialize, validate, publish, and execute capsules across hybrid compute infrastructure.
A technical deep-dive into Marqov's architecture: how Agent, Capsule, Platform, and Mesh work together to orchestrate quantum-classical compute workloads.
Discover why ExperimentOps is crucial for operationalizing AI knowledge and turning experiments into institutional memory.
Best practices for structuring, tracking, and learning from AI experiments to accelerate your development cycle.