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learn mlops & production ml
take a model from notebook to production: tracking, serving, monitoring, and the systems thinking around it.
curatedintermediate~5 weeks, part-time
mlops & production ml
the gap between a notebook and a system people rely on. learn to track, serve, monitor, and maintain models — the engineering that makes ml real.
4 modules · 12 resources · checkpoint per modulestay current
see the full digest →what's new in mlops & production ml
- UBEP: Re-architecting Expert Parallelism Communication Library for Production Superpodsthis paper introduces ubep, a re-architected communication library for expert parallelism in production superpods. it aims to improve the efficiency of large-scale model training and inference by optimizing communication for distributed systems.
- AdaptiveSD A Stability-Aware, Runtime-Adaptive Speculative Decoding Framework with Multi-Policy Orchestration for CPU-Constrained LLM Inferencethis paper presents adaptivesd, a framework for optimizing large language model (llm) inference on cpu-constrained systems. it helps practitioners improve the efficiency and stability of llm deployments by dynamically adapting decoding strategies.
- AGL-1: The Enterprise AI Governance Layer as a Control Plane for Trusted Enterprise Intelligencethis paper proposes agl-1, an enterprise ai governance layer designed to act as a control plane for trusted enterprise intelligence. it's relevant for practitioners aiming to implement robust governance, compliance, and trustworthiness across their ai initiatives, especially within large organizations.
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