{"slug": "from-ml-tooling-to-analytical-governance-recent-updates-to-kmds", "title": "From ML Tooling to Analytical Governance: Recent Updates to KMDS", "summary": "A developer has been refining KMDS, a framework for building repeatable and auditable machine learning systems, with recent updates focusing on analytical governance. New features include a Feature Advisor service that recommends feature engineering strategies and a Design Governance framework that captures decision points and generates implementation guidance. The long-term goal is to preserve analytical knowledge as reusable assets through knowledge graphs, ensuring institutional knowledge is retained even when team members leave.", "body_md": "Over the last few months I've been refining KMDS, a framework for building repeatable and auditable machine learning systems.\n\nThe original motivation behind KMDS was simple:\n\nMany machine learning projects fail long before model selection becomes important.\n\nTeams struggle with questions such as:\n\nMost organizations answer these questions at some point. The problem is that the answers often disappear into notebooks, documents, tickets, or the memories of individual contributors.\n\nKMDS is an attempt to make these decisions explicit, structured, and reusable.\n\nRecent updates have focused on moving beyond workflow automation and toward analytical governance.\n\nThe workflow begins with semantic tagging and metadata generation.\n\nRather than immediately building features or training models, the system first attempts to understand:\n\nThe goal is to establish a semantic foundation before modeling begins.\n\nOne of the new additions is a Feature Advisor service.\n\nGiven metadata and project context, the advisor recommends feature engineering strategies for non-numeric attributes.\n\nExamples include:\n\nThe objective is not automatic feature engineering.\n\nThe objective is to provide design guidance and rationale that helps practitioners make better decisions.\n\nA second addition is a Design Governance framework.\n\nMachine learning projects contain many decision points:\n\nThe Design Governance layer acts as a design-time advisor that captures these considerations and generates implementation guidance.\n\nThe output is a structured design blueprint that can be reviewed by humans or supplied to AI coding assistants.\n\nPerhaps the most important change is an increased emphasis on preserving analytical knowledge.\n\nThe long-term goal is not simply to create models.\n\nIt is to create reusable analytical assets.\n\nUsing KMDS tooling, project artifacts can be transformed into a knowledge graph representing:\n\nThis creates a queryable representation of the analytical lifecycle.\n\nMost organizations already have documentation.\n\nWhat they often lack is accessible institutional knowledge.\n\nCritical analytical decisions are frequently distributed across:\n\nWhen people leave, much of that context leaves with them.\n\nMy view is that the real asset is not the agent.\n\nThe real asset is the structured analytical knowledge that the agent can access.\n\nIf the knowledge is preserved independently of any specific model, tool, or LLM, organizations retain ownership of their analytical reasoning and can recreate capabilities as technology evolves.\n\nThe broader goal of KMDS is to make machine learning systems:\n\nRecent work has focused on feature governance, design governance, metadata-driven workflows, and knowledge graph generation.\n\nFuture work will continue exploring how analytical context can be captured and preserved as a first-class artifact rather than an afterthought.\n\nI would be interested in hearing how others are approaching analytical governance, reproducibility, and knowledge preservation in their own machine learning workflows.", "url": "https://wpnews.pro/news/from-ml-tooling-to-analytical-governance-recent-updates-to-kmds", "canonical_source": "https://dev.to/rajivsam/-from-ml-tooling-to-analytical-governance-recent-updates-to-kmds-548n", "published_at": "2026-06-17 04:32:36+00:00", "updated_at": "2026-06-17 04:51:17.882730+00:00", "lang": "en", "topics": ["machine-learning", "mlops", "developer-tools"], "entities": ["KMDS"], "alternates": {"html": "https://wpnews.pro/news/from-ml-tooling-to-analytical-governance-recent-updates-to-kmds", "markdown": "https://wpnews.pro/news/from-ml-tooling-to-analytical-governance-recent-updates-to-kmds.md", "text": "https://wpnews.pro/news/from-ml-tooling-to-analytical-governance-recent-updates-to-kmds.txt", "jsonld": "https://wpnews.pro/news/from-ml-tooling-to-analytical-governance-recent-updates-to-kmds.jsonld"}}