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It treats internal developers as customers, providing them with self-service infrastructure and standardized workflows so they can focus on writing product features rather than configuring Kubernetes clusters.","oneSentence":"Platform Engineering accelerates feature delivery by abstracting infrastructure complexity into self-service internal developer platforms.","tweetLength":"You cannot scale engineering by just hiring more people; the Coordination Tax will destroy your velocity. Platform Engineering centralizes complexity so your developers can actually write code.","keyTakeaways":["Reduces developer cognitive load.","Mitigates the Coordination Tax.","Treats internal developers as customers.","Enforces architectural standards by default."],"faqs":[{"question":"What is the difference between DevOps and Platform Engineering?","answer":"DevOps is a culture of shared responsibility; Platform Engineering builds the internal tools that make that responsibility manageable."}],"whenToUse":["When product teams spend more than 20% of their time on infrastructure and configuration"],"examples":{"enterprise":"Building a portal where developers can spin up compliant environments with one click.","startup":"Standardizing CI/CD pipelines across all repositories.","antiPattern":"Forcing every product team to write their own Terraform scripts.","commonMistake":"Building a platform without talking to the developers who will use it."}},"canonicalReadingOrder":[{"step":1,"title":"Combating the Coordination Tax","publisher":"RichardEwing.io","type":"Framework"}],"provenanceTimeline":[{"stage":"Observation","label":"Shift from DevOps to Platform","publisher":"Industry Meta","date":"2022","summary":"Observation of cognitive overload in traditional DevOps models."}],"evidenceLedger":[{"id":"ev-pe-1","title":"The Coordination Tax","url":"https://richardewing.io/blog/hiring-engineers-gross-margin","publisher":"RichardEwing.io","type":"Analysis","strength":5,"role":"Origin","date":"2025"}],"relatedConcepts":[{"slug":"coordination-tax","relationship":"mitigates"}]},{"@type":"DefinedTerm","@id":"https://www.richardewing.io/concepts/mlops","name":"MLOps & ML Engineering Operations","description":"The set of practices combining machine learning, DevOps, and data engineering to reliably build, deploy, and maintain machine learning models in production environments.","domain":"Engineering Leadership","health":{"confidence":0.9,"evidenceCount":3,"lastVerified":"August 2026","status":"Active","openQuestionsCount":1,"knownLimitationsCount":1},"aeo":{"shortDefinition":"MLOps is the application of DevOps principles to machine learning, focusing on deploying, monitoring, and maintaining models in production.","executiveSummary":"MLOps bridges the gap between data science experimentation and software engineering reliability. 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MLOps provides the infrastructure and discipline required to deploy, monitor, and scale AI without drowning in technical debt.","keyTakeaways":["Automates model training and deployment.","Essential for tracking model drift and degradation.","Enables scalable SLM Repatriation.","Treats ML models as standard software assets."],"faqs":[{"question":"Why is MLOps necessary?","answer":"Because deploying a model is only 10% of the work; maintaining it as data and user behavior changes is the real challenge."}],"whenToUse":["When moving machine learning models from experimentation into user-facing production systems"],"examples":{"enterprise":"Building automated pipelines to retrain models when performance drops.","startup":"Using tools like MLflow to track model experiments.","antiPattern":"Manually uploading model weights to a production server.","commonMistake":"Ignoring data versioning while focusing only on code versioning."}},"canonicalReadingOrder":[{"step":1,"title":"Operationalizing AI","publisher":"Built In","type":"Framework"}],"provenanceTimeline":[{"stage":"Observation","label":"The Deployment Bottleneck","publisher":"Industry Meta","date":"2020","summary":"Observation of models failing to reach production."}],"evidenceLedger":[{"id":"ev-mlops-1","title":"SLM Repatriation Economics","url":"https://theaieconomist.beehiiv.com","publisher":"Beehiiv","type":"Analysis","strength":5,"role":"Origin","date":"2025"}],"relatedConcepts":[{"slug":"slm-repatriation","relationship":"supports"}]}]}