A practitioner's newsletter on MLOps, LLMOps and AI platform engineering, covering topics like model update strategy, Python dependency management and RAG versus CAG architectures. Readers get concrete production-oriented guidance on keeping ML systems reliable and maintainable.
Website: https://mlops.tv
This newsletter publishes in-depth technical explainers on machine learning systems, covering LLM architecture, inference costs, and agent infrastructure. Readers get conceptual breakdowns with concrete examples drawn from recent model releases and platform APIs.
Weekly long-form posts on machine learning, generative AI and ML system design, including a fundamentals series that pairs theory posts with hands-on coding labs. Readers get explanations of concepts like post-training, SFT and DPO, plus practical walkthroughs and hands-on product reviews.
Case studies of how companies like Grab and Intercom built production AI and LLM platforms, alongside commentary on where DevOps, SRE and platform engineering are heading with agentic AI. Readers get architecture walkthroughs and career perspective aimed at DevOps professionals moving into AI and MLOps.
A personal technical blog with hands-on walkthroughs for Red Hat OpenShift and OpenShift AI, including installation, feature stores and model serving. Posts include commands, companion GitHub repos and version-specific notes.
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