Comparison-grade reviews of MLOps platforms. Feature stores, model registries, training infra, online inference, eval pipelines — what each platform actually delivers under production load, where the marketing diverges from the runbook.https://mlopsplatforms.com/enhttps://mlopsplatforms.com/posts/mlops-platform-selection-framework/https://mlopsplatforms.com/posts/mlops-platform-selection-framework/Vendor demos are optimized to look good. The gaps show up six months after sign-off. A rigorous evaluation framework covers the failure modes vendors don't volunteer.Wed, 06 May 2026 00:00:00 GMTmlopsplatform-selectionevaluationvendor-reviewproduction-mlMLOps Platforms Editorialhttps://mlopsplatforms.com/posts/data-versioning-production-ml/https://mlopsplatforms.com/posts/data-versioning-production-ml/Training data versioning sounds like an ML engineering nicety. In practice it's the prerequisite for reproducible models, auditable compliance, and debugging production failures.Wed, 06 May 2026 00:00:00 GMTdata-versioningdvcdelta-lakereproducibilitymlopsdata-engineeringMLOps Platforms Editorialhttps://mlopsplatforms.com/posts/eval-pipeline-design-production/https://mlopsplatforms.com/posts/eval-pipeline-design-production/CI evals catch regressions in code. They don't catch production drift, prompt sensitivity, or behavioral changes in upstream models. Building an eval system that covers both requires a different architecture.Tue, 05 May 2026 00:00:00 GMTevaluationevalsllm-testingmlopsci-cdproduction-mlMLOps Platforms Editorialhttps://mlopsplatforms.com/posts/training-infrastructure-cost-control/https://mlopsplatforms.com/posts/training-infrastructure-cost-control/Cloud training bills surprise teams that model costs at the benchmark level. Real training cost includes wasted compute, storage, egress, and idle GPUs. Here's how to audit and reduce it.Tue, 05 May 2026 00:00:00 GMTcost-optimizationtrainingcloud-mlinfrastructuremlopsgpuMLOps Platforms Editorialhttps://mlopsplatforms.com/posts/model-registry-patterns-production/https://mlopsplatforms.com/posts/model-registry-patterns-production/A model registry is supposed to be the source of truth for what's deployed. Most implementations drift from that ideal within six months. Here's what breaks and how to prevent it.Mon, 04 May 2026 00:00:00 GMTmodel-registrymlflowmlopsdeploymentversioninggovernanceMLOps Platforms Editorialhttps://mlopsplatforms.com/posts/online-inference-latency-budget/https://mlopsplatforms.com/posts/online-inference-latency-budget/P99 latency is a product problem as much as an engineering one. Breaking down the inference budget — model compute, preprocessing, retrieval, postprocessing — is the prerequisite for fixing it.Mon, 04 May 2026 00:00:00 GMTinferencelatencymlopsproduction-mlperformanceservingMLOps Platforms Editorialhttps://mlopsplatforms.com/posts/feature-store-comparison-2026/https://mlopsplatforms.com/posts/feature-store-comparison-2026/Feature stores are table stakes for production ML. Which one you choose depends on whether your bottleneck is freshness, scale, or team bandwidth — and not all options are honest about the tradeoffs.Sun, 03 May 2026 00:00:00 GMTfeature-storesfeasttectonhopsworksmlopsproduction-mlMLOps Platforms Editorialhttps://mlopsplatforms.com/posts/welcome/https://mlopsplatforms.com/posts/welcome/MLOps Platforms covers ML observability and MLOps from a production-engineering perspective. Here's what we publish.Sun, 03 May 2026 00:00:00 GMTmetaMLOps Platforms Editorial