Honest reviews and comparisons of MLOps platforms.
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.
MLOps Platform Selection: A Framework That Survives Reality
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.
Past briefings
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Data Versioning for Production ML: DVC, Delta Lake, What Works
Training data versioning sounds like an ML engineering nicety. In practice it's the prerequisite for reproducible models, auditable compliance, and debugging production failures.
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Evaluation Pipeline Design: What CI Evals Miss and How to Fix
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.
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Training Infrastructure Cost Control: Where ML Spend Goes
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.
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Model Registry Patterns That Hold in 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.
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Online Inference Latency: Where the Budget Actually Goes
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.
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Feature Store Comparison 2026: Feast, Tecton, and Hopsworks
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.
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What this site is for
MLOps Platforms covers ML observability and MLOps from a production-engineering perspective. Here's what we publish.
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