ML Research Built for Production Engineers
Technical guides, evaluation tooling, and case studies on vector databases, model lifecycle, and running LLMs at scale. Working code included.
What We Cover
Practical knowledge for engineers who maintain ML systems in real environments, not controlled demo conditions.
Evaluation Harnesses
Design and run evaluation pipelines that produce reproducible metrics. We publish harness configurations, prompt templates, and scoring code you can adapt directly to your models.
Vector Database Engineering
In-depth coverage of Pinecone, Weaviate, Qdrant, and pgvector. Indexing strategies, ANN algorithm tradeoffs, and benchmark data measured against real production workloads.
Model Lifecycle Management
From registry design to deprecation workflows. How to track model versions, manage staged rollouts, and detect quality drift before it surfaces in production metrics.
LLM Production Operations
Latency budgets, token cost accounting, caching strategies, and documented failure modes. The operational realities of running LLMs at scale that most guides skip entirely.
Transparent Benchmarks
Every benchmark result links back to the code that produced it and the hardware it ran on. Methodology is published alongside numbers so you can reproduce or challenge our conclusions.
Open-Source Tooling
Libraries and scripts released under the Apache 2.0 license. Built to solve specific production problems, documented at the level where reading the source is enough to trust it.
Reproducibility as Engineering Practice
We treat evaluation rigor the same way we treat code quality: measurable, trackable, and improvable over time.
Fully reproducible evaluation pipelines with pinned dependencies
Benchmark results traceable to specific commits and hardware configs
Working code published alongside every technical guide
GDPR-relevant data handling covered for EU-deployed ML systems
Vector index benchmarks across four major database backends
Model drift detection patterns drawn from production deployments
Access Plans
All plans include full access to published guides. Paid tiers add early access, private tooling builds, and direct support from our research team.
Free
Researcher
Full access to all published guides and open-source tooling.
All published technical guides
Open-source tooling repository
Community discussion access
Monthly benchmark digest
€49/mo
Engineer
Early access, private tooling builds, and priority Q&A with the research team.
Everything in Researcher
Early access to guides before publication
Private evaluation tooling builds
Priority Q&A with research team
Quarterly deployment pattern reviews
Custom
Enterprise
Team licensing, custom research engagements, and SLA-backed support.
Everything in Engineer
Team seat licensing
Custom evaluation harness design
Dedicated research consultation
SLA-backed support response times
Private benchmark runs on your data
What Engineers Say
Feedback from ML engineers and AI architects who use TestML resources in their day-to-day work.
“The vector database benchmarks saved us weeks of internal testing. Numbers are traceable, methodology is documented, and the conclusions held up when we reproduced them ourselves on our own dataset.”
Priya Nair
Senior ML Engineer, Berlin
“Most ML content stops at the tutorial stage. TestML publishes the unglamorous parts: cost accounting, failure modes, rollback strategies. Exactly what I need when things go wrong at 2am.”
James Okafor
AI Infrastructure Lead, Amsterdam
“Reproducibility is something the field talks about constantly and implements rarely. TestML ships working harness code with every evaluation guide. I have forked three of their repos directly into production.”
Sofía Méndez
Applied ML Researcher, Madrid
Tools and Platforms We Cover
Start With Guides That Ship Working Code
Browse TestML's technical library. Every guide includes a reproducible implementation and documented benchmarks you can verify yourself.
Explore the Library