AI Researcher & Strategist | PhD in Computer Science | LLMs & Generative AI | Bridging research & real-world solutions in business, education & government
✅ Data & model versioning
✅ Metadata tracking
✅ Automated pipelines
✅ Consistent training-serving
Reproducibility isn’t red tape, it’s strategy. That’s how you go from experiment to impact.
If you can’t reproduce it, you can’t scale it.
Reproducibility isn’t bureaucracy, it’s the backbone of every successful ML system.
Without it, you’re just guessing with data.
Automate pipelines with CI/CD.
Manual processes break reproducibility faster than bad data.
If your model can’t be rebuilt with one command, you’re not ready for scale.
In the design and deployment of real-time systems, there is always a tradeoff: latency, privacy, and scalability. You can’t maximize one dimension without sacrificing the others; the key is to strike a balance that fits the system’s specific requirements (edge, cloud, or hybrid).
The key difference:
GPT-4 → One-off shot.
GPT-5 → A living decision-making system that grows with you and your business
The new advantage is not using AI for isolated tasks…
It’s training your AI to evolve with your strategic challenges.
Updated our preprint to add a map of retracted papers (11k) in PubMed (21m). There are clear clusters and we believe it's paper mill activity.
E.g. there is a small island with 11% (!) retractions; we argue that the other 89% are all VERY suspicious and deserve further scrutiny.
I have 5 scholarships to the course "Applied Machine Learning".
Andrew Maas, Senior Manager at Apple and Instructor at Stanford University, teaches the course that starts in September 5th and lasts 4 weeks.
Read on for details on how to apply:
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