Topic
Machine Learning
My writing, talks, podcasts, and projects about Machine Learning.
Selected work
Anthony Alcaraz — GTM Agentic Engineering Lead at AWS
Your AI agents are lost — why context engineering and knowledge graphs give agents the structure they need for retrieval, memory, planning, reasoning, and learning from business outcomes.
Maria Vechtomova — Co-founder at Cauchy
LLM observability — how monitoring principles from MLOps apply to large language model applications, the gaps that went overlooked for nine years, and best practices for observing AI systems.
Tomás Hernando Koffman — Co-founder at Not Diamond
99%+ accuracy on a moving target — model deprecation, reliability with LLMs, and treating prompts as architectural components.
Frontiers of AI: Building with Rootly AI, Zscaler, CircleCI, Fireworks AI & Google DeepMind
Panel at Google HQ with 300+ attendees exploring real-world Gemini models, reinforcement learning, next-gen agent systems, and AI reliability.
With AI Now a Commodity, the Speed of Iteration Is the Next Challenge
AI models are becoming commoditized. The competitive advantage now lies in how fast teams can iterate on data, features, and deployment pipelines.
For companies that use ML, labeled data is the key differentiator
Why Tesla leads on ADAS — and what it teaches every company about the strategic value of training data, annotation pipelines, and the $6B labeling market.
What Is MLOps?
A primer on MLOps — the practices, tools, and culture for managing machine learning models in production at scale.
The Future of Code Quality, Security, and Agility Lies in Machine Learning
How machine learning is transforming code quality assurance, security scanning, and agile development workflows.
The New Generation of Artificial Intelligence Jobs
How AI is creating entirely new job categories and what skills workers need to fill them.
Automated Management of a Distributed Computing System
US Patent (US9674031B2) — A machine learning-powered self-healing infrastructure that monitors distributed systems, detects anomalies, and automatically applies remedies based on historical incident data. Co-designed at LinkedIn.
Why education should become more like artificial intelligence
The case for applying AI-driven personalization to education — adaptive learning paths, competency-based assessment, and project-driven curricula.