https://ryanordonez.github.io/Portfolio-Site/blog.html Thoughts on AI, data science, and the technology shaping our future. Written by Ryan Ordonez, Data Scientist with an M.S. from CU Boulder. en-us Sun, 05 Apr 2026 08:00:00 GMT ryanordonez7@gmail.com (Ryan Ordonez) ryanordonez7@gmail.com (Ryan Ordonez) 2026 Ryan Ordonez Data Science Artificial Intelligence Machine Learning https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-05-google-gemma-4-open-models.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-05-google-gemma-4-open-models.html Sun, 05 Apr 2026 08:00:00 GMT Machine Learning Google DeepMind's Gemma 4 ships four model sizes under Apache 2.0 with native function calling, 256K context, and MoE efficiency that puts 3.8B active parameters within striking distance of 31B dense quality. Local agentic AI just became production-viable. https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-04-gemini-embedding-2-multimodal.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-04-gemini-embedding-2-multimodal.html Sat, 04 Apr 2026 08:00:00 GMT AI Engineering Google's Gemini Embedding 2 is the first production embedding model that natively maps text, images, video, audio, and documents into a single vector space. Early adopters report 70% latency reduction and 20% recall improvement — here's what it means for your RAG pipeline architecture. https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-03-prompt-engineering-guide.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-03-prompt-engineering-guide.html Fri, 03 Apr 2026 08:00:00 GMT AI Engineering Prompt engineering has evolved from ad-hoc craft into disciplined practice. From structured outputs and DSPy optimization to agentic system prompts and retrieval-grounded patterns — here's what actually works in production, and the common mistakes data scientists make. https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-02-mlflow-production-pipelines.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-02-mlflow-production-pipelines.html Thu, 02 Apr 2026 08:00:00 GMT MLOps MLflow 3.0 introduces LoggedModel as a first-class entity, comprehensive tracing for GenAI, LLM-as-judge evaluation, and deployment jobs that validate models before production. Here's what matters after migrating two production pipelines — and what's still missing. https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-01-vector-databases-compared.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-04-01-vector-databases-compared.html Wed, 01 Apr 2026 08:00:00 GMT AI Engineering The vector database market has matured from a handful of startups into a crowded, competitive landscape. Here's a practical comparison of Pinecone, Weaviate, Chroma, Qdrant, Milvus, and pgvector — with guidance on which to choose for your use case. https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-31-fine-tuning-open-source-llms.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-31-fine-tuning-open-source-llms.html Tue, 31 Mar 2026 08:00:00 GMT Machine Learning From LoRA to QLoRA, the barrier to fine-tuning has shifted from compute to judgment. Here's the practical playbook for choosing your base model, preparing data, setting hyperparameters, and deploying fine-tuned 8B models on consumer hardware. https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-30-rag-architectures.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-30-rag-architectures.html Mon, 30 Mar 2026 08:00:00 GMT AI Engineering From naive retrieve-and-generate to agentic self-reflective pipelines and GraphRAG, the RAG landscape has evolved dramatically. Here's a practical framework for choosing between RAG, fine-tuning, and long context windows — and why most production systems in 2026 use all three. https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-29-gpu-costs-2026.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-29-gpu-costs-2026.html Sun, 29 Mar 2026 08:00:00 GMT MLOps B200s range from $2.25 to $16/hour depending on where you rent. H100s have dropped 60% in two years. Here's how to think about cloud vs. on-prem vs. marketplace for your ML workloads — with real numbers and practical recommendations. https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-28-claude-mythos-leak.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-28-claude-mythos-leak.html Sat, 28 Mar 2026 08:00:00 GMT AI Industry Anthropic's leaked internal documents reveal Claude Mythos, a model that represents a "step change" in reasoning and cybersecurity capabilities. Markets reacted, but the real story is what this tells us about the accelerating frontier model race. https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-27-ai-accountability-act.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-27-ai-accountability-act.html Fri, 27 Mar 2026 08:00:00 GMT AI Regulation The US just passed its most significant AI legislation ever, requiring mandatory bias audits for AI in hiring, lending, healthcare, and criminal justice. Here's how it changes the way we build and deploy models. https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-26-agentic-ai.html https://ryanordonez.github.io/Portfolio-Site/posts/2026-03-26-agentic-ai.html Thu, 26 Mar 2026 08:00:00 GMT AI Trends From autonomous code generation to supply chain orchestration, agentic AI systems are moving from research demos to production deployments. Here's what that means for data scientists and why Morgan Stanley says most of the world isn't ready.