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12 articles tagged #Machine Learning.
P.01Amazon closes Mechanical Turk and Ground Truth's human workforce on September 30, 2026, after 21 years. The dates that matter, and where teams are moving.
P.02Unit tests don't work on a feature that answers differently every time. Evals do. How to build a practical eval harness for an LLM feature, with real code.
P.03MLOps sits between the data scientist's model work and the DevOps engineer's infrastructure. What the role owns, what to screen for, and where people come from.
P.04Google's open-source Colab CLI provisions T4, L4, A100, and H100 GPUs and TPUs from the command line. Install it, run a job, wire it into an AI agent.
P.05LoRA and QLoRA fine-tune a multi-billion-parameter model on one consumer GPU by training small adapter weights. How each works, and when to pick which.
P.06Data scientist and ML engineer are two different jobs. The distinction, what a strong data scientist does, and how to screen for real analytical judgment.
P.07Everyone claims ML experience since the AI boom. The screen that separates people who ship ML systems from people who fine-tuned once in a Colab notebook.
P.08Clients ask agencies to automate PDFs constantly. Here's how to actually build document extraction pipelines: OCR, vision models, and validation.
P.09RAG and fine-tuning solve different problems: one changes what a model knows, the other changes how it behaves. Here's how to decide which one you need.
P.10Past the chatbot hype, AI is improving specific e-commerce outcomes. Here are the implementations producing measurable results, and the ones that still mostly disappoint.
P.11Small language models like Phi-4, Qwen2.5, and Gemma 3 now beat cloud LLMs on latency, cost, and privacy for most production edge deployments in 2026.
P.12EditorPickThe network latency between your Django app and your FastAPI ML service is probably longer than inference itself. Here is how to serve models from Django directly.