Tag

#mlops

20 posts

AI & Machine LearningMarch 22, 202610 min
Mastering ML Model Training: From Costs to Code
ML model training from costs to code: frontier costs (Gemini Ultra $191M), compute doubling every 6 months, plus runnable training patterns for smaller teams.
AI & Machine LearningMarch 8, 202610 min
Building Lightning-Fast AI Backends with FastAPI (2026 Edition)
FastAPI 0.136 for AI backends in 2026: Starlette 1.0, async streaming, token limits, and the patterns that keep LLM services fast under real-world load.
AI & Machine LearningMarch 6, 202611 min
OpenCoder Review: Free Open-Source Code Model
OpenCoder review: the free-to-download code model in 1.5B and 8B variants. 83.5% HumanEval, 79.1% MBPP — deployable on your own hardware, though the model weights carry a custom license, not Apache 2.0.
AI & Machine LearningFebruary 23, 202611 min
Mastering AI Error Tracking: From Debugging to Production Reliability
AI error tracking in production: data errors, model errors, and code bugs. Observability, evals, incident response, and the tooling that keeps LLMs reliable.
AI & Machine LearningFebruary 22, 20269 min
Mastering Technical AI Assessments: A Complete 2026 Guide
A deep, practical guide to preparing, designing, and evaluating technical AI assessments — from coding tasks to production-grade model evaluations.
AI & Machine LearningFebruary 14, 202610 min
Mastering Hyperparameter Tuning: From Basics to Production
Hyperparameter tuning from basics to production: grid, random, Bayesian optimization, Optuna, Ray Tune — and the patterns that save real GPU hours in practice.
LLM & IntegrationFebruary 14, 202611 min
Running LLMs Locally: The Complete Practitioner's Guide
Run LLMs locally with Ollama, LM Studio, llama.cpp, Hugging Face Transformers, and vLLM. Model selection, quantization, GPU sizing, and the privacy wins you lock in on day one.
AI & Machine LearningFebruary 11, 20268 min
Mastering XGBoost Optimization: From Theory to Production
A deep-dive guide into optimizing XGBoost for performance, scalability, and accuracy—complete with real-world examples, code, and troubleshooting tips.
Career & GrowthFebruary 6, 20269 min
AI Career Roadmap 2026: Skills, Tools & Real-World Pathways
A complete 2026 roadmap for building a successful AI career — from foundational skills to real-world applications, tools, and growth strategies.
AI & Machine LearningFebruary 5, 20269 min
AI Serverless Deployment: The Complete 2025 Guide
Learn how to deploy AI models efficiently using serverless architectures — from scaling and cost optimization to security, testing, and real-world examples.
AI & Machine LearningJanuary 30, 202610 min
Building Full‑Stack AI Apps: From Idea to Production
Build full-stack AI apps from idea to production: Next.js + Python, vector DB, auth, streaming, observability, and the deploy path to Vercel or AWS.
AI & Machine LearningJanuary 28, 202610 min
Model Serving Patterns: From Batch to Real-Time Inference
Model serving patterns: batch, online, streaming, edge. Latency, cost, and throughput trade-offs for each — plus the tools (BentoML, vLLM, TGI) to ship with.

Every post in #mlops (20)