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A detailed roadmap of topics to learn for becoming an AI/ML engineer, covering math fundamentals, deep learning architectures, training techniques, data pipelines, evaluation, inference, MLOps, and responsible AI.
This article explores the true meaning of Forward Deployed Engineering (FDE) in AI deployment, emphasizing that FDE is not simply about API calls or building agents, but rather a systematic engineering approach geared toward production deployment, including business translation, system design, platform integration, production operations, and capability accumulation.
A tweet announces LLM-Evaluation, a public GitHub repository containing workshop slides, sample notebooks, prompts, and reference links for evaluating LLMs, generative AI, and RAG systems, aiming to provide a practical map of evaluation workflows.
A Twitter thread outlines the seven key areas that will dominate AI engineering interviews in 2026, including LLM fundamentals, RAG systems, agentic workflows, inference optimization, evaluation, MLOps, and production realities.
This article discusses common reasons for the failure of enterprise AI projects from proof-of-concept to production deployment, highlighting key practices such as MLOps, early inspection of real data, and clear human-machine boundaries. It argues that project failures are often not due to model issues but due to neglect of the engineering implementation phase.
Harvard University open-sourced the textbook "Machine Learning Systems," which systematically covers practical topics such as ML system design, data engineering, model deployment, MLOps, and edge AI, aiming to help bring AI from research into production. It is freely available on GitHub.
A free, open-source AI engineering curriculum covering 20 phases from linear algebra to autonomous agent swarms, with hands-on building in Python, TypeScript, Rust, and Julia. All materials are reusable and connectable to Claude Code or Cursor.
DanKornas introduces an open-source AI Infrastructure Engineer Learning Path, a structured 10-module curriculum covering foundations to LLM infrastructure with hands-on labs and projects.
A comprehensive 500-hour learning path for AI Infrastructure Engineering, covering Docker, Kubernetes, MLOps, LLM infrastructure, and more through hands-on projects and labs.
This paper presents a multi-horizon time series forecasting framework for predicting emergency department boarding time using DLinear and NLinear models, and develops an MLOps web application prototype to support proactive operational decision making.
TRACER is a tool that replaces up to 90% of LLM classification calls with lightweight traditional ML by learning from LLM traces, reducing cost while maintaining accuracy.
Adaption AI introduces AutoScientist, a tool that automates the full research loop to make model training more accessible outside of frontier labs.
Based on interviews with 50+ AI teams, the author highlights that production agent failures often stem from minor prompt or configuration issues rather than deep model problems. The article advocates for adopting software engineering practices like versioning, A/B testing, and experiment tracking to improve reliability.
An opinion piece suggesting that AI teams will increasingly focus on 'harness engineering' and advocating for a review article on the framework.
Fireworks AI announces its training platform in preview, allowing developers to train, fine-tune, and deploy custom AI models with full ownership of data and weights.
Hugging Face introduces Storage Buckets, a new mutable, S3-like object storage feature on the Hub optimized for production ML workflows using its Xet backend for efficient deduplication.
OpenAI shares detailed lessons learned from scaling a single Kubernetes cluster to 7,500 nodes to support large machine learning workloads, covering networking, scheduling, and infrastructure challenges. The post builds on their earlier experience scaling to 2,500 nodes and aims to help the broader Kubernetes community.