130 related articles

In the AI wave, ML engineers' work is quietly shifting: from building models to using them, from feature engineering to LLM app development. This article outlines the new skills to prioritize, fading old ones, and how to turn AI into career leverage.

Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.

Torn between Géron, Chollet, and Raschka? This article breaks down 4 classic ML books for self-learners aiming at finetuning and small language models (SLM), helping you find the best advanced path.

Systematically learn ChatGLM large model development, covering Transformer principles, RAG, private deployment, fine-tuning, and Agent development, with a roadmap and hands-on cases.

Learn how to fine-tune Google's open-source PaliGemma 2 VLM for custom object detection. Covers model architecture, data formatting, fine-tuning strategies, and real-world value.

An AI research engineer with 3 years of experience sent 50 applications to FAANG with zero replies. This article breaks down the hidden barriers of top-tech AI roles, the truth about LinkedIn ghost jobs, and the MLE vs. Research Engineer divide.

OpenAI's GPT-5.6 series benchmarked: flagship Sol, balanced Terra, and lightweight Luna tested head-to-head. Agentic tasks rival top models, Luna starts at $1/M tokens. Full comparison with Fable 5 and Opus 4.8.

A collection of 28 fully reproducible enterprise-grade AI Agent projects covering code debugging, financial analysis, customer service, and multi-agent collaboration—deployable even for beginners.

Model capabilities are converging, making inference cost and scalability the new focus of AI competition. A deep analysis of AI infrastructure's core layers.

Want to break into LLM development but not sure where to start? This guide breaks the core skills into four progressive layers — from basic knowledge to RAG, fine-tuning, Agents, and multimodal — so you can align with real enterprise needs and land the job.

In-depth guide to Kaggle's free-tier compute: P100/T4 GPU with 30 hours/week quota, 12-hour sessions, suitable models like CNN and BERT fine-tuning, plus tips like mixed precision and checkpointing to start deep learning at zero cost.

Unsloth v0.1.462-beta adds full keyboard navigation to the Studio Model Picker, fixes Tab focus order, and improves accessibility for LLM fine-tuning workflows.

Unsloth v0.1.463-beta fixes a Studio crash caused by access-denied errors during llama-server service discovery. Improves stability for multi-user servers and Windows environments.

A systematic guide to the three cores of OpenAI LLM app development: GPT-4/GPT-3.5 model selection, token billing and cost-saving tips, and practical use of the Models, Completion, and Chat Completion APIs.

Want to switch careers into LLM development but don't know where to start? This guide breaks down a four-level skill roadmap — from basics and API calls to RAG, fine-tuning, Agent development, and multimodal — to help you build real AI career value.

As LLM costs keep falling, how can Java developers seize the AI opportunity? This article explores LangChain4J's core capabilities, supported models and vector databases, and compares LangChain4J vs. Spring AI to help you build local knowledge bases and intelligent customer service systems.

Hugging Face's open-source ml-intern autonomously reads papers, writes training scripts, and finetunes LLMs, deeply integrating the HF ecosystem and smolagents. Explore its features and impact on ML careers.

A tailored large-model learning path for ordinary programmers: from prompt engineering, API calls, and LangChain, to RAG, Agents, fine-tuning, and enterprise deployment—six steps to build AI application skills fast.

Unsloth v0.1.46-beta is out with key DiffusionGemma changes: tool calling disabled by default, artifacts canvas enabled. A deep dive for LLM fine-tuning devs.

Unsloth v0.1.461-beta fixes local GGUF vision model loading on llama-server in Studio, adds variant directory companion file lookup for stable multimodal deployment.