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17 articles
A practical guide to building evaluation pipelines for LLM-powered features covering semantic similarity metrics, behavioral testing, regression detection, and automated quality gates.
Fine-tune language models on your domain's data using parameter-efficient techniques like LoRA and QLoRA, with practical guidance on dataset preparation, training strategies, and evaluation methods that produce useful specialists without massive compute budgets.
Build effective RAG pipelines with practical TypeScript examples covering document chunking strategies, embedding generation, vector search tuning, prompt construction, and evaluation methods that measure retrieval quality.
Compare vector database options including Pinecone, Weaviate, Qdrant, Milvus, and pgvector for AI applications, analyzing indexing strategies, query performance, filtering capabilities, and operational complexity trade-offs.
Design systematic evaluation pipelines for LLM-powered applications covering automated metrics, human evaluation protocols, regression testing, prompt versioning, and production monitoring for output quality.
A practical guide to fine-tuning language models on domain-specific data, covering dataset preparation, training strategies, evaluation methods, and deployment considerations for production systems.
Learn practical techniques for detecting and mitigating bias in machine learning pipelines, from data collection through model deployment and monitoring.
Explore federated learning concepts, implementation patterns, and practical challenges of training machine learning models across distributed devices while preserving data privacy.
How to implement anomaly detection for application metrics — covering statistical methods, z-scores, moving averages, seasonal decomposition, and building alerting systems that catch real incidents without alert fatigue.
A practical guide to vector databases for similarity search — covering embeddings, indexing strategies, distance metrics, and when to use Pinecone, Weaviate, pgvector, or FAISS for your use case.
How to build production-ready NLP pipelines — covering text preprocessing, tokenization, embeddings, classification, and entity extraction with practical TypeScript and Python examples.
A practical introduction to recommendation systems — covering collaborative filtering, content-based filtering, and hybrid approaches with TypeScript implementations developers can understand and adapt.
A developer-friendly walkthrough of the Transformer architecture that powers GPT, BERT, and modern LLMs — covering self-attention, positional encoding, and the encoder-decoder structure with code examples.
A practical introduction to reinforcement learning concepts — agents, environments, rewards, and policies — with TypeScript examples that map RL ideas to problems developers already understand.
A beginner-friendly guide to computer vision fundamentals using Python and OpenCV — covering image processing, edge detection, object detection, and building a practical image classifier.
A practical introduction to natural language processing concepts web developers need — tokenization, sentiment analysis, named entity recognition, and integrating NLP APIs into applications.
A practical introduction to machine learning concepts that backend engineers need when integrating ML models into production applications.