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Models & integrations · 28 articles · Page 2 of 3
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.
Design and implement retrieval-augmented generation pipelines that go beyond simple demos, covering chunking strategies, embedding optimization, reranking, and evaluation metrics for production AI applications.
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.
How to integrate LLMs into your code review workflow — covering automated review bots, prompt patterns for catching bugs, security analysis, and balancing AI suggestions with human judgment.
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.