Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 5: From Research to Product
One command to fine-tune SPLADE for your catalog. No ML pipeline assembly required.
Thierry Damiba
March 9, 2026
Mastering Search
Go beyond the basics and master vector search with Qdrant. Learn how to combine filtering, hybrid retrieval, multivectors, and reranking to build high-quality search.
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 4: Specialization vs Generalization
When to fine-tune sparse embeddings and how far to specialize before generalization suffers.
Thierry Damiba
March 9, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 3: Evaluation and Hard Negatives
Evaluate fine-tuned SPLADE with Qdrant and boost results with hard negative mining.
Thierry Damiba
March 9, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 2: Training SPLADE on Modal
Train a SPLADE model on Amazon's ESCI dataset using Modal's serverless GPUs and Sentence Transformers.
Thierry Damiba
March 9, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 1: Why Sparse Embeddings Beat BM25
Dense embeddings blur exact matches. Sparse embeddings keep the details that matter in e-commerce search.
Thierry Damiba
March 9, 2026
MUVERA: Making Multivectors More Performant
Making multi-vector retrieval more efficient by approximating it with single-vector search
Kacper Łukawski
September 5, 2025
Optimizing Semantic Search by Managing Multiple Vectors
Qdrant's approach to storing multiple vectors per object, unraveling new possibilities in data representation and retrieval.
Kacper Łukawski
October 5, 2022
Mastering Batch Search for Vector Optimization
Introducing efficient batch vector search capabilities, streamlining and optimizing large-scale searches for enhanced performance.
Kacper Łukawski
September 26, 2022