Master Machine Learning: Optimize Model Costs and Performance

This short session from the Weaviate Community Series breaks down the critical factors to consider when selecting embedding models for machine learning applications, focusing on the balance between performance and cost. Learn about the trade-offs between model size and infrastructure requirements, how vector embedding dimensions affect both semantic richness and storage costs, and strategies for managing latency and throughput demands. Explore the MTEB leaderboard for comparing model performance, discover open-source alternatives to proprietary solutions, and see a demonstration of storing and querying vector embeddings using Weaviate Cloud's free tier. Perfect for developers and data scientists who need practical guidance on optimizing machine learning implementations beyond just accuracy metrics.