Welcome to the Eventual blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
Image Embeddings: Tutorial & Examples
Learn about the concept of image embeddings, their various use cases, and best practices for handling them in data processing workflows.
Multimodal Embeddings: Tutorial & Examples
Learn multimodal embedding techniques for cross-modal search, recommendation systems, and content moderation applications.
How We're Making Observability Better in Daft
Daft Observability Roadmap: metrics, OTEL integration, real-time dashboards, and DataFrame APIs for debugging and monitoring distributed pipelines.
Introducing daft.File: Work with Any File, Anywhere
daft.File brings lazy, distributed handling for audio, video, PDFs, and code to Daft DataFrames. One interface, local or remote.
Tuning Daft's Distributed UDFs: Lessons from ByteDance
Learn from the ByteDance Volcengine LAS Team on how to optimize Daft UDFs on Ray. Discover the formula to evenly distribute data across actors.
Introducing Dynamic Batching: Auto-Tuning for Daft Pipelines
Manually tuning batch sizes is hard. So I implemented dynamic batching to never deal with it ever again.
How We Use AI Coding Agents
Our engineering team's best practices for working with AI coding agents.
How Sourcetable Built the World's First AI Spreadsheet with Daft
Sourcetable CTO Andy Grosser discusses their data infrastructure choices and why reliability and scale drove their architecture decisions.
Multimodal Structured Outputs: Evaluating VLM Image Understanding at Scale
Leveraging ablation for contrastive image understanding evaluation in Daft