Resources · Industry insights
Notes on the data behind AI
What we've learned about data ops, annotation, and pipelines from labeling and preparing training data since 2021.
Multi Signal Data Labeling: Labels Based on Everything You Know
Data labeling is usually pictured as one file and one annotation. The harder, more valuable work labels a file using every other signal that surrounds it. Here is why multi signal labeling is becoming table stakes.
Prompts Are the New Bounding Boxes: Human-in-the-Loop for Synthetic Data
Generating data doesn't remove people from the loop. Instead of drawing boxes, they write prompts, set variables, and review the results. The work looks surprisingly similar to data labeling.
How Companies That Scraped the Web Before 2022 Got Lucky
Since generative AI went mainstream, more of the web is synthetic. Companies holding web data collected before 2022 now have an advantage that may be hard to match.
Why Data Sameness Matters More than You May think
In practice, model performance is deeply constrained by the data used during training. Sophisticated models trained on limited or poorly curated datasets rarely outperform simpler models trained on richer and more representative data.
From Raw Video to AI-Ready Data: Solving the Unstructured Data Problem in Computer Vision
One of the most persistent bottlenecks is not model architecture. It is data preparation.
The AI Data Problem We're Not Talking About
AI’s biggest bottleneck isn’t compute power. It’s data. With Stage 2 Capital, we explore what it really takes to solve AI’s data bottleneck—at scale.
What Is a Video Annotation Tool?
Video annotation tools are a big part of a larger ecosystem of data labeling tools.
You are working in Data Ops for AI? Wait, what do you do again?
Audrey Smith on eight years in data operations for AI: what the role is, why it's underrated, and why data ops people are key to shipping AI products.
What does a Data Ops role entail?
As we all know by now, a very good model with crappy data, will get you...well…a crappy model performance.
Using Large Language Models For Extract, Transform, And Load On AI Data : An MLtwist Brief
LLMs beneficial to a no-code ETL solution.
Data labeling operations in the context of AI
Audrey Smith joins Raghu Banda on the Extra AI podcast to discuss data labeling operations, data privacy, and AI model transparency.
Data-Centric AI: Why This Trend is Here to Stay
Several months ago, MLtwist had the pleasure of participating in the TWIML AI panel on Data-Centric AI.
Get your data ready for AI
Tell us what data you have and where it lives. We'll set it up on the platform, run by your team, ours, or both — and deliver it versioned, in your format.
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