Labelbox

says Ishan Babbar, lead data scientist at Nayya.An SDK-driven approach let its actuaries — the subject matter experts — share how the model generates predictions and evaluate them.We use Labelbox's Python SDK almost religiously. Part of the main value we get from the platform is being able to interact with how the data processing is going, performance quality metrics, and loop that information back to our actuaries and subject matter experts,

One of the primary things we care about is the quality of our training data, especially when it comes to streaming speech recognition. It's not just about perfect pronunciation; it's about whether the user reads the line and if their speech is understandable to an English speaker. This task involves subjectivity, so we focus on how consistently we hit that standard. Our goal is to generate ground-truth datasets where there's strong agreement on quality. At the end of the day, quality is king—it matters much more than quantity. Achieving this level of quality requires iteration and a dedicated team of the same people who can work consistently on refining it which Labelbox helps us with.— Andrew Hsu, Co-founder and CTO of Speak

At the highest level, we now have a way to tackle something that used to be very painful—an automatic labeling loop that lets us evaluate our speech systems daily on production data. This loop allows us to continuously retrain our models, which has led to significant improvements. For example, we saw model accuracy improvements of 35% after using Labelbox, and what effectively translated into a 2x increase in speed in terms of accelerating model development.— Tobi Szuts, Machine Learning Engineer at Speak