Product
Improving Datasets and Debugging Machine Learning Models with Scale Nucleus, Rebuilt from the Ground Up
by Scale Team on September 13th, 2021
It’s been an exciting journey since we shipped Scale Nucleus for the first time just over a year ago. We’ve helped over 100 different organizations explore, curate and improve their datasets and debug their machine learning (ML) models, across semantic segmentation, object detection, and even 3D point clouds. Today, we’re excited to share an all-new Nucleus, with powerful new features and a redesigned user interface that make it easier than ever to create better models with better datasets.
To improve ML models requires first understanding where they fail. Nucleus helps you go beyond aggregate error metrics, and instead quickly discover specific patterns of failure across your model predictions and dataset labels. Once you’ve identified problems, Nucleus makes them easy to fix. Address bad labels through efficient, model-assisted data quality assurance (QA) and one-click labeling integration, or improve model predictions through targeted data curation of what to label next.
Scale Nucleus is one of the most intuitive and efficient ways to select the right data for labeling: random sampling shouldn’t be the way you select data to label. Training better-performing models requires a focus on edge cases, false positives, and the portion of minority classes or edge cases that are relevant to the modeling problem you’re trying to solve. And your curation process shouldn’t be overly time-intensive or manual.
As we launched Nucleus, we explained that better ML starts with understanding your data in depth. To improve production ML, you need to understand your models’ qualitative failure modes, fix them by gathering the right data, and curate diverse scenarios.
From Similarity Search to Autotag:
When Scale customers look to improve ML accuracy, they find that their models often struggle with minority classes and edge cases, what we refer to as “the long tail.” It might be a QA team that highlights challenges with a self-driving vehicle, like the relatively rare combination of entering a tunnel at nighttime behind a truck. Although this cross-section accounts for only a small fraction of driving scenarios, their computer vision models must be capable of handling them just as well.
In the below example, we’ve identified uncertain samples, and using a similarity search based on internal models and feature vectors, surfaced similar portions of the dataset for labeling or QA. This process culminates in our Autotag feature:
Autotag is an incredibly efficient way to tag similar objects or scenes with a new class. Simply select a few similar images of the class you are trying to classify or create a tag for, click ‘Autotag,’ and then further refine your set of images with positive and negative examples. Scale Nucleus will then create an Autotag, an internal classification attached to the subset of your dataset that Scale identified as similar. You can then retrieve objects in your dataset based on this classification, even if labels for it don’t exist in your ground truth.
The same useful core, with refined usability:
Nucleus focuses on several main use cases that we’ve developed in conjunction with our customers:
Understand the strengths and weaknesses in your dataset as you identify ways to improve quality:
Analyze the long tail of your dataset, with collaborative label edits, and granular insights like Intersection over Union (IoU):
Examine metrics, failure cases, and confusion matrices, all linked to your underlying training data.
Scale Nucleus was built with the explicit goal of helping ML teams improve their datasets to extract better and better performance out of models they’re training on their data. Whether you’re starting out with an industry-standard dataset, and then execute transfer learning with the addition of proprietary data, or training a model entirely on your own data, Nucleus helps you train your model on the data that matters.
Privacy Mode: using Nucleus without sharing sensitive data
Several prospective customers asked if they could use Scale Nucleus without uploading their training datasets to our cloud. Accordingly, we created Privacy Mode in conjunction with our existing API, letting you use Nucleus to curate your dataset without sensitive raw data ever leaving your servers
With Privacy Mode, you can submit URLs to Nucleus that link to raw data assets like images or point clouds, instead of transferring that data to Scale. These URLs may optionally be protected behind your corporate VPN or an IP whitelist. When you load a Nucleus web page, your browser will directly fetch the raw data from your servers without it ever being accessible to Scale. Privacy Mode even works well with similarity search and Autotag, as users can create custom model embedding indexes for their datasets.
What customers are saying about Nucleus:
“KeepTruckin encounters all manners of surprising edge cases in real world data collection, so when it comes to knowing we’re labeling the most valuable subset of our collected data, we turn to Scale Nucleus. Its intuitive visualizations, query engine and Autotag help our teams improve both data quality and models, all in the same motion.”
—Ali Rehan, Engineering Manager AI/Vision Products, KeepTruckin
Nucleus makes it easy to query data samples based on their metadata, or simply bucket images based on “similarity,” specifically along feature vectors of a Scale-trained model. For example, if you know your model should be detecting police vehicles and it’s not doing so already, you can quickly query for a handful of examples, find similar images, and through simple positive and negative feedback to Nucleus, identify a subset of your dataset to send out for labeling.
One year in, Scale Nucleus has already come a long way towards building a new generation of ML tooling, but we’re just getting started. If you’d like to join us in this journey and try Scale Nucleus, sign up on our website or schedule a demo.