Nyckel
by Nyckel, Inc. • San Diego, CA, USA • Founded 2021
Train Custom ML Classification Models in Minutes Without Code
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21 reviews
What is Nyckel?
Nyckel is a no-code AutoML platform that enables teams to build, train, and deploy custom image, text, and tabular classification models in minutes. Powered by transfer learning, it automatically benchmarks hundreds of ML models against your data to find the highest-accuracy option.
Nyckel provides a RESTful API and Python SDK for production integration, along with active learning, invoke capture, and cross-validation for continuous model improvement. Backed by Y Combinator (W22), it is SOC 2 certified and HIPAA compliant, serving use cases like content moderation, defect detection, fraud classification, and document sorting at millisecond inference speeds.
Nyckel Demo Video
Key Features 8
Who Is Nyckel For
Pros & Cons
- Sub-Minute Model Training Speed
- Millisecond Inference Latency
- SOC 2 and HIPAA Certified
- Strong Python SDK Integration
- Limited to Classification Tasks
- No Object Detection Below $500
- Opaque Model Selection Process
Frequently Asked Questions
5 questionsNyckel supports three input data types: image, text, and tabular. Tabular inputs can combine text fields, number fields, and image fields within a single classification function, enabling multimodal predictions.
Nyckel's active learning automatically identifies the most informative samples from your production invokes and surfaces them for review. This lets your team focus annotation effort on data points that will improve model accuracy the most.
Nyckel can begin training a functional classifier with as few as 5 to 10 samples per label. Accuracy improves as more labeled data is added, and the platform trains new models within seconds of each update.
Yes. Nyckel offers a RESTful API, a Python SDK, and a Zapier integration. Models can be invoked via HTTP POST requests with JSON or multipart form data, making it straightforward to embed classification into any application stack.
Invoke capture automatically saves a subset of production invocations that the model finds most uncertain. These captured samples are queued in the dashboard for human annotation, creating a feedback loop that continuously strengthens model performance.
Trusted Reviews
Verified PlatformsWhat's New
monthlyBenchmarked ModernBERT across 146 datasets, providing transparency on transfer learning model performance within the platform.
Detailed a new approach to model development using transfer learning-based AutoML on AWS Lambda for faster and more cost-efficient training.
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Nyckel Pricing
From $50/mo
- 1,000 training samples
- 1,000 invokes included
- Image, text, and tabular classification
- API and SDK access
- 10,000 training samples
- 10,000 invokes included
- All classification function types
- Active learning and invoke capture
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