Data labeling is often the biggest bottleneck in machine learning. Active learning lets you train machine learning models with much less labeled data. The best AI-driven companies, like Tesla, use active learning.

Why you should be using active learning to build ML

We’re excited to be partnering with Stability AI, to build and release the first open-source large language model that can follow human instructions.

Humanloop partners with Stability AI to build the first open-source InstructGPT

Today we're excited to open up access to Humanloop for all developers who want to build next-generation applications with large language models like GPT-3.

Announcing: Humanloop for Large Language Models

Today, we are announcing the preview of Humanloop for Large Language Models (LLMs). Sign up to be part of the closed beta.

Coming soon: Humanloop for LLMs


Machine learning test metrics should always be calculated with credible intervals. Credible intervals give you upper and lower bounds on test performance so you know how big your test needs to be and when to trust your models. Humanloop Active Testing can give you uncertainty bounds on your test metrics and makes this easy.

Why you need to calculate error bounds on your test metrics

We're really excited to announce Programmatic 4.0 with support for No-Code Templates — simple UI-based labeling functions that anyone can understand even if they don't know how to program.

Announcing Programmatic 4.0

There are huge advantages to labeling in-house such as quality control, faster iteration and privacy. New technologies like transfer learning, programmatic labeling and active learning are now making it practical for the best teams.

Why you should be annotating in-house

Humanloop Programmatic is now available for early access. A powerful weak labeling tool to rapidly annotate your NLP datasets.

Introducing Humanloop Programmatic

Weak labeling can replace manually annotated data with data created from heuristic rules written by domain experts. Here I want to explain what weak supervision is, what I learned and why I think it complements techniques like active learning for data annotation.

Why I changed my mind about weak labeling for ML

Human-in-the-loop AI is an automation approach which means you can quickly deploy a working model, with less data and with guaranteed quality predictions. In this post we'll explain what HITL is and how you can make use of this approach in your own AI projects.

What is human-in-the-loop AI?

Machine learning systems are born through the marriage of both code and data. Academia mostly focuses on ways to improve the learning algorithms, the _how_ of machine learning. When you come to build practical AI systems though the dataset you're training on has at least as much impact on performance as the choice of algorithm.

Why you should take a data first approach to AI

Data first ML Ops needs new kinds of tools. An Active Learning platform is a single tool that combines data annotation, model training and deployment in one continuous process.

What is an active learning platform?

We discuss the real world performance of Open AI's GPT-3 language model and speculate on what the future of large pre-trained language models may hold.

How good is GPT-3 in practice?

The most surprising lessons about how to build tooling for machine learning, what is needed going forwards and why domain experts will play a much larger role in the future of AI.

Four lessons from a year building tools for machine learning

Humanloop worked with Black Swan Data, a company at the forefront of applied NLP, to test our active learning platform. Humanloop was able to save at least 40% of the labeling normally required whilst improving performance.

Measuring Active Learning performance in the real world

You can now train any Hugging Face model on your own data just by labeling and curating a small portion of that data, with early access to our Humanloop - Hugging Face integration. Label your data and we'll give you an API to your model, trained on your own data

Why you should be using active learning to build ML

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