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

Humanloop + Hugging Face 🤗 = the fastest way to build custom NLP

Last week I had the privilege to sit down with Sam Altman and 20 other developers to discuss OpenAI’s APIs and their product plans. Sam was remarkably open. The discussion touched on practical developer issues as well as bigger-picture questions related to OpenAI’s mission and the societal impact of AI.  Here are the key takeaways.

OpenAI's plans according to Sam Altman

In this post, we'll explore the fundamentals of prompt engineering. We'll explain how Large Language Models (LLMs) interpret prompts to generate outputs, and provide tips and tricks to get you started prototyping and implementing LLMs quickly.

Prompt Engineering 101

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

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

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.

Humanloop + Hugging Face 🤗 = the fastest way to build custom NLP

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