AI security blog

We have released an implementation of OpenAI’s CLIP model that completely removes the need for PyTorch, enabling you to quickly and seamlessly install this fantastic model in production and even possibly on edge devices.

We are excited to announce the release of our first integration with FiftyOne by Voxel51. This integration makes it possible to benefit from FiftyOne's powerful visualization features to dig into the insights generated by Lakera's MLTest. Read on to learn how you can benefit from this.

As we all know from machine learning 101, you should split your dataset into three parts: the training, validation, and test set. You train your models on the training set. You choose your hyperparameters by selecting the best model from the validation set. Finally, you look at your accuracy (F1 score, ROC curve...) on the test set. And voilà, you’ve just achieved XYZ% accuracy.

Deep-dive into advanced comparison methods beyond standard performance metrics to build computer vision models that consistently perform over the long term.

Model selection is a fundamental challenge for teams deploying to production: how do you choose the model that is most likely to generalize to an ever-changing world?

of prompt injections that are currently in discussion. What are the specific ways that attackers can use prompt injection attacks to obtain access to credit card numbers, medical histories, and other forms of personally identifiable information?