Iterating over analyses is an essential part of model training. #ML engineers run many experiments, tweaking models and tuning hyperparameters. This slows down the entire development process. Reports make it easier to track the progress and see how models improve. That's why we created Reports for you! 🎉

🖼️⏯️With simple commands you can retrieve images and audios from Aim storage. You can also retrieve metrics, figures and texts and group them by  color, stroke_style, row, column.

🚀 Hosting Aim on Kubernetes (K8S) is a game-changer for ML practitioners! 1. All your training data and runs in one place, accessible to everyone in your org from everywhere 2. Aim runs can be centralized on a remote volume, providing additional support for remote model training and monitoring 3. Deployment to K8S abstracts away the Aim CLI, letting users focus on visualizations and applications without setup worries #Kubernetes #MLops #Aim #experimenttracking #ml

The core advantage of using a K8S volume to store Aim runs is that other K8S deployments can mount the same volume and store their runs on it. This way, the core Aim K8S deployment can read the new runs and display them to users who want to visualize their results. For example, one can have a deployment that performs model training and records Aim runs on the same volume mounted to the Aim deployment. This model is illustrated by the following diagram:

👀Track the gradient, the weights and the biases across all layers with Aim. Use the Distributions tab to: - observe single runs, - navigate between layers, - search distributions by step. 1/2