Iguazio vs. Weights & Biases

With Iguazio we are able to scale up our organisations AI infrastructure which us vital to meet business goals and accelerate time-to-time. We are also able to manage our ML pipeline end-to-end using a full-stack,user-friendly environment, feature-rich integrated feature store and powerful data transformation and real-time feature engineering capabilities.

Incentivized

No brainer to use it when doing ML experiments as it is very easy compared to any other open source tool. You don't have to host anything like in Tensorboard.

Experiment details can be shared very easily with public using the reports

Incentivized

• Dynamic scaling capacity.
• Central Metadata management.
• Data ingestion and preparation.

Incentivized

• Metrics Logging
• Hyperparmeters Sweeps
• Model Artifcats

Incentivized

• The user interface is not so much user-friendly, and easy-to-use, navigate.

Incentivized

• Dashboard lags when we log a lot of metrics
• Improved support for matplotlib charts and documentation of wandb custom charts is not straghtforward

Incentivized

Execution, experiment, data, model tracking, and automated deployment is done automatically through the MLRun serverless runtime engine. MLRun maintains a project hierarchy with strict membership and cross-team collaboration. End-to-end data governance is fully solidified and managed with authentication and identity management. Customers securely share data by providing access directly to it and not to copies.

Incentivized

• Is a fully integrated solution with a user-friendly portal.
• Manage our ML pipeline end-to-end using Full-stack,user friendly environment.
• Iguazio enables our teams to manage all artefacts throughout their lifecycle.
• Enhance team work and collaboration in our teams.

Incentivized

• Made it very easy to track experiments
• Track ML and Business Metrics improvements across experiments
• Reproduce runs which is essential in ML modelling

Incentivized