Iterative removes friction from getting ML models into production and introduces seamless data scientists collaboration.
Maintain a code repository with data files, ML model files, and model metrics. DVC, an open data repository format is in use.
Pipeline and workflow visualization. Collaborate on ML experiments. Navigate through experiments by model metrics.
Keep track of ML experiments to share knowledge about successful ideas as well as failed ones.
Data scientists, ML engineers, DevOps Your team can work at the same time instead of waiting for handoffs because teams are backlogged.
Provision high performance hardware (CPU, GPU, and memory) for quickly retraining ML models.
Automate ML model deployment and rollback processes.