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