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Pros & Cons
Open source platform
Experiment tracking feature
Powerful visualization capabilities
Model evaluation
Model registry
Manages end-to-end workflows
Aids in application building
Tracks progress during fine-tuning
Facilitates packaging and deploying models
Secures hosting models at scale
Runs on Databricks, cloud, PCs
Integrates with PyTorch, TensorFlow
Integrates with LangChain, Spark
Integrates with Keras, Prophet
Integrates with scikit-learn, XGBoost
Integrates with LightGBM, CatBoost
Used by global companies
Fine tuning progress tracking
Securely hosts LLMs at scale
Active global contributor community
Constant version updates
14M+ monthly downloads
600+ worldwide contributors
Provides how-to guides, tutorials
Lack of customer support
Complex Configuration
No GUI
No real-time collaboration
Minimum workflow automation
Limited algorithm support
Incomplete documentation
No built-in hyperparameter tuning
Limited integration options
Dependent on Python environment
