# Client Library Reference

- [Installation and Setup](/developers/client-library-reference/installation-and-setup.md): Explore our installation guide to set up Fiddler’s Python SDK client. Learn how to connect, install, import, authorize, and set log levels in your environment.
- [Naming Convention Guidelines](/developers/client-library-reference/naming-convention-guidelines.md): Learn Fiddler's naming requirements: start with lowercase letters, use only a-z, 0-9, underscores and hyphens (Teams only). See examples and best practices.
- [Alerts with Fiddler Client](/developers/client-library-reference/alerts-with-fiddler-client.md): Discover our guide to alerts with Fiddler Client. Learn to set up alert rules to add, delete, and list all alerts, including triggered alerts.
- [Model Onboarding](/developers/client-library-reference/model-onboarding.md)
- [Create a Project and Model](/developers/client-library-reference/model-onboarding/create-a-project-and-model.md): Explore our guide to creating a project and onboarding a model for observation. Learn how projects organize models and define a ModelSpec and Model Task.
- [Customizing Your Model Schema](/developers/client-library-reference/model-onboarding/customizing-your-model-schema.md): Delve into our guide to customize your Model Schema with Fiddler. Learn how to adjust a column’s value range, possible values, and data type to match your model.
- [Updating Model Schema](/developers/client-library-reference/model-onboarding/updating-model-schema.md): Learn how to modify your model's schema after initial creation by adding new columns using the Python client's add\_column() method. Add features, metadata, and tracking columns to existing models.
- [Task Types](/developers/client-library-reference/model-onboarding/task-types.md): Explore our guide to selecting a model task type when onboarding your ML models and LLM applications.
- [Custom Missing Values](/developers/client-library-reference/model-onboarding/specifying-custom-missing-value-representations.md): Learn how you can customize a model column to assign values to be treated as missing or null data in order to handle a value or token that is inserted in place of null.
- [Publishing Production Data](/developers/client-library-reference/publishing-production-data.md): Navigate our client guide to publishing production data. Learn how to provide event data to Fiddler, update it, and retrieve it efficiently.
- [Creating a Baseline Dataset](/developers/client-library-reference/publishing-production-data/creating-a-baseline-dataset.md): Learn to create a baseline dataset and detect data drift in production. Explore baseline types in Fiddler and start building one that fits your model best.
- [Publishing Batches of Events](/developers/client-library-reference/publishing-production-data/publishing-batches-of-events.md): Dive into our guide on publishing batches of events. Learn how Fiddler supports multiple source formats when publishing batches of events.
- [Streaming Live Events](/developers/client-library-reference/publishing-production-data/streaming-live-events.md): Learn how to stream your ML model's inference event data using the Fiddler Python client.
- [Updating Events](/developers/client-library-reference/publishing-production-data/updating-events.md): Dive into our guide on updating inference events. Learn how to update your ground truth labels and metadata using the Fiddler Python client.
- [Deleting Events](/developers/client-library-reference/publishing-production-data/deleting-events.md): Dive into our guide on deleting existing events from your data whether due to regulatory compliance, custom data retention policies, or publishing mistakes.
- [Ranking Events](/developers/client-library-reference/publishing-production-data/ranking-events.md): Explore our guide to publishing production data. Learn how to publish and update ranking events in a grouped format with a detailed example.