Introduction to Timber
Timber is a powerful tool that compiles trained tree-based models into optimized native C and serves them over a local HTTP API. This means that teams can now deploy their machine learning models without the need for a Python runtime, resulting in faster and more predictable inference.
Benefits of Using Timber
With Timber, teams can enjoy native latency, which is measured in microseconds. Additionally, the tool provides a simple and efficient way to load and serve models, making it an ideal solution for teams that need fast and reliable model deployment.
Some of the key benefits of using Timber include:
- Faster inference times
- Improved predictability and reliability
- Native latency (microseconds)
- One command to load, one command to serve
Who Can Benefit from Timber
Timber is designed for teams that need fast, predictable, and portable inference. This includes:
- Fraud/risk teams running classical models in low-latency transaction paths
- Edge/IoT teams deploying models to gateways and embedded devices
- Regulated industries (finance, healthcare, automotive) needing deterministic artifacts and audit trails
- Platform/infra teams replacing Python model-serving overhead with native binaries
For example, a fraud detection team can use Timber to deploy their machine learning model and enjoy faster and more reliable inference times.
Getting Started with Timber
To get started with Timber, simply install the timber-compiler using pip and load your model using the timber load command. You can then serve your model using the timber serve command and make predictions using the /api/predict endpoint.
