## Layer - Metadata Store for Production ML
[Layer](https://layer.ai) helps you build, train and track all your machine learning project metadata including ML models and datasets with semantic versioning, extensive artifact logging and dynamic reporting with local↔cloud training
**[Start for Free now!](https://app.layer.ai/login?returnTo=%2Fgetting-started)**
## Getting Started
Install Layer:
```shell
pip install layer --upgrade
```
Login to your free account and initialize your project:
```python
import layer
layer.login()
layer.init("my-first-project")
```
Decorate your training function to register your model to Layer:
```python
from layer.decorators import model
@model("my-model")
def train():
from sklearn import datasets
from sklearn.svm import SVC
iris = datasets.load_iris()
clf = SVC()
clf.fit(iris.data, iris.target)
return clf
train()
```
Now you can fetch your model from Layer:
```python
import layer
clf = layer.get_model("my-model:1.1").get_train()
clf
# > SVC()
```
[**🚀 Try in Google Colab now!**](https://colab.research.google.com/github/layerai/examples/blob/main/tutorials/add-models-to-layer/how_to_add_models_to_layer.ipynb)
## Reporting bugs
You have a bug, a request or a feature? Let us know on [Slack](https://bit.ly/layercommunityslack) or [open an issue](https://github.com/layerai/sdk/issues/new/choose)
## Contributing code
Do you want to help us build the best metadata store? Check out the [Contributing Guide](https://github.com/layerai/sdk/blob/main/CONTRIBUTING.md)
## Learn more
- Join our [Slack Community ](https://bit.ly/layercommunityslack) to connect with other Layer users
- Visit the [examples repo](https://github.com/layerai/examples) for more inspiration
- Browse [Community Projects](https://layer.ai/community) to see more use cases
- Check out the [Documentation](https://docs.layer.ai)
- [Contact us](https://layer.ai/contact-us) for your questions