The article also explains how to deploy custom models using Cog, Replicate's open-source tool for packaging machine learning models. Cog generates an API server and deploys it on a large cloud cluster, scaling up and down to handle demand. Users only pay for the compute they use. The article provides examples of defining the environment the model runs in with cog.yaml and how predictions are run on the model with predict.py.
Key takeaways:
- You can get started with any open-source model with just one line of code and also have the ability to fine-tune models or deploy your own custom code.
- There are thousands of open-source models published by the community that are ready to use in production.
- You can improve open-source models with your own data to create new models that are better suited to specific tasks.
- You can deploy your own custom models using Cog, an open-source tool for packaging machine learning models, which takes care of generating an API server and deploying it on a big cluster in the cloud.