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Copy file name to clipboardexpand all lines: README.md
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## 🚀 Bringing it to Production 🚀
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BentoML offers a number of options for deploying and hosting online ML services into production, learn more at [Deploying Bento Docs](https://docs.bentoml.org/en/latest/concepts/deploy.html).
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## 🚀 Deploying to Production 🚀
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Effortlessly transition your project into a production-ready application using [BentoCloud](https://www.bentoml.com/bento-cloud/), the production-ready platform for managing and deploying machine learning models.
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Start by creating a BentoCloud account. Once you've signed up, log in to your BentoCloud account using the command:
> Note: Replace `<your-api-token>` and `<bento-cloud-endpoint>` with your specific API token and the BentoCloud endpoint respectively.
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Next, build your BentoML service using the `build` command:
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```bash
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bentoml build
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```
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Then, push your freshly-built Bento service to BentoCloud using the `push` command:
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```bash
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bentoml push <name:version>
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```
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Lastly, deploy this application to BentoCloud with a single `bentoml deployment create` command following the [deployment instructions](https://docs.bentoml.org/en/latest/reference/cli.html#bentoml-deployment-create).
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BentoML offers a number of options for deploying and hosting online ML services into production, learn more at [Deploying a Bento](https://docs.bentoml.org/en/latest/concepts/deploy.html).
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## 👥 Community 👥
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BentoML has a thriving open source community where thousands of ML/AI practitioners are
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