profile base to use as a starting point for customization. See the [API reference](https://github.com/istio/api/blob/master/operator/v1alpha1/operator.proto)
for details.

## Developer quick start

The quick start describes how to install and use the operator `mesh` CLI command and/or controller.

### CLI

To build the operator CLI, simply:

```bash
make build
```

    } catch (MinioException e) {
      System.out.println("Error occurred: " + e);
    }
  }
}
```

## Explore Further

- [Use `minio-java` SDK with MinIO Server](https://min.io/docs/minio/linux/developers/java/minio-java.html)
- [Object Lock and Immutablity Guide](https://min.io/docs/minio/linux/administration/object-management/object-retention.html)

[Test using MinIO Client `mc`](#test-using-minio-client-mc) for more information on using the `mc` commandline tool. For application developers,
see <https://min.io/docs/minio/linux/developers/minio-drivers.html> to view MinIO SDKs for supported languages.

nature and they do not require/implement rename API. The default S3A committer emulates renames through copy and delete APIs. This interaction pattern causes significant loss of performance because of the write amplification. _Netflix_, for example, developed two new staging committers - the Directory staging committer and the Partitioned staging committer - to take full advantage of native object storage operations. These committers do not require rename operation. The two staging committers were evaluated,...

* **Fast to code**: Increase the speed to develop features by about 200% to 300%. *
* **Fewer bugs**: Reduce about 40% of human (developer) induced errors. *
* **Intuitive**: Great editor support. <abbr title="also known as auto-complete, autocompletion, IntelliSense">Completion</abbr> everywhere. Less time debugging.

[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine