cat > testobject << EOF
MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.
EOF
```

Upload this object to the bucket via `mc cp`
```
mc cp testobject myminio/functionbucket/
```

## Invoke Lambda transformation via PresignedGET

---

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## Machine Learning modelleri, spaCy ve FastAPI

GitHub: <a href="https://github.com/microsoft/cookiecutter-spacy-fastapi" class="external-link" target="_blank">https://github.com/microsoft/cookiecutter-spacy-fastapi</a>

### Machine Learning modelleri, spaCy ve FastAPI - Features

* **spaCy** NER model entegrasyonu.

* iSCSI volume plugin: iSCSI initiatorname support ([#48789](https://github.com/kubernetes/kubernetes/pull/48789), [@mtanino](https://github.com/mtanino))
* On AttachDetachController node status update, do not retry when node doesn't exist but keep the node entry in cache. ([#50806](https://github.com/kubernetes/kubernetes/pull/50806), [@verult](https://github.com/verult))

    "input": "aa--",
    "output": "aa--"
  },
  {
    "input": "a†--",
    "output": "xn--a---kp0a"
  },
  {
    "input": "ab--c",
    "output": "ab--c"
  },
  {
    "comment": "Label with leading hyphen",
    "input": "-x",
    "output": "-x"
  },
  {
    "input": "-†",
    "output": "xn----xhn"
  },
  {
    "input": "-x.xn--zca",
    "output": "-x.xn--zca"
  },
  {
    "input": "-x.ß",

catch() {
	if [ $# -ne 0 ]; then
		echo "error on line $1"
		echo "server logs ========="
		cat "/tmp/sitea_1.log"
		echo "==========================="
		cat "/tmp/sitea_2.log"
	fi

	echo "Cleaning up instances of MinIO"
	pkill minio
	pkill -9 minio
	rm -rf /tmp/multisitea
	if [ $# -ne 0 ]; then
		exit $#
	fi
}

catch

set -e
export MINIO_CI_CD=1
export MINIO_BROWSER=off

## Healing Metrics

| Name                                         | Description                                                      |
|:---------------------------------------------|:-----------------------------------------------------------------|
| `minio_heal_objects_errors_total`            | Objects for which healing failed in current self healing run.    |

app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":
        return {"model_name": model_name, "message": "LeCNN all the images"}

## Caso de Uso { #use-case }

Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.

Imaginemos que tienes algunos **modelos de machine learning** que quieres usar para manejar requests. 🤖

		"Average last minute latency in µs for drive API storage operations",
		append(allDriveLabels, apiL)...)
	driveHealthMD = NewGaugeMD(driveHealth,
		"Drive health (0 = offline, 1 = healthy, 2 = healing)", allDriveLabels...)

	driveOfflineCountMD = NewGaugeMD(driveOfflineCount,
		"Count of offline drives")
	driveOnlineCountMD = NewGaugeMD(driveOnlineCount,
		"Count of online drives")