* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.

## Use Case { #use-case }

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

///

/// tip | Tips

"AlxexNet", "ResNet", dan "LeNet" adalah nama <abbr title="Secara teknis, arsitektur model Deep Learning">model</abbr> *Machine Learning*.

///

### Mendeklarasikan *parameter path*

Kemudian buat *parameter path* dengan tipe anotasi menggunakan *class* enum dari (`ModelName`)

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Piensa en una enorme hoja de cálculo con números y multiplicando todos juntos al mismo tiempo.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

///

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

///

/// tip | Consejo

Si te estás preguntando, "AlexNet", "ResNet" y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.

///

### Declarar un *path parameter*

Luego crea un *path parameter* con una anotación de tipo usando la clase enum que creaste (`ModelName`):

* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

link: https://eng.uber.com/ludwig-v0-2/ title: 'Uber: Ludwig v0.2 Adds New Features and Other Improvements to its Deep Learning Toolbox [including a FastAPI server]' - author: Maarten Grootendorst author_link: https://www.linkedin.com/in/mgrootendorst/ link: https://towardsdatascience.com/how-to-deploy-a-machine-learning-model-dc51200fe8cf title: How to Deploy a Machine Learning Model - author: Johannes Gontrum author_link: https://x.com/gntrm link: https://medium.com/@gntrm/jwt-authentication-w...

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