Metadata-Version: 2.1
Name: modelify
Version: 0.0.2.4
Summary: New Version of MLOps Platforms.
Home-page: UNKNOWN
Author: Muzaffer Senkal
Author-email: info@modelify.ai
License: MIT
Keywords: mlops,machine learning,model deployment,deploy model,data science,computer vision
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: cloudpickle
Requires-Dist: python-multipart
Requires-Dist: jinja2
Requires-Dist: Pillow
Requires-Dist: onnxmltools
Requires-Dist: onnxruntime
Requires-Dist: skl2onnx
Requires-Dist: requests-toolbelt
Requires-Dist: tf2onnx
Requires-Dist: pydantic
Requires-Dist: tqdm
Requires-Dist: python-dotenv
Requires-Dist: pyngrok
Requires-Dist: nest-asyncio
Requires-Dist: click

# Modelify

Modelify takes over all devops jobs from data scientists and machine learning practitioners and brings their models to production.

## Install 

```
pip install modelify
```

## Usage

Deploying LightGBM Model

```python
import pandas as pd
from sklearn.datasets import load_iris
from lightgbm import LGBMClassifier, Dataset, train as train_lgbm
import modelify
from modelify import ModelInference
from modelify.helpers import create_schema

# import data
iris = load_iris()
df= pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                     columns= iris['feature_names'] + ['target'])

# train test split
train, test = train_test_split(df, test_size=0.2 )
y_train = df["target"]
X_train = df.drop(columns=["target"])

# build your model
clr = LGBMClassifier()
clr.fit(X_train, y_train)

# deployment
inference = ModelInference(model=model, framework="LIGHTGBM", inputs=create_schema(X_train))

modelify.connect("YOUR_API_KEY")

modelify.deploy(inference, app_uid="YOUR_APP_UID")

```


