Getting Started

Installation

You can install AdamOps and all of its extensive features directly from PyPI:

pip install adamops

Using the Model Playground

AdamOps features a built-in Streamlit dashboard for instantaneously visualizing your trained models, evaluating metrics, and generating predictions.

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from adamops.deployment.playground import launch_playground

# 1. Load data
data = load_breast_cancer(as_frame=True)
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 2. Train a standard model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# 3. Launch the playground to interact with your model!
launch_playground(model=model, X_test=X_test, y_test=y_test)

The dashboard will open in your browser automatically, providing live sliding predictions, confusion matrices, and interactive data exploration over your test dataset.

Using AdamOps Studio

If you prefer building ML workflows visually instead of writing code, you can use the AdamOps Studio pipeline builder.

Launch the studio from your terminal:

adamops studio

Or launch it directly from Python:

from adamops.studio import launch
launch()

This commands opens a drag-and-drop web interface where you can assemble data loading, preprocessing, model training, and evaluation nodes into an executable pipeline graph.