How Featrix Prism Works: The Easiest Path to AI Success
1. Embeddings Unlock Hidden Patterns
Featrix Prism’s first step transforms your data into rich embeddings that capture hidden relationships, even across unaligned fields. Forget complex joins and endless cleanup—Prism reveals the structure in your data effortlessly.
2. Build Predictors without Complex Optimization
From recommendations and scoring to predictive analytics, Featrix Prism enables you to train neural functions directly in the embedding space. Get performance without the iterative optimization process that requires AI skills and takes time.
3. Seamless Deployment and Continuous Learning
Host your models with Featrix or in your own environment. Run inference using secure REST APIs.Keep your models up to date effortlessly with continuous learning from new data.
Why Choose Featrix Prism?
Unmatched Speed and Simplicity
With Featrix Prism, say goodbye to traditional AI complexities like data joining, hyperparameter tuning, and model optimization. Build predictors in three intuitive steps, cutting down weeks of effort to mere hours
Flexible and Intuitive Data Fusion
Featrix Prism eliminates the need for expensive data preparation and joins by leveraging vector embeddings. This revolutionary approach finds connections other approaches miss, helping you visualize and act on your data faster.
AI for Every Skill Level
Whether you’re an AI expert or a novice, Featrix Prism provides tools that adapt to your needs. Effortlessly query, sample, and refine your models without advanced coding expertise.
More Than a Model: A Complete AI Solution
Unlike alternatives that require a patchwork of tools, Featrix Prism delivers everything you need in one platform. From embedding-based EDA to deployment-ready APIs, it’s AI simplified.
Why Featrix Prism Outperforms Alternatives
| Feature | Prism | Alternatives |
|---|---|---|
| Ease of Use | No coding required for AI novices | Extensive coding for setup and tuning |
| Data Preparation | Optional, auto-handled | Required: cleaning, joining, etc.g |
| Model Optimization | Integrated, minimal effort | Requires expertise and time |
| Deployment | Seamless, secure API or self-host | Complex, costly |
| All-in-One Solution | Yes | Requires multiple tools |
Go from data to hosting your private AI model in just a few steps
Connect your data
Upload your raw CSV or connect a cloud database. No need to clean, impute, fix or otherwise do deep data prep. Featrix makes it easy to work with multiple tables without joining.
Explore your Data (Optional)
Visualize and explore your data with our contextually rich embeddings. Featrix embeddings capture mutual information relationships in your data, letting you zero in on where the predictive power is.
Build models
Classification, regression, clustering, and recommendation are all a cinch with Featrix. Featrix makes it easy to stand up and test models in your applications.
Serve and Deploy
Simplest way is to let Featrix host your models and use our API. Enterprise customers can export models and use existing model serving infrastructure. Featrix Feeds also provides a way for you to begin accumulating data right away without any additional infrastructure.
98% less code than doing things by hand with other solutions
The below code is a 15 line solution to the "spaceship Titanic" Kaggle competition. Try it out for yourself.
Featrix Solution
import pandas as pd
from sklearn.model_selection import train_test_split
import featrix as ft
full_df = pd.read_csv("kaggle/titanic/train.csv")
train_df, test_df = train_test_split(full_df, test_size=0.2)
client = ft.newclient()
embeddingSpace = client.newEmbeddingSpace() embeddingSpace.fit(train_df)
model = embeddingSpace.newModel()
model.fit(train_df, target="Transported")
X = model.predict(test_df, target= "Transported")
Traditional Approach
import pandas as pd
from sklearn.preprocessing import StandardScaler
def preprocess_data(df):
# Handle missing values
df.fillna(0, inplace=True)
# Scale numerical features
scaler = StandardScaler()
df[['age', 'fare']] = scaler.fit_transform(df[['age', 'fare']])
return df
def engineer_features(df):
# Create title feature
df['title'] = df.name.str.extract(' ([A-Za-z]+)\.')
# Create family size feature
df['family_size'] = df.sibsp + df.parch + 1
# Create is_alone feature
df['is_alone'] = (df.family_size == 1).astype(int)
return df
from sklearn.ensemble import RandomForestClassifier
def train_model(X_train, y_train):
model = RandomForestClassifier(
n_estimators=100,
max_depth=10,
random_state=42
)
model.fit(X_train, y_train)
return model
def predict_survival(model, X_test):
predictions = model.predict(X_test)
proba = model.predict_proba(X_test)
return predictions, proba
from sklearn.metrics import accuracy_score, f1_score
def evaluate_model(y_true, y_pred):
accuracy = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
return accuracy, f1
from sklearn.model_selection import GridSearchCV
def tune_hyperparameters(X, y):
param_grid = {
'n_estimators': [100, 200, 300],
'max_depth': [10, 20, 30]
}
grid_search = GridSearchCV(
RandomForestClassifier(),
param_grid,
cv=5
)
grid_search.fit(X, y)
return grid_search.best_params_
def get_feature_importance(model, features):
importances = model.feature_importances_
indices = np.argsort(importances)[::-1]
for f in range(len(features)):
print("%d. %s (%f)" % (f + 1,
features[indices[f]],
importances[indices[f]]))
from sklearn.model_selection import cross_val_score
def perform_cross_validation(model, X, y):
scores = cross_val_score(
model, X, y, cv=5,
scoring='accuracy'
)
return scores.mean(), scores.std()
from sklearn.ensemble import VotingClassifier
def create_ensemble(X, y):
clf1 = RandomForestClassifier()
clf2 = GradientBoostingClassifier()
clf3 = XGBClassifier()
ensemble = VotingClassifier(
estimators=[
('rf', clf1),
('gb', clf2),
('xgb', clf3)
],
voting='soft'
)
return ensemble
def run_pipeline(train_data, test_data):
# Preprocess data
train_processed = preprocess_data(train_data)
test_processed = preprocess_data(test_data)
# Engineer features
train_featured = engineer_features(train_processed)
test_featured = engineer_features(test_processed)
# Train model
model = train_model(train_featured, train_labels)
# Make predictions
predictions = predict_survival(model, test_featured)
return predictions
Comparison Results
- Featrix reduces code complexity by up to 90%
- Automated feature engineering saves hours of manual work
- Featrix model achieves comparable or better accuracy
- Significantly faster development and iteration cycles