Comprehensive documentation for integrating machine learning models with Elené's open-source frameworks. Includes code examples and best practices.
This guide shows you how to integrate trained machine learning models (TensorFlow, PyTorch, ONnx) into the Elené platform. We'll use our NeuroForge framework and provide optimized deployment pipelines for production-grade applications.
pip install elene-ml==0.4.1
elene-ml init --model-directory ./models
The above commands will setup our ML integration tools and create necessary infrastructure. This will also install optimized ONNX runtime dependencies.
import onnx
from tensorflow.keras.models import load_model
# Load Keras model
model = load_model('my_model.h5')
# Convert to ONNX format
onnx_model = convert_keras(model, 'my_model.onnx')
# Optimize for Elené deployment
optimize_for_inference(onnx_model)
Convert your trained model to ONNX format and optimize using Elené's tools for maximum performance.
elene-ml deploy \
--model-path ./models/mnist.onnx \
--inference-engine onnxruntime \
--replicas 3 \
--target-platform edge-cluster
Use our deployment command-line interface to push your model to the target platform. This will automatically scale and optimize based on your infrastructure specs.
After deployment, our system automatically monitors key metrics and provides real-time optimization suggestions.
Apply dynamic quantization to reduce model size by up to 78% with minimal accuracy loss.
Turn on secure inference pipelines with built-in adversarial attack protection and input sanitization.
Configure auto-scaling policies that automatically adjust resources based on incoming request patterns.
Use our built-in A/B testing framework to compare different models and select optimal performer automatically.
How do I handle model drift in production environments? Any best practices for continuous training integration?
Check the NeuroForge documentation, but I recommend using our built-in model retraining scheduler that auto-fetches new data from our data lakes.
Join our community forum to get help with ML model integration and other technical questions.
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