Graph-Enhanced Deep Learning

Turns what others say is impossible with only machine learning into a reality.
Achieve significant real-world impact with the most advanced AI techniques available.
Transform your organization with state-of-the-art results that blow away the competition.

Graph-Enhanced Deep Learning Benefits

illustration depicts graph structure, distance and context by showing three separate graphs the first with a shape highlighted, the second with a dashed line following a path for distance and the third multiple styles of dotted and dashed lines to show context

Structure, Distance & Context

Incorporate structure, distance, and context from your graph into neural network training to unlock your data’s most powerful predictive attributes. Structure helps you build features based on important patterns in the graph. Distance introduces a quantifiable attribute useful in characterizing time, sequence, and flow features. Context supports a variety of nuanced features focused on the way your data elements relate to each other. Take the data you already have and maximize its value by developing neural network-based models with real-world awareness.

illustration depicting developer friendly by showing a female developer at a laptop using a simple graph and text interface with a male developer pointing at it and discussing it with her

Developer & Data Scientist Friendly

Can you imagine a better playground? Graph-enhanced and GPU training together in one fully automated and simple-to-use package keeps your developers and data scientists from becoming operators. Accelerate the path to production with an automated pipeline for training, testing, registering, and deploying models. The graph provides a highly-predictive set of new attributes to incorporate during feature engineering.

GPU Accelerated

Get your model to production faster by speeding through training in minutes that used to take days. Improve your AI’s performance with highly predictive graph features. Reduce the training time while incorporating many more graph features and attributes using GPUs instead of CPUs during model development. Empower your team with state-of-the-art Graph + AI on a fully automated GPU architecture. GraphGrid handles the complexity of training on GPU so your team remains focused on creative solution approaches, quality, performance, and impact.

This illustration depicts graph-enhanced deep learning with a graph of nodes and edges transitioning to a vector

Transformative

Get results where non-graph approaches to AI fail. No more struggling with models because your “data lake is too messy” or the “data is not good enough” or “there isn’t enough data.” Make it quick and easy to introduce graph characteristics into AI model training data so your model outperforms those not enhanced with graph characteristics. And, you can explain why it improved by describing the graph features.

Graph-Enhanced Deep Learning Role in GraphGrid Graph + AI Platform

Our Graph-Enhanced Deep Learning gives your team simple APIs and SDKs that accelerate development, training, testing, and deployment. Augment the rest of your training data with graph data and characteristics to enhance your neural network models.

State-of-the-Art
Keep your team focused on impact. GraphGrid provides the fastest path to get models to production using the most advanced techniques for building solutions with reliable AI outputs brought together in an easy to use, fully automated GPU + Graph + AI pipeline.
Accelerating
Feel confident moving forward with advanced AI techniques. GraphGrid handles the complexities for you so you can build with APIs and SDKs in languages and technologies you already know and love.
Keep Production Current
Rest easy knowing your models in production retrain automatically and deploy consistently as your knowledge changes over time so your graph-enhanced models always perform their best.