The Future of Machine Learning: Automatic Graph-based ML
By Susan Hu
Artificial Intelligence (AI) has made rapid advancements in recent years, especially as big data becomes more available and computing capability improves tremendously.
Machine learning (ML) is an area of AI where machines are trained by algorithms and autonomously, continuously learn from data to improve their performance in problem solving. The machine learning market is expected to grow from to 8.81 Billion USD by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%.
What is the future of AI, and specifically what role does ML play? Today we are going to explore Automatic Graph-Based Machine Learning.
What is Automatic Graph-Based Machine Learning?
Automatic Graph-Based Machine Learning is an Automated Model Optimizer System (AMOS) combined with Graph Computing.
We've explored what Graph Computing is and why it's going to be the next wave in AI in a previous post. In short, Graph Computing mimics and exceeds full human brain functions by the way it stores and analyzes data. It gathers, stores and processes information in a way that considers the complex connections and relationships between each piece. And therefore, it's able to solve complex real-life problems in context with true intelligence.
Now let's take a look at what is an AMOS, Automated Model Optimizer System. As straightforward as the name sounds, AMOS is a system that automatically builds, optimizes, deploys and manages models.
When building models, AMOS automatically and incrementally increases the number of clusters, estimates the quality of identified clusters, and tests models for quality control. As a result, models built from AMOS are more reliable and accurate.
Utilizing AMOS is an essential step in achieving future AI and ML for problem solving. As AMOS makes it possible for machines to learn and trains itself to build, analyze and optimize complex models based on the input data, all in an autonomous way.
Graphen AMOS workflow
Graphen's AI Ardi – built upon the Automatic Graph-Based Machine Learning
Recognizing the gap between data pattern analysis and ML, Graphen creates our Ardi AI platform that perfectly integrates data and ML pipeline to solve complex business and societal problems.
Utilizing Graph Computing, Ardi defines patterns needed for data analysis from the very beginning of the process and identifies hidden relationships and correlations. Its native Graph database supports property graphs and key-value storage with dynamic schema and bulk ingestion. Large sets of graph analytics for BFS, egonet, connected components, cycles, community detection, PageRank, clustering, centrality, etc., makes it possible to discover previously undiscoverable relationships by integrating internal enterprise data and domain knowledge.
Combined with an AMOS, Ardi's analytic pipeline includes asynchronous scheduling, management and logging systems.
Built upon advanced AI technology, Ardi is the future of AI that mimics full brain functionality including perception, comprehension and reasoning. It is enterprise ready for companies and organizations with large quantity of data processing and complicated problem-solving needs. It has automatically distributed exploration of machine learning, deep learning and Bayesian network, with built-in explainability.
To achieve the future of AI and ML, it is essential to streamline complex data analysis and autonomous model optimization.
Combining the power of Graph Computing and an advanced Automated Model Optimizer System, we believe Automatic Graph-Based ML will help shape the future of AI and change the landscapes across industries.
If you want to learn more about AI, Graphen's AMOS, and how it can help you transform and upgrade your business, please reach out to me at email@example.com.