Technical Blogs

Glance through the most cited articles in the Reliability Engineering & System Safety journal [1], you may notice the emergence of data-driven techniques such as function approximation, optimization, search, and planning – a collection of techniques broadly studied under the discipline of artificial intelligence.

With the wide adoption of technologies such as cloud computing, big data, industrial IoT, and artificial intelligence, asset-heavy industries have become increasingly interested in developing digital twins.

Time series — collections of data points indexed and ordered by time — are extensively used in modeling a variety of problems and detecting outliers or anomalies in time series is one of the most impactful applications of machine learning.

Reliability engineering as a discipline traces its origins to the 19th-century industrial revolution. Industrial pioneers such as George Westinghouse and Thomas Edison embraced technology and illuminated the world with novel innovations

The C3 AI Platform offers our customers extensive and powerful capabilities to build and operate enterprise AI applications, but there can be a steep learning curve for new users during the onboarding process.

Artificial Intelligence (AI) has become a common element in enterprise-scale digital transformation efforts as it provides significant benefits to surfacing, monitoring, and predicting business insights across many industries

The C3 AI applications provide businesses with insights that could save them time, money, and resources by optimizing inventory, providing maintenance to failing machinery, and improving energy management

In this blog post, Josh and Amir will go through the steps for building a simple diagram parsing model and demonstrate how this model can be made production-ready using the C3 AI Application Platform.

Omid shares the journey to better and simpler testing at C3 AI.