What's needed is a more intelligent and holistic class of process automation that weaves together a combination of technology capabilities including RPA, machine learning, natural language processing, optical character reader, and other flavors of AI.
Nearly every organization today is exploring ways to achieve competitive differentiation through data science and machine learning. But the success of an investment in a data science or machine learning platform depends on how well it addresses the needs and concerns of data scientists, the IT team, and executive leadership.
IoT, machine learning, artificial intelligence, 5G, augmented reality, and virtual reality all benefit from increased edge compute power.
Microsoft found that pairing machine learning models with security experts significantly improves the identification and classification of security bugs.
As the volumes of data used in businesses grows, getting data suitably annotated and tagged to train machine learning models is an enormous challenge.
AI and machine learning are dependent on high-value data, which means IT departments need to have the proper visibility into what is happening on their networks.
Artificial intelligence and machine learning could revolutionize the way that businesses do CRO.
Data annotation takes time. And for in-house teams, labeling data can be the proverbial bottleneck, limiting a company's ability to quickly train and validate machine learning models.
Golang is now becoming the mainstream programming language for machine learning and AI with millions of users worldwide.
With AutoAI, businesses can build and deploy a machine learning model with sophisticated training features and no coding.