Knowledge graphs are network graphs that link related concepts and properties together to create a form of inferencing engine, with knowledge engineering being the programming aspect of graph usage. Explore how knowledge graphs are created and queried, how they are used as part of a broader form of enterprise metadata management, and how they tie into ML and the IoT.
Artificial intelligence (AI) and machine learning (ML) have received widespread interest in recent years due to their potential to set new paradigms in healthcare delivery. It is being said that machine learning will transform many aspects of healthcare delivery, and radiology & pathology are among the specialties set to be among the first to take advantage of this technology.
In September, Kaggle released its annual survey for the state of data science and machine learning.
This list is based on LinkedIn. Criteria for selection include the number of followers (above 50k), the relevancy and contributions to the field, relevant education… Read More »Machine Learning Superstars: The Top 30 Influencers To Follow in 2023
With the release of SAS Container Runtime (SCR), organizations can execute models and decisions outside of SAS using standard technologies. Containerized deployments are lightweight to… Read More »DSC Webinar Series – Best Practices for Adopting Containers within your MLOps Process.mp4
The issue is not just the actual multiplication but the fastest method to perform the multiplication. The speeding up of matrix multiplication calculations has a high impact because matrix multiplication is a part of many applications – especially in deep learning and image processing.
Use cases for ML are seemingly infinite, from automatic responses to queries and automated stock trading, to recommendation engines and customer experience enhancements
This 30 minutes video features my interview about the upcoming course “Intuitive Machine Learning”, based on my new book with the same title. Hosted by… Read More »Video: Introduction to Machine Learning
The aphorism acknowledges that models of our knowledge always fall short of the complexities of reality but can still be useful nonetheless. With this model background, let us delve into this article focusing on specific technical debt in Machine Learning System development.
In the first part here, I discussed missing, outdated and unobserved data, data that is costly to produce, as well as dirty, unbalanced and unstructured… Read More »15 Data Issues and How to Fix Them – Second Part
This article is intended to users relying on machine learning solutions offered by third party vendors. It applies to platforms, dashboards, traditional software, or even external pieces of code that are too time consuming to modify. One of the goals is to turn such systems into explainable AI.
