Machine learning is rapidly growing in popularity, especially in areas such as natural language processing, computer vision, and predictive analytics. It’s proving useful at a number of different use cases allowing organizations to make better insights from their data, better service their customers and employees, better understand their market and competitors, and reduce costs and …
Machine learning has the potential to unlock insights, automate processes, and drive revenue growth. However, with so many different machine learning tools available, it can be overwhelming for those looking to take advantage of machine learning to know where to start. In this post, we’ll explore 10 considerations for ML tool selection. Purpose: Make sure …
Machine learning (ML) projects can be complex and time-consuming, and it’s easy to make mistakes along the way. In this article, we’ll go over 10 common mistakes people make with ML projects and how to avoid them. Not clearly defining the problem for ML Projects Before starting any ML project (really any project for that …
10 common mistakes people make with ML projects and how to avoid them Read More »
While much of the attention up until now has been focused on the development of machine learning models, as the industry moves from innovators and early adopters to the early majority, new customers will be more concerned about consuming third party models and building on top of existing best practices rather than building their own …
AI Today Podcast #137: ML Ops in Practice: Interview with Seth Clark, Modzy Read More »
Machine Learning Model Operationalization Management, referred to as “MLOps”, is focused on the lifecycle of model development and usage, machine learning model operationalization, and deployment. Cognilytica recently published a report on this topic that examines the ML Operationalization Management market, provides visibility into emerging best practices in the MLOps space, identifies open source and commercial …
AI Today Podcast #136: ML Model Management and Operations (“ML Ops”) Read More »
As companies move from building models to buying models the focus shifts from tooling and platforms focused solely on model development to tools and platforms focused on the overall usage, consumption, and management of models. Machine Learning Model Operationalization Management, referred to as “MLOps”, is focused on the lifecycle of model development and usage, machine …
While the Robotic Process Automation (RPA) market is heating up, there are many that say that RPA is not AI per se, but rather a gateway on the path to cognitive automation. Automation Anywhere certainly agrees with this perspective, and in this interview, Manish Rai, VP of Product Marketing at Automation Anywhere shares insights into …
In this episode of the AI Today podcast we interview Colin Angle, Chairman, CEO and Founder of iRobot. He shares with us his thoughts on why autonomy is not intelligence, some exciting use cases and examples of robots he’s seen, and how customers have started to become more comfortable with robots inside their homes over …
Facial recognition technology is still an area in AI that brings about mixed emotions. While there can be positive use cases for the technology such as enhanced convenience like checking into flights with just your face or helping law enforcement find missing persons or criminals, there are also many potential misuses for the technology as …
AI Today Podcast #122: The Good, Bad, and Ugly of Facial Recognition Read More »
AI in impacting just about every industry and e-commerce, retail, and supply chain management is no exception. In this AI Today podcast we interview Professor Dr. Michael Feindt, Strategic Advisor at JDA and Founder of Blue Yonder. He shares his insights into use cases for AI in retail/eCommerce space and beyond, how companies that are …
AI Today Podcast #119: Interview with Michael Feindt, JDA Read More »
Login to register for events. Don’t have an account? Just register for an event and an account will be created for you!