machine

In: Artificial Intelligence

This post is co-written with Zhanghao Wu, co-creator of SkyPilot. The rapid advancement of generative AI and foundation models (FMs) has significantly increased computational resource requirements for machine learning (ML) workloads. Modern ML pipelines require efficient systems for distributing workloads across accelerated compute resources, while making sure developer productivity remainsContinue Reading

In: Artificial Intelligence

This is a joint post co-authored with Harsh Vardhan, Global Head, Digital Innovation Hub, Apollo Tyres Ltd. Apollo Tyres, headquartered in Gurgaon, India, is a prominent international tire manufacturer with production facilities in India and Europe. The company advertises its products under its two global brands: Apollo and Vredestein, andContinue Reading

In: Artificial Intelligence

This post is co-written with Qing Chen and Mark Sinclair from Radial. Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. With over 30 years of industry expertise, Radial tailors its services and solutions to align strategically withContinue Reading

In: Technology

Researchers at Rice University have developed a new machine learning (ML) algorithm that excels at interpreting the “light signatures,” or optical spectra, of molecules, materials and disease biomarkers, potentially enabling faster and more precise medical diagnoses and sample analysis. “Imagine being able to detect early signs of diseases like Alzheimer’sContinue Reading

In: Artificial Intelligence

Headquartered in São Paulo, Brazil, iFood is a national private company and the leader in food-tech in Latin America, processing millions of orders monthly. iFood has stood out for its strategy of incorporating cutting-edge technology into its operations. With the support of AWS, iFood has developed a robust machine learningContinue Reading

In: Artificial Intelligence

In the modern, cloud-centric business landscape, data is often scattered across numerous clouds and on-site systems. This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This post presents an architectural approach to extract data from different cloud environments, such as GoogleContinue Reading