Abstract The rapid development of AI-based products and their underlying models has led to constant innovation in deep learning frameworks. Google has been pioneering machine learning usage across dozens of products. Maintaining the multitude of model source codes in different ML frameworks and versions is a significant challenge. So far the maintenance and migration work

The Hariri Institute for Computing at Boston University has named Boston University alumni Mona Jalal as the new lead of its Data Science Mentoring Circles Program (DSMC), effective September 2026. Jalal was selected for her passion for mentoring and experience in bridging cutting-edge AI research and real-world engineering. As a Computer Vision Research Engineer III

Generative AI inference is uniquely hard: models are tens to hundreds of gigabytes, latency requirements are measured in tokens per second, cold starts can span multiple minutes as containers and weights transfer, GPU capacity is constrained, and traditional monitoring tools expose none of the token-level signals that matter in production. Amazon SageMaker AI offers customers

September 18, 2026 e-Book 2026 is pretty clearly the year of Physical AI, and there is much that engineers and developers need to know, especially in robotics and automation, now that Physical AI is the method of the moment. It’s fine if we all agree to use the new term, but to me, that means

JOURNAL ARTICLE Published In: Journal of Intelligent & Fuzzy Systems, 2024, v. 47. P. 245 1 of 3 Database: Academic Search Ultimate 2 of 3 Authored By: Lamani, Dharmanna; Shanthi, T.S.; Kirubakaran, M.K.; Roopa, R. 3 of 3 Abstract The article focuses on the development and evaluation of SSWSO LeNet, an optimized deep learning model

Open-weight models are changing the economics of building and deploying AI at scale. Rapid gains in intelligence and efficiency mean companies can match each workload with the right balance of capability, speed, and cost. AWS is building for a future in which organizations can adopt open-weight innovation with the reliability and security required for production.

image:  The development of an ML model involving the inclusion of stroke-related biological processes. view more  Credit: Targetome A research team has developed an artificial intelligence-integrated framework that combines pathway-based machine learning, transcriptomic prediction, and pharmacophore analysis to rapidly identify and optimize therapeutic candidates for ischemic stroke. The system prioritized cannabidiol (CBD) as a repurposable

Deploying a Hugging Face model to production means making a dozen decisions: choosing the right serving container for the model’s architecture, confirming the current image tag for your AWS Region, and matching an instance type to the model’s memory footprint. Beyond infrastructure, you must wire autoscaling so you don’t burn GPU hours on an idle