Key Takeaways -   To understand data science, one needs a lot of technical expertise along with business understanding. Generative AI, MLOps, and clou ...

NVIDIA recently introduced a new vision language model (VLM) that enables AI systems to identify and localize objects. Apple ...

Overview:  Compares the leading backend frameworks used by developers in 2026.Explains where FastAPI, Django, NestJS, Express.js, Spring Boot, Laravel, Go, ...

Chandigarh University Uttar Pradesh, positioned as India's first AI-augmented multidisciplinary university, has introduced 17 new academic programmes from the 2026 academic session onward, including 5 ...

The programme aims to equip professionals build production-grade AI capabilities across machine learning, deep learning, MLOps, cybersecurity, Generative AI, LLMs and agentic AI systems.,, Times Now ...

IITM Pravartak and TimesPro have announced Batch 03 of the Advanced Certificate in Applied Artificial Intelligence & Deep Learning, a seven-month online programme covering areas such as TensorFlow, ...

IITM Pravartak Announces Batch 03 of Applied Artificial Intelligence and Deep Learning Programme to Build Enterprise-Ready AI Talent ...

Roorkee: The Indian Institute of Technology Roorkee has opened admissions for the 11th batch of its Post Graduate Certificate in Data Science, Machine Learning & Generative AI, an advanced ...

The Federal Government has announced plans to expand the Deep Blue Project to vulnerable coastal and maritime corridors across the country, including the Bakassi axis, as part of efforts to further ...

Deep Fission has started drilling for its first-of-a-kind "gravity reactor" project in Kansas, which places a 15-megawatt reactor 6,000 feet underground to cut operational costs by up to 80%. The ...

The Brookfield Compressor Station, photographed on January 12, 2025, is the subject of local opposition to a proposal to increase the capacity of the Iroquois Gas Transmission System. Credit: Shahrzad ...

Most ML projects fail to reach production. Five recurring pitfalls drive failures in ML projects: choosing the wrong problem, data quality/labeling issues, the model-to-product gap, offline-online ...