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News

DEV Community
dev.to > lakshmankrish77 > your-ml-model-died-in-production-heres-why-400p

Your ML Model Died in Production. Here's Why.

1+ hour, 13+ min ago   (310+ words) Did your ML model look amazing in your notebook but tank in the real world? Good. Let's talk about the nasty surprises that trip up model deployments and why that "Train & Forget" approach is bleeding companies dry. It's a story…...

DEV Community
dev.to > cyprus09 > is-speculative-decodings-speedup-a-hardware-problem-or-a-model-problem-7k6

Is Speculative Decoding's Speedup a Hardware Problem or a Model Problem?

6+ hour, 14+ min ago   (279+ words) The first full gamma sweep, on an open-ended, opinion-style prompt, came back like this: Slower across the board. The accept/reject math had already been verified correct, with stable, repeatable acceptance rates and no correctness bugs, so this wasn't a…...

NVIDIA Technical Blog
developer.nvidia.com > blog

ModelExpress: Distributing Model Artifacts at the Speed of Light

1+ day, 7+ hour ago   (434+ words) Every byte moved has a cost. As model checkpoints grow to hundreds of gigabytes or even a terabyte, that cost adds up quickly. To make things even worse…...

EdexLive
edexlive.com > news > karnataka-looks-to-deepen-ai-ecosystem-with-industry-partnership-focus-on-youth-and-rural-education

Karnataka boosts AI ecosystem, focuses on youth and rural education

1+ day, 16+ hour ago   (408+ words) Bengaluru: Karnataka Chief Minister DK Shivakumar on Friday said the government aims to take artificial intelligence (AI) education to rural students and build long-term industry partnerships to strengthen the state's AI ecosystem, while industry leaders backed the state's vision to…...

bookstoread.ai
bookstoread.ai > guides > ai-engineers > aspiring-ai-engineers

Best Books for Aspiring AI Engineers

2+ day, 8+ hour ago   (513+ words) Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville and Designing Machine Learning Systems by Chip Huyen move you from model curiosity to engineering judgment. One thread: learn the theory, then operationalize it. Ian Goodfellow, Yoshua Bengio, Aaron Courville You…...

Towards Data Science
towardsdatascience.com > lessons-learned-after-8-5-years-of-ml

Lessons Learned After 8.5 Years of ML

2+ day, 15+ hour ago   (760+ words) However, I think that there are still some lessons that are applicable regardless of the field one actually is doing machine learning research or machine learning practices in. Frequently, I take the opportunity of another half year of progress in…...

Deeploy
docs.deeploy.ai > docs > deployment > test-a-deployment

Test a Deployment | Embed governance in your AI deployments with Deeploy

2+ day, 23+ hour ago   (155+ words) After creating a Deployment, you can easily test it on the Test page. If you included the exampleInput with a json value as part of the metadata.json in your repository, you can click Use example input. Alternatively, you can…...

Medium
medium.com > @learncalibreos > training-serving-skew-the-silent-bug-that-kills-production-ml-models-b4d7baa9a646

Training-Serving Skew: The Silent Bug That Kills Production ML Models

2+ day, 12+ hour ago   (32+ words) The most common production ML failure does not show up in error logs. A model passes offline evaluation, deploys cleanly, and …...

NEWS BBVA
bbva.com > en > innovation > mlops-transformation-at-bbva-from-data-platform-to-scalable-ml

MLOps transformation at BBVA: from data platform to scalable ML

2+ day, 23+ hour ago   (666+ words) BBVA is continuing to scale its Machine Learning capabilities with a new technology architecture integrated into ADA, the bank’s global data and AI platform based on AWS technologies. In recent years, the growth in the number of AI and ML…...

Medium
medium.com > @rudratyagi1135 > building-machine-learning-systems-taught-me-there-are-no-perfect-solutions-14f927be9ff0

Building Machine Learning Systems Taught Me There Are No Perfect Solutions

2+ day, 16+ hour ago   (634+ words) Introduction One of the biggest misconceptions I had when I started learning machine learning was believing that every …...