#DeepLearningTools

[opensourceais]

Your AI Doesn’t Sleep. Neither Should Your Monitoring.

We’re living in a world run by models from real-time fraud detection to autonomous systems navigating chaos. But what happens after deployment?

What happens when your model starts drifting, glitching, or breaking… quietly?

That’s the question we asked ourselves while building the AI Inference Monitor, a core module of the Aurora Framework by Auto Bot Solutions.

This isn’t just a dashboard. It’s a watchtower.

It sees every input and output. It knows when your model lags. It learns what “normal” looks like and it flags what doesn’t.

Why it matters: You can’t afford to find out two weeks too late that your model’s been hallucinating, misclassifying, or silently underperforming.

That’s why we gave the AI Inference Monitor:

Lightweight Python-based integration

Anomaly scoring and model drift detection

System resource tracking (RAM, CPU, GPU)

Custom alert thresholds

Reproducible logging for full audits

No more guessing. No more “hope it holds.” Just visibility. Control. Insight.

Built for developers, researchers, and engineers who know the job isn’t over when the model trains it’s just beginning.

Explore it here: Aurora On GitHub : AI Inference Monitor https://github.com/AutoBotSolutions/Aurora/blob/Aurora/ai_inference_monitor.py

Aurora Wiki https://autobotsolutions.com/aurora/wiki/doku.php?id=ai_inference_monitor

Get clarity. Get Aurora. Because intelligent systems deserve intelligent oversight.

Sub On YouTube: https://www.youtube.com/@autobotsolutions/videos

[thedevmaster-tdm]

Top Tools and Libraries for Deep Learning in 2024 💻 Greetings and welcome to our in-depth examination of the best deep learning tools and libraries of 2024! No matter your experience level in data science, it's critical to keep up with the most recent tools. We'll guide you through the essential structures and libraries that are influencing deep learning in the future in this video. 🚀 What you will discover: 1. Modern Tools: Learn about the newest and most potent deep learning tools. 2. Framework Comparisons: Identify the advantages and disadvantages of well-known libraries such as PyTorch, TensorFlow, and others. 3. Use Cases: Examine the practical applications of these tools. 4. Expert Advice: Discover how to get the most out of these technologies by consulting the industry experts. 🔔 To keep up with our most recent videos on data science and deep learning, subscribe and click the bell button! 👍 Please give this video a thumbs up and forward it to your friends who are interested in deep learning if you find it useful.

#DeepLearning #AI #DataScience #Tech2024 #MachineLearning #artificialintelligence #deeplearningtools #2024 #technologies #technology #DataScientist

[techvandaag]

Microsoft Azure ondersteunt voortaan Nvidia GPU Cloud voor deep learning

Microsoft Azure-klanten kunnen nu de GPU Cloud van Nvidia gebruiken voor de training en inferentie van deeplearningmodellen. Dat meldt het bedrijf in een blogpost. De Nvidia GPU Cloud biedt softwarecontainers voor het versnellen van high performance computing (HPC) en deep learning voor onderzoekers en ontwikkelaars. Het Nvidia-containerschema ondersteunt populaire deeplearningtools zoals TensorFlow, Microsoft Cognitive Toolkit […] http://dlvr.it/QhZbDQ

[aboutict]

Microsoft Azure ondersteunt voortaan Nvidia GPU Cloud voor deep learning

Microsoft Azure-klanten kunnen nu de GPU Cloud van Nvidia gebruiken voor de training en inferentie van deeplearningmodellen. Dat meldt het bedrijf in een blogpost. De Nvidia GPU Cloud biedt softwarecontainers voor het versnellen van high performance computing (HPC) en deep learning voor onderzoekers en ontwikkelaars. Het Nvidia-containerschema ondersteunt populaire deeplearningtools zoals TensorFlow, Microsoft Cognitive Toolkit […] http://dlvr.it/QhYr9S