ETEducation’s Post

🚀 Want to give a Deep Learning shout-out(see what I did there) to NVIDIA for a journey that went beyond expectations! 🎓 Just wrapped up the AI in the Data Center course on Coursera that, at least judging by the title, I figured would just be a cursory exploration into their AI ecosystem but proved to be an in-depth examination of Nvidia's Tensor Core architecture, the CUDA-X AI libraries toolkit, and the nuanced Multi-Instance GPU (MIG).🛠️ From a Cyber perspective, the course seamlessly transitioned beyond hardware, providing comprehensive insights into the Data Center GPU Manager (DCGM) and the practical application of CUDA-X libraries in diverse AI workflows and even touches a tad on Morpheus. But for those that want to learn more about that, I highly recommend this FREE course from Nvidia: https://lnkd.in/g8uxyrBu 🚨Notably, I most enjoyed learning about the implications of Data Gravity* concerning how closely your AI resides to where your Devs are trying to "shift left". Developers find themselves spending more time carefully grooming each training run since failure can be costly. Not having the resource-intensive AI training computations occur close to where the data resides can greatly slow iteration and stifle innovation from a DevOps perspective. I like that Nvidia's educational journey isn't just about absorbing knowledge; it's about being part of a dynamic community of AI innovators. Their Jetson AI Certification may be one worth looking at if you're into hands-on projects.🤝 https://lnkd.in/gDurKJET All in all, a great experience, and I will definitely be back for more! 🙌💡 *Data gravity is the tendency for applications and data processing activities to gravitate towards where the data resides. #Nvidia #AI #deeplearning #ML #AIinDataCenter #ProfessionalDevelopment #DevOps #innovators #morpheus #digitalfingerprinting #cyberdefense 🚀🔍

🎓 Proud to announce that I have successfully completed the "Introduction to AI in the Data Center" course by NVIDIA on Coursera! 🌟 Throughout this course, I gained a comprehensive understanding of AI, Machine Learning (ML), and Deep Learning (DL) concepts and their real-world applications in various industries. The course delved into the power of GPU computing, unlocking the potential for accelerating AI workloads in data centers. The course covered a wide range of topics, including the introduction to AI and its applications across various industries. I became familiar with the deep learning frameworks and how training and inference happen in a deep learning workflow. I also learned about the history and architecture of GPUs and its fundamental differences from CPU, the NVIDIA GPU software ecosystem, and essential considerations when deploying AI workloads in different server environments, whether on-premises, in the cloud, hybrid models, or multi-cloud environments. In the subsequent modules, I explored rack-level considerations for deploying AI clusters, such as storage and networking requirements, and gained insights into NVIDIA's reference architectures for designing efficient AI systems. Additionally, I studied data center-level considerations, focusing on infrastructure provisioning, workload management, cluster orchestration, job scheduling, cluster monitoring, and power and cooling considerations for data center deployments. The course also introduced me to NVIDIA's colocation program, offering valuable information about AI infrastructure provided by NVIDIA partners. Completing the course allowed me to explore various deployment options for AI workloads. The knowledge gained has equipped me to efficiently deploy AI clusters and manage them effectively in data center environments. I am grateful to NVIDIA and Coursera for providing this enriching learning experience, equipping me with essential knowledge and skills in AI data center management. #AI #DataCenter #GPU #CourseraCertification #ContinuousLearning #NVIDIA #DeepLearning #MachineLearning

Just completed & passed the "Introduction to AI in the Data Center" course by NVIDIA & Coursera and it was mind-blowing! 🚀 This nicely curated course has given me a deep insight into the world of AI and Deep Learning and how the growing needs for high level computing would be addressed in Data Centers with #spaceplanning #poweroptimization #coolingsolutions #heatexchangers As technology progresses, it is crucial to stay updated and equipped with knowledge in this field. #AIinDataCenter #DeepLearning #TechEnthusiast #nvidia #coursera #datacenter #keeplearning #heatexchanger #liquidcooling

Today I earned my "Build classical machine learning models with supervised learning" badge from Microsoft Learn. Just a stepping stone in my journey in artificial intelligence and machine learning at IIT Madras BS in Data Science Programme.

I’m pleased to share my progress in Data Science and Machine Learning through the Post Graduate course with IIT Roorkee and CloudxLab. Here’s a link to my CloudxLab profile, showcasing some of the projects I’ve completed. Looking forward to connecting with others in the field and exploring new opportunities. #DataScience #MachineLearning #ArtificialIntelligence

🚀 Transforming Data Science with NVIDIA's RAPIDS and cuDF: A 150x Faster Pandas If you're in Data Science or AI, chances are you've crossed paths with the Pandas library. A staple in data manipulation, Pandas stands tall alongside TensorFlow, PyTorch, Numpy, and Scikit. Its prowess in handling tabular data is undisputed. But, like all giants, Pandas has its Achilles' heel. 🐼 The challenge? Handling massive datasets, particularly those in the terabyte range. Pandas operates in-memory, loading entire datasets into RAM. This design, while efficient for smaller datasets, hits a wall with larger volumes, often leading to performance issues or outright failure. 🔍 In this blog post, we delve deep into: 1. The intricacies of Pandas' memory usage. 2. Past efforts to enhance Pandas' capabilities. 3. The game-changing role of NVIDIA's RAPIDS and cuDF. The highlight? NVIDIA's cuDF in the RAPIDS suite. This groundbreaking technology not only accelerates Pandas but shatters previous barriers, enabling the processing of terabytes of data with unprecedented efficiency - boasting a jaw-dropping 150x speed increase! 🌪️ 🔗 https://lnkd.in/epynuA9h #DataScience #Pandas #NVIDIA #RAPIDS #cuDF #BigData #AI #artificialintelligence #artificialgeneralintelligence #dataanalysis #datascience #datascientist #datascientists #ml #mlengineer

Will fine-tuning of Large Language Models (LLM) ever be available to everyone? Imagine two of your top Data Scientists come up with a great idea for fine-tuning an LLM, so they come and ask you for…. 100 000 GPUs for two weeks, please. I believe your answer to that question is obvious? Now they come back a few days later. “Ok we have changed approach. Is it ok to ask for 2.7 GPU for two weeks?“. Maybe you’d be curious enough to ask them what changed in their approach...? The answer: LoRA, a revolutionising algorithm that tunes only a tiny fraction of the parameters with just as good results as tuning the whole LLM. The point of the story is that LoRA makes LLM fine-tuning possible where it previously wasn’t. And this leads to why Cloudera's AMPs are useful for Data Scientists: Recently presenting to a crowd of over 130 or so DSs and DEs, I asked “who has heard about LoRA?“. Only four hands went up. This is not something everyone knows about, but more of frontier science. Cloudera's Applied Machine learning Prototypes - AMPs, will help your people stay in the know and play with the latest&greatest as it comes out of the hands of the scientists. Check them out here: https://lnkd.in/dT-gUm8b. In Cloudera Machine Learning environment, you get the joy of using it and running the code as well.

Kudos to Mary-Jo Diepeveen for publishing our latest Learn module on the Transformer architecture and large language models in Azure Machine Learning. Data Scientists should check it out at https://lnkd.in/gnx6atHF.