Denis Panjuta’s Post

🎉 Hey everyone! Just finished the AWS Machine Learning course! 🚀 🧠 Learning about machine learning with AWS was eye-opening. This course taught me so much about how AI works and how to use it. 🔍 I got to explore lots of cool tools and methods for machine learning on AWS. Now I understand all the important words and steps. 💡 Getting good at AWS's machine learning stuff means I can do lots of cool stuff with AI. There are so many chances to use it to solve problems and make new things! 👨💻 Can't wait to try out what I've learned on real projects and help make awesome AI stuff happen! #AWS #MachineLearning #AI #DataScience #LearningForever

Excited to share that I'm diving into the world of Machine Learning and Artificial Intelligence through Amazon AWS's "Fundamentals of Machine Learning and Artificial Intelligence" course. 🚀 This course is providing me with a solid foundation in building machine learning models, understanding training data, algorithms, and model evaluation. 📊 The insights gained are incredibly valuable as we continue to see AI and ML transform industries. 💡 #MachineLearning #ArtificialIntelligence #AWS #ContinuousLearning #DataScience

🚀 Brace yourself for a deep dive into the world of #GenerativeAI on AWS! This fresh technical course, spearheaded by AWS AI guru Emily Webber, isn't just a brief overview, but a comprehensive journey through the landscape of foundation models and machine learning. From origins to optimization, from data preparation to deployment, this course has got you covered. 📘 Want to pre-train new foundation models? Need to prepare training datasets on AWS at scale? Ever wondered about different fine-tuning techniques? And how about a sprinkle of reinforcement learning? It's all there! What I admire the most is how this course embodies the real essence of AI, translating theory into practical production. Learning how to launch a new foundation model on Amazon SageMaker with multi-GPU usage is a game-changer. Plus, some bonus content about prompt engineering and Stable Diffusion deep dives is like icing on the cake. 🎂 For those who prefer a good reading session, Emily's book on building and deploying foundation models on AWS would be a perfect companion. No time to hesitate, folks! It's time we all rolled up our sleeves and delved into the advancing world of AI and ML on AWS. Let's dive into the code and sculpt our futures. The link sits right here waiting https://lnkd.in/guhWWQS4 🚀 In the realm of tech, a day spent without learning something new is a day wasted. So, go ahead, take advantage of this course, and share your thoughts below about how it reshapes your understanding of Generative AI. 🤖 #FoundationalModels #AI #AWS #MachineLearning #DataScience #DeepLearning #ReinforcementLearning Keep learning, keep growing, Casey Jones

"Just completed an enriching learning journey on LinkedIn! 🚀 Delved into the intricacies of machine learning and artificial intelligence, exploring everything from fundamental concepts to advanced techniques. From understanding the basics of ML to planning and executing ML projects with Amazon SageMaker, the journey has been immensely rewarding. Special thanks to [Amazon Web Services (AWS)] for the comprehensive curriculum. Excited to apply this knowledge to real-world projects and continue growing in the field of data science and AI! #MachineLearning #AI #DataScience"

Generative AI Foundations on AWS is a new technical deep dive course that gives you the conceptual fundamentals, practical advice, and hands-on guidance to pre-train, fine-tune, and deploy state-of-the-art foundation models on AWS and beyond. https://lnkd.in/dcY52bWD Course outline: 1. Introduction to foundation models 2. Picking the right foundation models 3. Prompt engineering and fine-tuning 4. Pretraining a new foundation model 5. Preparing data and training at scale 6. RL with human feedback 7. Deploying a foundation model YouTube playlist: https://lnkd.in/deiUjAUx

I am happy to announce that I recently finished and enjoyed the course on "Generative AI with Large Language Models" from DeepLearning.AI and Amazon Web Services (AWS). The special thing about this course is that it addresses LLMs within the framework of a GenAI project life cycle, i.e. a methodological approach. Specifically, from defining the scope of use cases that your application should cover, to selecting the model (base model vs. own pretraining), to a variety of options for adapting and investigating the LLM (e.g., prompt engineering, fine-tuning, reinforcement learning with human feedback, evaluation), right up to embedding it in your own infrastructure with the help of external sources and applications (Retrieval Augmented Generation (RAG), Program-aided language models (PAL)). In synergy with practical labs in AWS SageMaker, the course offers a broad view on and therefore an ideal introduction into LLMs. https://lnkd.in/dYNA5Enc #generativeai #llm #deeplearningai #aws #coursera

Hello, LinkedIn Community! 👋 For those who are always learning and pushing the envelope in #DataScience and #MachineLearning, let's dive into something fascinating - Generative AI Foundations on AWS! This article drops a ton of knowledge about the nuances of understanding and effectively deploying Generative AI Foundations for your ML and Data Science projects. 🧠💡 Check out the key points covered - from understanding foundation models and their relationship with generative AI to a deep dive into Stable Diffusion processes. Not just that, there's a bonus - learning how these techniques can be utilised on the mammoth platform that is AWS - it's like an entire 101 course squeezed into a single blog. The methodology for scaling, data preparation, and deployment using Amazon SageMaker are particularly attention-grabbing. And if you're up for some hands-on experience, there's a link to source code on GitHub. Ready to get your hands dirty? 💻 Do check it out! 👇 https://lnkd.in/guhWWQS4 My major takeaways? Scaling laws and reinforcement learning reshaping model performances, and utilising AWS for the optimal environment for scaling and deploying models. Will this profound knowledge impact your future projects? What are your thoughts on Generative AI Foundations on AWS? Let's get the conversation started! #GenerativeAI #AWS #FoundationModels #ReinforcementLearning #AmazonSageMaker Keep learning and growing, Casey Jones. 🌱

"The Generative AI Foundations on AWS" course is designed to cater to a diverse range of audiences, from recent graduates to seasoned professionals. It excels in presenting complex topics in an accessible and easy-to-understand manner. I would like to extend my appreciation to Emily Webber, the AWS Generative AI Worldwide Foundations Lead, for her commendable efforts in developing this informative and well-structured course. This is a comprehensive course that provides conceptual fundamentals, practical advice, and hands-on guidance for pre-training, fine-tuning, and deploying state-of-the-art foundation models on AWS and beyond. The course covers a wide spectrum of progressively complex generative AI techniques, providing a strong foundation for understanding, designing, and applying models for optimal performance. The course begins by recapping foundation models, understanding their origins, functions, and relationships to generative AI, and then teaches about selecting the right foundation model for each use case. The core subject of the course is pre-training new foundation models, covering scaling laws, preparing training datasets at scale on AWS, fine-tuning foundation models, evaluating recent techniques, and running them with scripts and models. The course also delves into reinforcement learning with human feedback, exploring how to maximize foundation model performance. Finally, the course teaches how to apply theory to production by deploying foundation models on Amazon SageMaker, including across multiple GPUs and using top design patterns like retrieval augmented generation and chained dialogue. The course also includes a Stable Diffusion deep dive, prompt engineering best practices, and standing up LangChain. #aws #sagemaker #genai #generativeai #foundationmodels #langchain #machinelearning #finetuning #reinforcementlearning #stablediffusion

Just completed an intensive course on Generative AI – Building Language Models on AWS Training & Certification ! 🤩 🔍 Here’s a glimpse of what I covered: Training and Deploying a Language Model on AWS: 🔸 Addressed challenges in building large language models and utilized distributed training techniques. 🔸 Leveraged Amazon SageMaker for efficient model training and deployment. 🔸 Demonstrated setting up Amazon SageMaker Studio and ingesting model data. 🔸 Explored training large models, including data parallelism with PyTorch Lightning and fine-tuning GPT-2 using SageMaker model parallelism. 🔸 Deployed models on SageMaker using various techniques like DeepSpeed containers for optimal performance. 🔸 Customizing and Deploying a Foundation Model with Amazon SageMaker JumpStart: 🔸 Customized foundation models for generative AI tasks, enhancing output and performance. 🔸 Utilized SageMaker JumpStart to deploy FLAN-T5 models for text generation, incorporating with RAG. A big thank you to Amazon Web Services (AWS) for providing an excellent learning platform 🌐💡 #GenerativeAI #AWS #AIEngineering #ContinuousLearning