Tutorials Dojo’s Post

My latest blog post. Amazon SageMaker is a great tool for developing and managing Machine Learning applications in AWS. However, it’s very easy to spend a significant amount of money when using SageMaker. This article covers strategies to avoid incurring unnecessarily high cost in Amazon SageMaker AI. https://lnkd.in/gj6MQ9y9

Amazon SageMaker Clarify now supports foundation model evaluations https://ift.tt/SnFXHDO Foundation model evaluations with SageMaker Clarify is now generally available. This capability helps data scientists and machine learning engineers evaluate, compare, and select foundation models based on a variety of criteria across different tasks within minutes. via Recent Announcements https://ift.tt/PoLO1CE April 25, 2024 at 07:02PM #aws #cloudcomputing

Raise your hand if you're not experimenting with LLMs 🙋! C'mon, be honest ... OK, I thought so - pretty much everyone does SOMETHING. Some folks create their own prompt repos, others import their knowledge bases (personal notes, documents 📃, etc.), and few experiment with a multi-agent approach or build voice solutions on top of existing models. That's all very cool & in many cases, you get promising effects very quickly 🚀 (that only encourages you to try even more). But of course, sooner or later, you get stuck 💢 - your continuous efforts don't improve the quality of responses. For instance, doing highly effective RAG is apparently not as easy as uploading a bunch of unprocessed, unlabelled files 😰 ... That's the moment when you feel you could use a peek into the secrets of the kitchen of someone who's been doing the same thing 🕵️, but maybe is already few steps ahead ... Easier said than done - Gen AI & LLMs are a very fresh domain, with many patterns and reference architectures still far from solidifying. That's why every piece of wisdom is so appreciated in this space 🙏. And today, I'd like to compliment "Advanced RAG patterns on Amazon SageMaker" (https://lnkd.in/dqdAK-gC) - a brand new blog post created by some good folks in Amazon Web Services (AWS), that sheds some more light on how to do RAG beyond "hello-world" examples (with LangChain 🦜🔗, parent document retrievers and contextual compression). Good stuff, not only informative but also pretty much immediately applicable 💪 - highly recommended. #genai #llm #rag #aws #cloud #sagemaker #langchain

I'm thrilled to share a new blog post I co-authored with the AWS team, where we dive into how Monks quadrupled processing speed for real-time diffusion AI image generation using Amazon SageMaker and AWS Inferentia2. In this post, we explore how we tackled challenges like scalability and cost management, ultimately achieving a 60% reduction in cost per image while maintaining low latency. Whether you're dealing with high-demand AI workloads or interested in AWS Inferentia2's capabilities, this post offers valuable insights. Check it out and let me know what you think! https://lnkd.in/g-B9gXFU #AWS #MachineLearning #GenerativeAI #Inferentia2 #SageMaker #TechInnovation #Monks

Amazon Bedrock now supports compressed embeddings from Cohere Embed! 💡 Cohere Embed, a top text embedding model, is widely known for enhancing RAG & semantic search systems. The int8 and binary compressed embeddings now available empower developers and businesses to create more efficient #generativeAI applications without sacrificing performance. Explore more at: https://go.aws/4eBniXm #AWS ☁️

Llama 3.3 70B represents a significant breakthrough in model efficiency and performance optimization. This new model delivers output quality comparable to Llama 3.1 405B while requiring only a fraction of the computational resources. According to Meta, this efficiency gain translates to nearly five times more cost-effective inference operations. https://lnkd.in/d678VBAV #llama3.3 #aws #sagemaker #llm

Go batch or go home. ☁️⚡️💻 https://go.aws/3ZcMgaD #AmazonBedrock Batch Inference is now generally available in all #AWS regions. Use batch inference to run multiple inference requests asynchronously & improve the performance of model inference on large datasets. Amazon Bedrock offers select foundation models for batch inference at 50% of on-demand inference pricing.

Automate Batch Inference at Scale with Amazon Bedrock. 🚀 Bedrock is Amazon's open source framework for running batch inference workloads at scale. It handles the complexity of deploying, monitoring, and autoscaling your models. 🤖 Key benefits: - Serverless - pay per use. ⚡ - Optimized for cost and performance. 💰 - Easy integration with SageMaker, EKS, and more. 😎 - Open source and extensible. 📜 Bedrock lets you focus on your models while it handles the infrastructure. 🛠️ It's designed for production workloads like fraud detection, recommendations, lead scoring etc. 🌟 Interested to learn more? Check out the link for a deep dive into Bedrock's architecture and how to get started. 👇 I'm happy to chat more about optimizing your batch inference! https://lnkd.in/ezvbgu79 #aws #amazon #ai #artificialintelligence #bigdata #ml #machinelearning #dataanalytics #datascience #genai #generativeai #llm #batchinference #amazonbedrock

AWS SageMaker: Amazon SageMaker is a fully managed service that data scientists and developers use to quickly build, train, and deploy machine learning models. In this introductory course, you are given an overview of Amazon SageMaker, focused on the service's three main components: notebooks, training, and hosting. #ai #ml #datascience #aws

🎉 Just completed the "Introduction to Machine Learning on AWS" course by Amazon Web Services! 🚀 It was a great experience diving into the world of machine learning, where I learned to: ✅ Differentiate between AI, machine learning, and deep learning. ✅ Build, train, and deploy machine learning models. ✅ Select the right AWS machine learning services for various use cases. Excited to apply these new skills to real-world challenges and continue exploring the possibilities in AI and machine learning! #MachineLearning #AWS #AI #LifelongLearning #SkillUp #Coursera #TechInnovation