AWS AI’s Post

Simplify & accelerate the machine learning (ML) development lifecycle with #AmazonQ Developer in #AmazonSageMaker Studio. ☁️⚡💻 https://go.aws/3Y1KDvM This #generativeAI assistant generates code, recommends the best tools for ML tasks, provides step-by-step guidance, & offers troubleshooting assistance when you encounter errors. With Amazon Q Developer in SageMaker Studio, you can build, train & deploy ML models without leaving SageMaker Studio. #AWS

SageMaker will solve all of your life’s problems.

"AWS isn't hard or complex" meanwhile it takes them 3 sets of documentation combining almost 11,000 pages to show how to use Sagemaker which is meant to be a "easy ML solution" lol #AWS #ML #Software #AmazonSageMaker #AI

🚀 Big News in the ML Universe! 🌌 JFrog, the powerhouse behind the cutting-edge JFrog Software Supply Chain Platform, has just leaped forward with a pivotal integration with Amazon SageMaker! 🧠💡 This is not just another tech update - it's a transformative step for all my fellow developers and data scientists out there looking to harness the cloud for machine learning. What does this mean for you? A streamlined journey from idea to deployment! 💥 Imagine combining JFrog Artifactory's robust management of software artifacts with the machine learning prowess of Amazon SageMaker. It's now possible. What we're talking about is: - Expedited building of ML models. 🏗️ - Swift training processes. 🏋️♂️ - Seamless deployment straight to the cloud. ☁️ Time? Saved! Resources? Optimized! Outcome? Revolutionary! This is not just a transformation - it’s an evolution in efficiency that empowers you to deliver smarter solutions faster than ever before. 🚀🧑💻 🔍 Want to dive deeper into this tech marvel? You won't want to miss the full story on how these twin titans of tech are changing the ML deployment game. 👉 Check out the full article on my tech, AI, and Web3 website to see how this integration marks a new era for cloud-based machine learning. Don’t just watch the future happen – be a part of it. Innovate, integrate, and revolutionize with JFrog and Amazon SageMaker! 🌟 🔗 Get the insights you need to stay ahead - [Click here for more!](https://lnkd.in/d29NV2Dm) #MachineLearning #CloudInnovation #JFrog #AmazonSageMaker #TechNews #ArtificialIntelligence #DevOps #AIIntegration #TechTrends #DataScience #CloudComputing #Innovation

AWS SageMaker: Unleashing the Power of Machine Learning Amazon SageMaker is revolutionizing machine learning (ML) for data scientists and developers. As a fully managed service, it simplifies the process of building, training, and deploying ML models in a production-ready environment, making it the ideal solution for ML projects. Key features include a user-friendly interface for an efficient ML workflow, effortless data management, optimized ML algorithms for large environments, and the flexibility to support custom algorithms. SageMaker also ensures secure, scalable model deployment with a simple console interface. 🚀 For pricing, SageMaker is free within the AWS Free Tier and for Studio Lab users, following AWS's pay-as-you-go structure without upfront commitments. New users can easily start with comprehensive ML resources, guided setups, automated low-code/no-code options, and explore diverse ML environments. Extensive documentation further aids in learning. Amazon SageMaker is not just a tool but an entire ecosystem, catering to both beginners and experts with its comprehensive features and flexible pricing, making it an indispensable asset for leveraging ML. 💡 #AWS #SageMaker #AIInnovation #CloudComputing Take a look at their official docs here! https://lnkd.in/dN4YSTqN

📢 Exciting News! 🚀 Check out this comprehensive guide on "Building machine learning workflows with Amazon SageMaker Pipelines: A guide to best practices and design patterns." 📚 Amazon SageMaker Pipelines is an incredible AWS service that streamlines and orchestrates the entire process of building and coordinating machine learning workflows. From data loading and transformation to training, tuning, and deployment, it empowers ML developers with efficient workflow management. 📊 To maximize the value of SageMaker Pipelines and enhance your development experience, the post outlines some key best practices: 1️⃣ Utilize Pipeline Session for lazy loading and convenient interaction with other SageMaker entities and resources. 2️⃣ Run pipelines locally in "local mode" for cost-effective and quick iterations during development. 3️⃣ Implement versioning to manage pipeline definitions and artifacts effectively. 4️⃣ Integrate with SageMaker Experiments for organized and tracked pipeline runs. 5️⃣ Secure your ML workloads within a private VPC by deploying SageMaker Pipelines in a secure network configuration. 6️⃣ Use tags to monitor the cost of pipeline runs efficiently in the cost explorer. The blog post also covers design patterns for common scenarios, including running lightweight Python functions using Lambda steps and passing data between different pipeline steps. 💻 By following these best practices and leveraging the design patterns, you can supercharge your machine learning workflows with Amazon SageMaker Pipelines! ⚙️📈 Read the full article here: [Link to Blog Post](https://ift.tt/1VtqpWa) 📌 Stay ahead in the ML game! Don't miss out on this valuable resource. 📖 The post originally appeared on InstaDataHelp AI News. Check it out to stay up-to-date with the latest AI and ML trends. 🌟 #SageMaker #MachineLearning #Workflow #AmazonAWS #DataScience #AI #AWS #Development #BestPractices #DesignPatterns #InstaDataHelpAI

"Amazon SageMaker is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case. With SageMaker, you can build, train and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more – all in one integrated development environment (IDE). SageMaker supports governance requirements with simplified access control and transparency over your ML projects. In addition, you can build your own FMs, large models that were trained on massive datasets, with purpose-built tools to fine-tune, experiment, retrain, and deploy FMs. SageMaker offers access to hundreds of pretrained models, including publicly available FMs, that you can deploy with just a few clicks." https://lnkd.in/gCJ76wBA

Learn how to build an end-to-end #AI workflow leveraging #Snowpark and #AWS Sagemaker, and the common patterns for preparing training data, building models, and deploying models. https://okt.to/8bga9v

There is still a sharp learning curve and a cost-barrier to testing out your AI Use Cases. Today I was creating a PoC with AWS AutoML which allows you to Fine-Tune models without writing code. It intends to make ML Tuning more accessible for developers and non-devs alike. Alternative would be to open a Juptyer Notebook in AWS Sagemaker and start from scratch. AutoML supports major LLM models, including Google's FLAN-T5 (the model I wanted to use). Big downside of this approach is that it only supports Full Fine-Tuning. In my example below, I was training the FLAN-T5 Large on only 350 rows of data but it will still require 2-5 hours of compute time (and a lot of $$$) because that's how full Fine-Tuning works. A better way to build this PoC would be to use Parameter Efficient Fine Tuning (PEFT) methods as they affect, which is currently not supported by AWS AutoML. The learning curve and cost barrier still exists and tools like AutoML we are starting to close the gap. We still have a long way to go!

🚀 Unlock Your ML Potential with Our 'Amazon SageMaker: Machine Learning Workflows' lesson by Deniz Yilmaz! 🚀 https://lnkd.in/errnsKxQ Are you looking to elevate your machine learning operations (#MLOps) #skills and harness the power of Amazon #SageMaker? Then this less is designed just for you! 🌟 Why Enroll? 🔍 MLOps Mastery: Understand MLOps principles and discover how Amazon SageMaker seamlessly integrates to enhance efficiency and scalability. 🛠️ Amazon SageMaker Studio: Dive into its Integrated Development Environment to leverage key SageMaker features for a streamlined workflow. 📈 Lifecycle Management: Learn how SageMaker Debugger and Model Monitor optimize and manage the machine learning lifecycle, ensuring robust and reliable models. 🔧 CI/CD for ML: Utilize Amazon SageMaker Projects to implement Continuous Integration and Continuous Delivery practices, essential for MLOps engineers. 🔗 Pipeline Tools: Gain hands-on experience with various tools, including SageMaker Model Building Pipelines, SageMaker Operators for Kubernetes, and SageMaker Components for Kubeflow Pipelines. Whether you're an ML engineer, data scientist, or tech enthusiast, this lesson provides the skills needed to build, deploy, and monitor sophisticated machine learning models with ease. #MachineLearning #MLOps #AmazonSageMaker #DataScience #AI #MachineLearningPipelines #ContinuousIntegration #ContinuousDelivery #MLWorkflows #TechTraining #AIInnovation