AWS AI’s Post

Amazon Bedrock Introduces Prompt Management and Prompt Flows AWS announced the preview launch of Amazon Bedrock Prompt Management and Prompt Flows. These new tools aim to enhance how developers and prompt engineers work with foundation models. Key features: 📝 Bedrock Prompt Management: - Simplifies creation, evaluation, versioning, and sharing of prompts - Includes a Prompt Builder for experimenting with multiple FMs, configurations, and messages - Allows in-place testing and comparison without deployment 🔄 Bedrock Prompt Flows: - Accelerates creation, testing, and deployment of workflows - Features an intuitive visual builder for low-code development - Enables drag-and-drop functionality for prompts, Knowledge Bases, and Lambda functions How it works: Prompt Management: Use the Prompt Builder to experiment with different FMs and configurations. Test and compare prompts in-place, then version and share them via API calls for downstream applications. Prompt Flows: Navigate to the Bedrock Console or Studio, click 'Create Flow', and use the visual builder to drag and drop components. Link nodes, add code hooks, and integrate AWS services to create powerful AI workflows without extensive coding. 🌐 Now in preview across multiple AWS Regions worldwide. To learn more, visit the Amazon Bedrock Prompt Flows and Prompt Management pages - https://lnkd.in/e_wtaWFU #AmazonBedrock #AWS #AITools #PromptEngineering #MachineLearning

Latest Amazon Bedrock features, Prompt Flow, Claude 3 Haiku fine tuning and fully managed Agents. #genai #bedrock #aws

Prompt management that goes with the flow. ☁️✨ https://go.aws/3Y5lSyF With #AmazonBedrock Prompt Management & Prompt Flows, you can simplfy the creation, evaluation, versioning, & sharing of prompts to get the best foundation models responses for your use cases & accelerate the creation, testing, & deployment of workflows through an intuitive visual builder. #AWS #generativeAI

End-to-end vs canonical MLOps stack of best-in-class tools Or maybe something in between How do we see it? The answer, as always, is “it depends”. Some teams have a fairly standard ML use case and decide to buy an end-to-end ML platform. They get everything-MLOps out of the box, and they can focus on ML. Work for many teams, really. We mean it. Quick(ish) time to value. All the things you seem to need (and more). The problem? The further away you go from the standard use case, the harder it gets to adjust the platform to your workflow. And everything looks simple and standard at the beginning. Then business needs to change, requirements change, and it is not so simple anymore. And then there is the pricing discussion. Can you justify spending “this much” on an end-to-end enterprise solution when all you really need is just 2 out of 10 components? Sometimes you can. Sometimes you cannot. Because of all that, many teams stay away from end-to-end. They decide to build a canonical MLOps stack from point solutions that solve just some parts very well. An interesting situation is a hybrid. You start with something like AWS Sagemaker, get to value quickly, and then swap components if/when needed. We saw Brainly do that with experiment tracking. They use both and end-to-end platform (Sagemaker) and best-in-class solution (Neptune). How do you see it? End-to-end, canonical stack, hybrid? Link: https://buff.ly/3wZAFww #ML #MLOps #MLOpsStack

Deploying a machine learning model from a Hugging Face repository to Amazon SageMaker Endpoints is tricky in production. Not only do you have to keep a track of all the data and metadata you produced to get to that model, you need to track all candidates of this model all the way to the best one in production. And that's just the start. The next step is full automation from training to production with comprehensive lineage tracking. 🚀 Good news! ZenML can help! My latest blog post walks you through the steps of leveraging ZenML pipelines to transition your Hugging Face model into a robust, production-ready AWS Sagemaker endpoint. This includes everything from feature engineering to model promotion – all while maintaining full transparency and control with ZenML's brand new Model Control Plane. 🤖 Key takeaways from this end-to-end #MLOps guide: - 🔗 Integrating ZenML with Huggingface Hub and AWS Sagemaker. - 📈 Metrics-driven model promotion ensuring only top-performing models hit production. - 🌐 Deploying a continuously updated, live endpoint using a ZenML deployment pipeline. I sort of went full into this example because I loved it so much; not only open-sourcing the code, but producinga YouTube tutorial series paired with a GitHub repository for those who prefer to learn by watching and doing 🎥. Check all of this out now: 👉 Blog: https://lnkd.in/gtFw3wsM 👉 YouTube Series: https://lnkd.in/g3BudjpU 👉 Code Repo: https://lnkd.in/gFCbdrcH #MLOps #Huggingface #AWS #Sagemaker #ZenML #MachineLearning #Automation #AI P.S. Did you know that ZenML Cloud is now accessible to all! 🌟 Book your demo now and we'll show you how you can build a powerful MLOps workflow for your needs in a few days: https://lnkd.in/gBJ6AJCW

This new integration between SageMaker Model Registry and Model Cards make it very easy to view all information about your models in one place! You can now view your model governance details right where the model metadata resides in SageMaker Studio. #aws #sagemaker #governance

#AWS Step Functions Announces Optimized Integration for AWS #Elemental #MediaConvert 🚀 AWS Elemental MediaConvert, enabling #video #transcode jobs in your #workflow. With this integration, customers can easily build automated, resilient #media workflows using the visual authoring and operator experience of Step Functions ! #NAB #stepfunctions #mediaentertainement

🚀 Welcome to the Journey of Machine Learning with AWS SageMaker! 🚀 Today, I'd like to share some insights about AWS SageMaker. AWS SageMaker is a cloud-based machine learning service that provides a powerful platform to optimize business processes, simplify model training, and scale your machine learning endeavors. Here are some key advantages of AWS SageMaker: 🔍 Data Exploration and Preparation: SageMaker offers user-friendly tools for analyzing and preparing your data, accelerating the model training process. 🧠 Model Training: With easily scalable and customizable algorithms, training complex models becomes more straightforward. 🚀 Deployment and Management: Deploying your trained models quickly and reliably is made easy with just a few clicks using the service. 💡 AutoML Support: SageMaker facilitates faster and more effective model creation with its automatic machine learning (AutoML) capabilities. 🛠 Integration Ease: Integrated with AWS's extensive ecosystem, SageMaker seamlessly works with your existing infrastructure. If you have any questions about machine learning or want to share your experiences, feel free to comment below! #AWS #SageMaker #MachineLearning #CloudComputing #ML #Forecasting #Churn #DataAnalysis #Data #NormDigital

With #AmazonBedrock Prompt Management & Prompt Flows, you can simplfy the creation, evaluation, versioning, & sharing of prompts to get the best foundation models responses for your use cases & accelerate the creation, testing, & deployment of workflows through an intuitive visual builder. #AWS #generativeAI https://go.aws/3Y5lSyF

I've been integrating AWS SageMaker's DataWrangler and AutoML into my workflow and am impressed by the speed and ease of use for data preparation and model development. These tools are fantastic for quick prototyping, but it's crucial to keep an eye on the budget as costs can add up. It's important to balance the convenience and features with the expense to make sure it fits within your project's financial plan. #AI #MachineLearning #AWS #CloudComputing #DataScience