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Hello AWS SI Partners, if you are looking for efficient language models for your use cases, join Deepgram's webinar to learn about their offerings and how you can leverage them on AWS.

Last week, I attended a bootcamp on Generative AI organised by Neo4j Graph Databases and Amazon Bedrock. The key highlight of the bootcamp was the hands-on sessions with Neo4j Graph Databases and Amazon Bedrock Platform. We created and interacted with data and relations using Neo4j graph databases and then used Claude on Amazon Bedrock to achieve RAG. Thanks to Darren Wood for amazing insights and patience to deal with innumerable queries! #graphdatabases #neo4j #bedrock #aws #generativeai

I had a wonderful opportunity to drive a computer vision project in AWS ecosystem, in association with iNeuron.ai. Using a dataset from Kaggle.com, an image classification model was built to detect pneumonia in a patient's chest x-ray. The model was deployed and found to consistently deliver accurate predictions. Current performance of this model: Accuracy: 85.2% Recall Score: 100% (pneumonia positive) and 88% (pneumonia negative) API Latency: 0.9 s (average of 16 samples) Sharing the project flowchart below:

🚀 Exciting News! 🚀 I'm thrilled to announce the successful deployment of my "Flight Fare Prediction System" project on AWS using Beanstalk and CodePipeline! 🛫💰 After rigorous development and testing, I've harnessed the power of machine learning to predict flight fares accurately. Utilizing the Naive Bayes Regressor model, this system can provide travelers with insightful fare estimates for their upcoming journeys. The Data is taken from Kaggle. Check out the project on GitHub: https://lnkd.in/dfsJRY9Q Here's a sneak peek of the deployed API. P.S.: Ignore my HTML Skills #MachineLearning #AWS #Deployment #CodePipeline #Beanstalk #FlightFarePrediction #DataScience #NaiveBayesRegressor #kaggle

Amazon SageMaker now supports geospatial Processing jobs, making it easier for data scientists and ML engineers to run planetary-scale ML workloads. To run such large-scale workloads, customers need a flexible compute cluster that can scale from tens of instances to process a city block, to thousands of instances for planetary-scale processing. Manually managing a DIY compute cluster is slow and expensive. Additionally, building and maintaining a standardized environment to access, process, and visualize geospatial data is complex, time consuming, and expensive. #cloud #space #AI #ML #Aws

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🚨AI Alert 🚨 Sridhar Ramaswamy CEO at  Snowflake announced ❄️Snowflake Arctic❄️ ☃️ A top-tier enterprise-focused LLM that pushes the frontiers of cost-effective training and openness. The Arctic is efficiently intelligent and truly “open”. ☃️Arctic offers top-tier enterprise intelligence among open source LLMs, and it does so using a training compute budget of roughly under $2 million (less than 3K GPU weeks). This means Arctic is more capable than other open source models trained with a similar compute budget. ☃️Arctic is designed to have 480B parameters spread across 128 fine-grained experts and uses top-2 gating to choose 17B active parameters. ☃️Snowflake Arctic is directly available for download from Hugging Face. ☃️Access Arctic via your model garden or catalog of choice including Amazon Web Services (AWS), Lamini, Microsoft Azure, NVIDIA API catalog, Perplexity, Replicate and Together AI over the coming days. 🎥 Be sure to watch the attached Video-Sound on 🔊🔊 Learn More Here: https://lnkd.in/g-H_G4_6 Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #AI #llms #opensource #snowflakearctic #snowflake

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Happening tomorrow: Learn how MongoDB built Atlas Vector Search and a RAG chatbot for its customers. Register for your spot & the recording 👇