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Racing into the future: How AWS DeepRacer fueled my AI and ML journey

AWS Machine Learning Blog

At the time, I knew little about AI or machine learning (ML). But AWS DeepRacer instantly captured my interest with its promise that even inexperienced developers could get involved in AI and ML. Panic set in as we realized we would be competing on stage in front of thousands of people while knowing little about ML.

AWS
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Map Earth’s vegetation in under 20 minutes with Amazon SageMaker

AWS Machine Learning Blog

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. We pick the first week of December 2023 in this example. This way, our analysis is based on clear and reliable imagery.

ML
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How I Automated My Machine Learning Workflow with Just 10 Lines of Python

Flipboard

The world’s leading publication for data science, AI, and ML professionals. You don’t need deep ML knowledge or tuning skills. Why Automate ML Model Selection? It’s not just convenient, it’s smart ML hygiene. Libraries We Will Use We will be exploring 2 underrated Python ML Automation libraries.

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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

ML
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Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

AWS Machine Learning Blog

This post is co-written with Ken Kao and Hasan Ali Demirci from Rad AI. Rad AI has reshaped radiology reporting, developing solutions that streamline the most tedious and repetitive tasks, and saving radiologists’ time. In this post, we share how Rad AI reduced real-time inference latency by 50% using Amazon SageMaker.

ML
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Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 2: ModelBuilder

AWS Machine Learning Blog

The recent update enhances usability of the ModelBuilder class for a wide range of use cases, particularly in the rapidly evolving field of generative AI. The machine learning (ML) practitioners need to iterate over these settings before finally deploying the endpoint to SageMaker for inference.

ML
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AI Cybersecurity —  Replacement for Specialists or Efficiency Booster?

Dataconomy

AI is rapidly taking its place in the market, penetrating new application areas in ways we couldnt imagine, including AI cybersecurity solutions. The reason is clear: AIs potential for improving efficiency is almost limitless. The use of AI is evident on both sides of the barricades: by attackers and defenders alike.

AI