From the course: Advanced AI Analytics on AWS: Amazon Bedrock, Q, SageMaker Data Wrangler, and QuickSight
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Creating living insights with Amazon Q AI analytics
From the course: Advanced AI Analytics on AWS: Amazon Bedrock, Q, SageMaker Data Wrangler, and QuickSight
Creating living insights with Amazon Q AI analytics
- [Instructor] We're going to talk about how Amazon Q could transform raw data into living actionable insights. And what you're seeing is not just a visualization, it's a living ecosystem where the data is dynamically flowing and transforming and emerging with powerful business insights. And the reason these powerful business intelligence insights are there is that we're able to use automatic anomaly detection and pattern detection using these AI features for analytics. Inside this central nerve center at the heart of the system is the Amazon Q neural core which is a sophisticated AI engine that coordinates all of the data processing and insight generation, and it's pulsing with activity because it's processing and doing pattern recognition. Now, if we look at the data highway here, these are the pathways that are like a high-speed data highway, and they're traveling through and carrying out the information that's going to get processed. If we look at the natural language processing…
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Analyzing lambda costs: Rust vs. traditional approaches3m 18s
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Benchmarking lambda performance: Rust vs. Python with Claude5m 50s
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Leveraging AWS Data Wrangler for analytics2m 50s
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Optimizing energy efficiency in AI analytics workloads3m 56s
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Creating living insights with Amazon Q AI analytics2m 39s
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Setting up development environments with Amazon Q code catalyst5m 13s
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Translating analytics workflows with Q: Python CLI demo8m 4s
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