Before You Scale the AI, Fix the Evaluation
Once an AI system makes measurable decisions in production, evaluation decides whether you can trust it. Five learning outcomes on metrics, operating points, and the handoffs between ML and generative AI.
Once an AI system makes measurable decisions in production, evaluation decides whether you can trust it. Five learning outcomes on metrics, operating points, and the handoffs between ML and generative AI.

18 minutes
A 150-billion-parameter mixture-of-experts model streamed from NVMe on an ordinary developer laptop with no usable GPU. Across a 200-token …

2 minutes
Is AI-driven predictive analytics the future of business intelligence or just another overhyped trend? In this episode of Hot or Hype, we …

11 minutes
Discover how combining Ray and Vertex AI creates a complete solution for scalable AI. Our team takes a look at training, deployment, and …

5 minutes
Thoughts on best practices for scaling AI models with cloud computing, including MLOps, cost optimization, and industry-specific use cases …

7 minutes
How cloud platforms close the gap between AI experiments and production capability, with elastic compute, managed data services, and …

4 minutes
Strategies for detecting and mitigating bias to ensure fairness and equity in artificial intelligence.

4 minutes
Generative AI can be employed as a powerful defense tool, enhancing robustness and resilience against various attack vectors.

5 minutes
Implementing Zero Trust for containers can help organizations secure their dynamic containerized environments and minimize the risk of …

2 minutes
Andrew Stevens, CTO of Sakura Sky, will be presenting at CSS2018.

1 minute
If you are interested in TensorFlow, Scikit-learn, Spark MLlib, Mahout, Google Cloud CloudML, PyTorch, or something else, please join us and …

2 minutes
After completing this course, students will be able to use Python and key libraries to manipulate data.

2 minutes
This course will show you how to build deep learning applications using TensorFlow
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