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All digests tagged Model Deployment
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The continual learning loop is unsolved. We must balance SFT (knowledge injection) and RL (behavior optimization) by carefully tuning the SFT/Distillation to RL ratio for optimal model performance.
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The shift to open intelligence means owning the entire AI stack. Customization now involves RL, environments, and controlling data/safety, moving beyond simple API calls.
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CoreWeave details the AI inference landscape, guiding teams through Serverless, Dedicated, and CKS paths. Learn how to deploy custom models at scale with explicit GPU control and managed operations.
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Discover the shift from physics-based to AI weather forecasting models. Learn how Hugging Face is standardizing the deployment, evaluation, and fine-tuning of complex scientific models using EarthMover and cloud infrastructure.
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Great models often fail in production due to poor scoping, monitoring gaps, or changing real-world data. Success requires aligning model value with business need and planning for drift.
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AI engineering is maturing rapidly. Cost, quality, and agentic capabilities now drive model choice, while 'eval' remains the biggest stack challenge. Agents are moving from reading to taking actions.
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