Over the past five years, advances in ai models’ Data processing and reasoning capabilitys have driven enterprise and Industrial Developers to Pursue Larger Models and More Amritius Benchmarks. Now, with agentic ai emerging as the successor to generative ai, demand for smarter, more nuaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa Yet too often “smart ai” is measured by model size or the volume of its training data.
Data Analytics and Artificial Intelligence Company Databricks Argues That Today’s AI Arms Race Misses A Crucial Point: In Production, What Matters Mosts is not a model “knows,” Performs when stakeholders relay on it. Jonathan Frankle, Chief Ai Scientist at Databricks, Emphasizes that real-world trust and return on investment come from how ai models behave in production, not from how much information the constitution.
Unlike Traditional Software, AI Models Generate Probabilistic Outputs Rather Than Deterministic Oones. “The only thing you can measure about an ai system is how it behaves. You can’t look inside it. There Fast companyHe contends that while public benchmarks are useful for gaugging general capability, enterprises often over-India.
What matters far more, he says, is rigorous evaluation on business-specific data to measure quality, refine outputs, and guide reinforcement learning strategies. “Today, People often Deploy Agents by Writing a Prompt, Trying a Couple of Inputs, Checking their vibes, and deplying.
