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AI and MLOps Roundup: May 2023

Researchers and enterprise teams are leveraging large language models (LLMs) for a variety of innovative applications, but future improvements will rely on more than just parameters. Check out our roundup of the top AI and MLOps articles for May 2023!

OpenAI’s CEO Says the Age of Giant AI Models Is Already Over

We've hit diminishing returns on LLM size. Future models will rely on architecture improvements or fine tuning rather than more parameters: https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over

OpenAI’s CEO Says the Age of Giant AI Models Is Already Over

Emergent autonomous scientific research capabilities of LLMs

Carnegie Mellon University chemists have demonstrated LLMs can perform autonomous research. The AI agents synthesized drugs like ibuprofen and aspirin from simple prompts. But without guardrails what would prevent nefarious usage of these novel models? https://arxiv.org/abs/2304.05332

Emergent autonomous scientific research capabilities of LLMs

91% of models degrade in performance

Researchers used 32 datasets and 4 model types to run ~2.5 million experiments on model performance. Their results are eye-opening: https://www.nature.com/articles/s41598-022-15245-z

Temporal quality degradation in AI models

Improve model performance with flexible charts and dashboards

Custom charts and rich dashboards help MLOps teams measure and improve model performance with deeper insights: https://www.fiddler.ai/blog/supercharge-model-performance-with-flexible-charts-and-dashboards

Improve model performance with flexible charts and dashboards

What an MLOps engineer actually does

Interested in becoming an MLOps engineer? Mikiko Bazeley has put together a great guide on what to expect and how to get there: https://medium.com/kitchen-sink-data-science/what-an-mlops-engineer-does-565d4d0adb2b

What an MLOps engineer actually does

How Meta measures the management of its AI ecosystem

How does Meta manage their thousands of ML models to handle model governance, security, accountability, AI fairness, model robustness, and efficiency? Here's a comprehensive overview: https://ai.facebook.com/blog/meta-ai-ecosystem-management-metrics

How Meta measures the management of its AI ecosystem

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