Microsoft is ready for big tech's war for AI dominance. But will hallucination snowballing ruin LLMs? And what makes an AI engineer different than others? Check out our roundup of the top AI and MLOps articles for September 2023!
Microsoft’s Satya Nadella is winning Big Tech’s AI war
When Satya Nadella became CEO of Microsoft, its market cap was a stagnant $300 billion; today it has surged past $2.5 trillion. Here's how he plans to become the leader in AI: https://www.fastcompany.com/90931084/satya-nadella-microsoft-ai-frontrunner
The Rise of the AI Engineer
The "AI Engineer" is an emerging subdiscipline separate from ML engineers, data engineers, or other software engineering roles. Here's what makes it different and why it will be the most in-demand skill: https://www.latent.space/p/ai-engineer
Four Ways that Enterprises Deploy LLMs
With the rapid pace of LLMOps, so far enterprises have deployed LLMs in four ways. Learn the pros and cons of each, and which may be the right fit for your org: https://www.fiddler.ai/blog/four-ways-that-enterprises-deploy-llms
How Language Model Hallucinations Can Snowball
A major risk of LLMs is their tendency to hallucinate, which can result in further false claims and eventually hallucination snowballing. Researchers explored why this happens and how to fix it: https://arxiv.org/pdf/2305.13534.pdf
Chronon — A Declarative Feature Engineering Framework
Airbnb went from months to days to generate new sets of features for their models. Learn how they're able to continuously turn raw data into features for both training and serving: https://medium.com/airbnb-engineering/chronon-a-declarative-feature-engineering-framework-b7b8ce796e04
Testing Language Models (and Prompts) Like We Test Software
Testing LLMs like software is a particularly challenging emerging area of focus. Check out this great guide on ways to get started: https://towardsdatascience.com/testing-large-language-models-like-we-test-software-92745d28a359
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