The Long Tail of ML Deployment - Challenges and Solutions

Dive into a 51-minute podcast episode featuring Tuhin Srivastava, co-founder and CEO of Baseten, discussing the long tail of ML deployment. Explore insights on alleviating engineering burdens for machine learning and data engineers, the importance of embracing engineering aspects in ML, and the evolution of ML practices since 2010. Gain valuable perspectives on the Cambrian explosion in AI, the limitations of LLMs, documentation challenges, and the benefits of microservices in ML deployment. Learn about Baseten's approach to creating valuable models and their hiring opportunities. Connect with the MLOps community through various channels and explore related resources, including the MLOps Jobs board and merchandise.

[] Partnership with QuantumBlack
[] Nayur Khan presenting QuantumBlack
[] QuantumBlack is hiring!
[] Tuhin's preferred coffee
[] Takeaways
[] Please share this episode with a friend!
[] Comments/Reviews
[] Tuhin's background
[] Finance and Law common complaint culture
[] Doing Machine Learning in 2010 - 2011
[] Gum broad or the next company shape?
[] Engineers need to learn machine learning
[] Software engineers need to dig deeper
[] Cambrian Explosion
[] The Holy Trifecta
[] Objective truth and prompting
[] Limitations of LLMs
[] Documentation challenges
[] Baseten creating valuable models
[] Advocate for Microservices or API-based solution
[] Learning Git pains
[] Baseten back ups
[] Baseten is hiring!
[] Wrap up