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Evaluation Panel // Large Language Models in Production Conference Part II

Evaluation Panel // Large Language Models in Production Conference Part II

FromMLOps.community


Evaluation Panel // Large Language Models in Production Conference Part II

FromMLOps.community

ratings:
Length:
32 minutes
Released:
Aug 25, 2023
Format:
Podcast episode

Description

MLOps Coffee Sessions #174 with Evaluation Panel, Amrutha Gujjar, Josh Tobin, and Sohini Roy hosted by Abi Aryan.

We are now accepting talk proposals for our next LLM in Production virtual conference on October 3rd. Apply to speak here: https://go.mlops.community/NSAX1O

// Abstract
Language models are very complex thus introducing several challenges in interpretability. The large amounts of data required to train these black-box language models make it even harder to understand why a language model generates a particular output. In the past, transformer models were typically evaluated using perplexity, BLEU score, or human evaluation. However, LLMs amplify the problem even further due to their generative nature thus making them further susceptible to hallucinations and factual inaccuracies. Thus, evaluation becomes an important concern.

// Bio
Abi Aryan
Machine Learning Engineer @ Independent Consultant
Abi is a machine learning engineer and an independent consultant with over 7 years of experience in the industry using ML research and adapting it to solve real-world engineering challenges for businesses for a wide range of companies ranging from e-commerce, insurance, education and media & entertainment where she is responsible for machine learning infrastructure design and model development, integration and deployment at scale for data analysis, computer vision, audio-speech synthesis as well as natural language processing. She is also currently writing and working in autonomous agents and evaluation frameworks for large language models as a researcher at Bolkay.

Amrutha Gujjar
CEO & Co-Founder @ Structured
Amrutha Gujjar is a senior software engineer and CEO and co-founder of Structured, based in New York. With a Bachelor of Science in Computer Science from the University of Washington's Allen School of CSE, she brings expertise in software development and leadership to my work.
Connect with Amrutha on LinkedIn to learn more about her experience and discuss exciting opportunities in software development and leadership.

Josh Tobin
Founder @ GantryJosh Tobin is the founder and CEO of Gantry. Previously, Josh worked as a deep learning & robotics researcher at OpenAI and as a management consultant at McKinsey. He is also the creator of Full Stack Deep Learning (fullstackdeeplearning.com), the first course focused on the emerging engineering discipline of production machine learning. Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel.

Sohini Roy
Senior Developer Relations Manager @ NVIDIASohini Bianka Roy is a senior developer relations manager at NVIDIA, working within the Enterprise Product group. With a passion for the intersection of machine learning and operations, Sohini specializes in the domains of MLOps and LLMOps. With her extensive experience in the field, she plays a crucial role in bridging the gap between developers and enterprise customers, ensuring smooth integration and deployment of NVIDIA's cutting-edge technologies.

// MLOps Jobs board
https://mlops.pallet.xyz/jobs

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/
Connect with Amrutha on LinkedIn: https://www.linkedin.com/in/amruthagujjar/
Connect with Josh on LinkedIn: https://www.linkedin.com/in/josh-tobin-4b3b10a9/
Connect with Sohini on Twitter: https://twitter.com/biankaroy_
Released:
Aug 25, 2023
Format:
Podcast episode

Titles in the series (100)

Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.