Morgan McGuire and Anish Shah discuss the challenges of productionizing large language models, including cost optimization, latency requirements, trust of output, and debugging. They also mention an upcoming AI in Production Conference on February 22 with informative workshops.
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Quick takeaways
User data is crucial for evaluating ML models, aiding in identifying areas for improvement and enhancing performance.
Evaluation challenges include benchmark limitations, gaming leaderboards, and the need for rigorous assessment of model strengths and weaknesses.
Deep dives
Importance of Gathering User Data for Evaluation
One key insight from the podcast is the importance of gathering user data for evaluation purposes. The hosts emphasize the need to mine user data and incorporate it into the evaluation framework. By collecting data from actual users of an application that utilizes LM (large language model), teams can gather insights on how users interact with the application and identify areas for improvement. Additionally, this user data can inform decisions on updating documentation, improving product intuitiveness, and addressing specific use cases. It is seen as a valuable resource for evaluating and enhancing the performance of LM-based applications.
Evaluation Challenges and Trade-offs
The podcast discusses the challenges and trade-offs involved in the evaluation of LM models. It highlights the complexity of evaluating LM models due to limitations in existing benchmarks and the potential for gaming leaderboards. Evaluating context precision, context recall, answer correctness, and other relevant metrics is crucial. The hosts stress the importance of rigorously assessing the strengths and weaknesses of different models, and the need to evaluate performance across a range of datasets and tasks. They also mention the potential of evaluation as a service, but note the skepticism around proprietary data sources and the importance of transparency in the evaluation process.
Potential of Hallucination Mitigation Techniques
The podcast explores the potential of hallucination mitigation techniques for LM models. The hosts mention the emergence of various techniques aimed at reducing the occurrence of hallucinations in model-generated outputs. They discuss the concept of an evaluation harness, similar to the LM evaluation harness, specifically designed for evaluating the effectiveness of hallucination mitigation techniques. The hosts highlight the need for reliable evaluation tools and suggest that integrating such techniques into LM evaluation frameworks can provide valuable insights and improve the overall reliability of LM models.
The Role of Weights and Biases in LM Evaluation
The podcast highlights the role of Weights and Biases in facilitating LM evaluation. The hosts discuss how W&B provides a platform to measure, analyze, and visualize evaluation metrics, making it easier for developers to assess model performance. They mention the integration of external evaluation tools, such as eleutherAI's LM evaluation harness, into the W&B platform. This integration allows users to efficiently log and track various evaluation metrics. The hosts also stress the benefits of using W&B as a centralized system of record, where evaluation metrics, reports, and data can be stored and shared effectively, enhancing collaboration and decision-making.
Morgan McGuire has held a variety of roles in the past 13 years. In 2008, he completed a Research Internship at Queen Mary, University of London.
Currently, he is the Head of Growth ML and Growth ML Engineer at Weights & Biases.
Anish Shah has been working in the tech industry since 2015. In 2015, he was a Technical Support at Fox School of Business at Temple University.
In 2021, he has been an MLOps Engineer - Growth and a Tier 2 Support Machine Learning Engineer at Weights & Biases.
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Large Language Models have taken the world by storm. But what are the real use cases? What are the challenges in productionizing them? In this event, you will hear from practitioners about how they are dealing with things such as cost optimization, latency requirements, trust of output, and debugging. You will also get the opportunity to join workshops that will teach you how to set up your use cases and skip over all the headaches.
Join the AI in Production Conference on February 22 here: https://home.mlops.community/home/events/ai-in-production-2024-02-15
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MLOps podcast #213 with Weights and Biases' Growth Director, Morgan McGuire and MLE, Anish Shah, Evaluating and Integrating ML Models brought to you by our Premium Brand Partner @WeightsBiases.
// Abstract
Anish Shah and Morgan McGuire share insights on their journey into ML, the exciting work they're doing at Weights and Biases, and their thoughts on MLOps. They discuss using large language models (LLMs) for translation, pre-written code, and internal support. They discuss the challenges of integrating LLMs into products, the need for real use cases, and maintaining credibility.
They also touch on evaluating ML models collaboratively and the importance of continual improvement. They emphasize understanding retrieval and balancing novelty with precision. This episode provides a deep dive into Weights and Biases' work with LLMs and the future of ML evaluation in MLOps. It's a must-listen for anyone interested in LLMs and ML evaluation.
// Bio
Anish Shah
Anish loves turning ML ideas into ML products. He started his career working with multiple Data Science teams within SAP, working with traditional ML, deep learning, and recommendation systems before landing at Weights & Biases. With the art of programming and a little magic, Anish crafts ML projects to help better serve our customers, turning “oh nos” to “a-ha”s!
Morgan McGuire
Morgan is a Growth Director and an ML Engineer at Weights & Biases. He has a background in NLP and previously worked at Facebook on the Safety team where he helped classify and flag potentially high-severity content for removal.
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
AI in Production Conference: https://home.mlops.community/home/events/ai-in-production-2024-02-15
Website: https://wandb.ai/
Prompt Templates the Song: https://www.youtube.com/watch?v=g6WT85gIsE8
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Morgan on LinkedIn: https://www.linkedin.com/in/morganmcg1/
Connect with Anish on LinkedIn: https://www.linkedin.com/in/anish-shah/
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