In this podcast, the MLOps vs. LLMOps Panel discuss the high-level differences between MLOps and LLMOps, the impact of ML ops on companies, the challenges of open source tools and data safety in financial firms, the cost and rationalization of MLOps, options for large enterprises in ML model development, and the use of foundational models and vector databases.

Beyang Liu, developer of Cody, an open-source AI coding assistant, discusses the challenges and process of incorporating AI into existing products, navigating complex code bases, and the technology used in building Cody. The chapter also touches on the complexity of fine-tuning AI models and supporting multiple language models in Cody.

Language model interpretability experts and AI researchers discuss challenges of evaluating large language models, the impact of chat GPT in the industry, evaluating model performance and data set quality, the use of large language models in machine learning, and tool sets, guardrails, and challenges in language models.

Lingjiao Chen discusses strategies to reduce the cost of using large language models (LLMs) and introduces FrugalGPT, which can match the performance of GPT-4 with up to 98% cost reduction. The podcast also explores optimizing LLM prompts, comparing API providers for cost and quality, approximating performance with a cache layer, and reducing the cost of using LLMs

Panelists George Mathew, Asmitha Rathis, Natalia Burina, and Sahar Mor discuss building products with LLMs, emphasizing transparency, control, and explainability. They explore the challenges of prompting in language models and provide tips for avoiding impersonation and hallucination. They highlight the importance of feedback loops in improving language models and discuss the economic components of using APIs and inference calls. The panel concludes with excitement about the conference and promotion of their own podcast.

Thibaut Labarre, AngelList investing and natural language processing expert, discusses the innovative use of large language models at AngelList, including news article classification for investor dashboards. They also talk about the challenges of prompt engineering, the importance of involving domain experts, and the ethical concerns of using AI models for reading legal texts.

MLOps Coffee Sessions #170 with Phillip Carter, All the Hard Stuff with LLMs in Product Development. 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 Delve into challenges in implementing LLMs, such as security concerns and collaborative measures against attacks. Emphasize the role of ML engineers and product managers in successful implementation. Explore identifying leading indicators and measuring ROI for impactful AI initiatives. // Bio Phillip is on the product team at Honeycomb where he works on a bunch of different developer tooling things. He's an OpenTelemetry maintainer -- chances are if you've read the docs to learn how to use OTel, you've read his words. He's also Honeycomb's (accidental) prompt engineering expert by virtue of building and shipping products that use LLMs. In a past life, he worked on developer tools at Microsoft, helping bring the first cross-platform version of .NET into the world and grow to 5 million active developers. When not doing computer stuff, you'll find Phillip in the mountains riding a snowboard or backpacking in the Cascades. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links ⁠Website: https://phillipcarter.dev/ https://www.honeycomb.io/blog/improving-llms-production-observability https://www.honeycomb.io/blog/hard-stuff-nobody-talks-about-llm https://phillipcarter.dev/posts/how-to-make-an-fsharp-code-fixer/ The "hard stuff" post: https://www.honeycomb.io/blog/hard-stuff-nobody-talks-about-llm Our follow-up on iterating on LLMs in prod: https://www.honeycomb.io/blog/improving-llms-production-observability --------------- ✌️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 Phillip on LinkedIn: https://www.linkedin.com/in/phillip-carter-4714a135/ Timestamps: [00:00] Phillip's preferred coffee [00:33] Takeaways [01:53] Please like, share, and subscribe to our MLOps channels! [02:45] Phillip's background [07:15] Querying Natural Language [11:25] Function calls [14:29] Pasting errors or traces [16:30] Error patterns [20:22] Honeycomb's Improvement cycle [23:20] Prompt boxes rationale [28:06] Prompt injection cycles [32:11] Injection Attempt [33:30] UI undervalued, charging the AI feature [35:11] ROI cost [44:26] Bridging ML and Product Perspective [52:53] AI Model Trade-offs [56:33] Query assistant [59:07] Honeycomb is hiring! [1:00:08] Wrap up

MLOps Coffee Sessions #169 with Barak Turovsky, MLOps at the Age of Generative AI. Thanks to wandb.ai for sponsoring this episode. Check out their new course on evaluating and fine-tuning LLMs wandb.me/genai-mlops.course // Abstract The talk focuses on MLOps aspects of developing, training and serving Generative AI/Large Language models // Bio Barak is an Executive in Residence at Scale Venture Partners, a leading Enterprise venture capital firm. Barak spent 10 years as Head of Product and User Experience for Languages AI and Google Translate teams within the Google AI org, focusing on applying cutting-edge Artificial Intelligence and Machine Learning technologies to deliver magical experiences across Google Search, Assistant, Cloud, Chrome, Ads, and other products. Previously, Barak spent 2 years as a product leader within the Google Commerce team. Most recently, Barak served as Chief Product Officer, responsible for product management and engineering at Trax, a leading provider of Computer Vision AI solutions for Retail and Commerce industries. Prior to joining Google in 2011, Barak was Director of Products in Microsoft’s Mobile Advertising, Head of Mobile Commerce at PayPal, and Chief Technical Officer at an Israeli start-up. He lived more than 10 years in 3 different countries (Russia, Israel, and the US) and fluently speaks three languages. Barak earned a Bachelor of Laws degree from Tel Aviv University, Israel, and a Master’s of Business Administration from the University of California, Berkeley. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Bio and links about Barak's work: https://docs.google.com/document/d/1E4Yrmt_Y57oTEYHQQDvt71XzSJ8Ew5WvscAQbHV4K3U/edit Framework for evaluating Generative AI use cases: https://www.linkedin.com/pulse/framework-evaluating-generative-ai-use-cases-barak-turovsky/?trackingId=%2BMRxEZ9WTPCNH2JscILTeg%3D%3D The Great A.I. Awakening: https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html --------------- ✌️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 Barak on LinkedIn: https://www.linkedin.com/in/baraktur/ Timestamps: [00:00] Barak's preferred coffee [00:23] Barak Turovsky's background [03:10] Please like, share, and subscribe to our MLOps channels! [04:09] Getting into tech [08:39] First wave of AI [12:39] Building a product at a scale and the challenges [15:59] Framework for evaluating Generative AI use cases [24:33] Machine trust adoption [29:13] Wandb's new course [31:10] Focus on achievable use cases for LLMs. [36:36] User feedback [38:23] Disruption of entertainment and customer interactions [46:14] Get new tools or work with your own distribution? [47:57] Importance of data engineers [53:28] ML Engineers Collaborate with Product [56:13] Wrap up

MLOps Coffee Sessions #168 with Piotr Niedźwiedź, Experiment Tracking in the Age of LLMs, co-hosted by Vishnu Rachakonda. // Abstract Piotr shares his journey as an entrepreneur and the importance of focusing on core values to achieve success. He highlights the mission of Neptune to support ML teams by providing them with control and confidence in their models. The conversation delves into the role of experiment tracking in understanding and debugging models, comparing experiments, and versioning models. Piotr introduces the concept of prompt engineering as a different approach to building models, emphasizing the need for prompt validation and testing methods. // Bio Piotr is the CEO of neptune.ai. Day to day, apart from running the company, he focuses on the product side of things. Strategy, planning, ideation, getting deep into user needs and use cases. He really likes it. Piotr's path to ML started with software engineering. Always liked math and started programming when he was 7. In high school, Piotr got into algorithmics and programming competitions and loved competing with the best. That got him into the best CS and Maths program in Poland which funny enough today specializes in machine learning. Piotr did his internships at Facebook and Google and was offered to stay in the Valley. But something about being a FAANG engineer didn’t feel right. He had this spark to do more, build something himself. So with a few of his friends from the algo days, they started Codilime, a software consultancy, and later a sister company Deepsense.ai machine learning consultancy, where he was a CTO. When he came to the ML space from software engineering, he was surprised by the messy experimentation practices, lack of control over model building, and a missing ecosystem of tools to help people deliver models confidently. It was a stark contrast to the software development ecosystem, where you have mature tools for DevOps, observability, or orchestration to execute efficiently in production. And then, one day, some ML engineers from Deepsense.ai came to him and showed him this tool for tracking experiments they built during a Kaggle competition (which they won btw), and he knew this could be big. He asked around, and everyone was struggling with managing experiments. He decided to spin it off as a VC-funded product company, and the rest is history. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://neptune.ai/blog/author/piotr-niedzwiedz https://www.youtube.com/playlist?list=PLKePQLVx9tOfKFbg9GY2Anl41Be4T1-m5 https://thesequence.substack.com/p/-piotr-niedzwiedz-neptunes-ceo-on https://open.spotify.com/episode/2KEqTMAHODbPKdUEtlrhm7?si=ed862b2ac7534e39https://www.linkedin.com/in/piotrniedzwiedz/ --------------- ✌️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 Piotr on LinkedIn: https://www.linkedin.com/in/piotrniedzwiedz/ Timestamps: [00:00] Introduction to Piotr Niedźwiedź [01:35] Please like, share, and subscribe to our MLOps channels! [01:58] Wojciech Zaremba [05:20] The Olympiad [06:31] Building own company [12:16] Talking outside Poland with the same passion [13:45] Adapting with Neptune [19:35] Core values focus [22:02] Product Vision changes with advances [29:36] Control and confidence [30:05] Experiment tracking existing use cases [37:25] Control pane [38:59] Piotr's prediction [43:20] WiFi issues around the world [44:09] Wrap up

MLOps Coffee Sessions #167 with Maxime Beauchemin, Treating Prompt Engineering More Like Code. // Abstract Promptimize is an innovative tool designed to scientifically evaluate the effectiveness of prompts. Discover the advantages of open-sourcing the tool and its relevance, drawing parallels with test suites in software engineering. Uncover the increasing interest in this domain and the necessity for transparent interactions with language models. Delve into the world of prompt optimization, deterministic evaluation, and the unique challenges in AI prompt engineering. // Bio Maxime Beauchemin is the founder and CEO of Preset, a series B startup supporting and commercializing the Apache Superset project. Max was the original creator of Apache Airflow and Apache Superset when he was at Airbnb. Max has over a decade of experience in data engineering, at companies like Lyft, Airbnb, Facebook, and Ubisoft. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Max's first MLOps Podcast episode: https://go.mlops.community/KBnOgN Test-Driven Prompt Engineering for LLMs with Promptimize blog: https://maximebeauchemin.medium.com/mastering-ai-powered-product-development-introducing-promptimize-for-test-driven-prompt-bffbbca91535https://maximebeauchemin.medium.com/mastering-ai-powered-product-development-Test-Driven Prompt Engineering for LLMs with Promptimize podcast: https://talkpython.fm/episodes/show/417/test-driven-prompt-engineering-for-llms-with-promptimizeTaming AI Product Development Through Test-driven Prompt Engineering // Maxime Beauchemin // LLMs in Production Conference lightning talk: https://home.mlops.community/home/videos/taming-ai-product-development-through-test-driven-prompt-engineering --------------- ✌️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 Max on LinkedIn: https://www.linkedin.com/in/maximebeauchemin/ Timestamps: [00:00] Max introducing the Apache Superset project at Preset [01:04] Max's preferred coffee [01:16] Airflow creator [01:45] Takeaways [03:53] Please like, share, and subscribe to our MLOps channels! [04:31] Check Max's first MLOps Podcast episode [05:20] Promptimize [06:10] Interaction with API [08:27] Deterministic evaluation of SQL queries and AI [12:40] Figuring out the right edge cases [14:17] Reaction with Vector Database [15:55] Promptomize Test Suite [18:48] Promptimize vision [20:47] The open-source blood [23:04] Impact of open source [23:18] Dangers of open source [25:25] AI-Language Models Revolution [27:36] Test-driven design [29:46] Prompt tracking [33:41] Building Test Suites as Assets [36:49] Adding new prompt cases to new capabilities [39:32] Monitoring speed and cost [44:07] Creating own benchmarks [46:19] AI feature adding more value to the end users [49:39] Perceived value of the feature [50:53] LLMs costs [52:15] Specialized model versus Generalized model [56:58] Fine-tuning LLMs use cases [1:02:30] Classic Engineer's Dilemma [1:03:46] Build exciting tech that's available [1:05:02] Catastrophic forgetting [1:10:28] Promt driven development [1:13:23] Wrap up