Agentic Conversations (formally mlops.community)

Bernie Wu is VP of Business Development for MemVerge. He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies, including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft.

Boosting LLM/RAG Workflows & Scheduling w/ Composable Memory and Checkpointing // MLOps Podcast #270 with Bernie Wu, VP Strategic Partnerships/Business Development of MemVerge.

// Abstract

Limited memory capacity hinders the performance and potential of research and production environments utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques. This discussion explores how leveraging industry-standard CXL memory can be configured as a secondary, composable memory tier to alleviate this constraint.

We will highlight some recent work we’ve done in integrating this novel class of memory into LLM/RAG/vector database frameworks and workflows. Disaggregated shared memory is envisioned to offer high-performance, low-latency caches for model/pipeline checkpoints of LLM models, KV caches during distributed inferencing, LORA adaptors, and in-process data for heterogeneous CPU/GPU workflows. We expect to showcase these types of use cases in the coming months.

// Bio

Bernie is VP of Strategic Partnerships/Business Development for MemVerge. His focus has been on building partnerships in the AI/ML, Kubernetes, and CXL memory ecosystems. He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies, including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft. He is also on the Board of Directors for Cirrus Data Solutions. Bernie has a BS/MS in Engineering from UC Berkeley and an MBA from UCLA.

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: www.memverge.com

Accelerating Data Retrieval in Retrieval Augmentation Generation (RAG) Pipelines using CXL: https://memverge.com/accelerating-data-retrieval-in-rag-pipelines-using-cxl/

Do Re MI for Training Metrics: Start at the Beginning // Todd Underwood // AIQCON: https://youtu.be/DxyOlRdCofo

Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // MLOps Podcast #228: https://youtu.be/6MY-IgqiTpg

Compute Express Link (CXL) FPGA IP: https://www.intel.com/content/www/us/en/products/details/fpga/intellectual-property/interface-protocols/cxl-ip.htmlUltra Ethernet Consortium: https://ultraethernet.org/

Unified Acceleration (UXL) Foundation: https://www.intel.com/content/www/us/en/developer/articles/news/unified-acceleration-uxl-foundation.html

RoCE networks for distributed AI training at scale: https://engineering.fb.com/2024/08/05/data-center-engineering/roce-network-distributed-ai-training-at-scale/

--------------- ✌️Connect With Us ✌️ -------------

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Bernie on LinkedIn: https://www.linkedin.com/in/berniewu/

Timestamps:

[00:00] Bernie's preferred coffee

[00:11] Takeaways

[01:37] First principles thinking focus

[05:02] Memory Abundance Concept Discussion

[06:45] Managing load spikes

[09:38] GPU checkpointing challenges

[16:29] Distributed memory problem solving

[18:27] Composable and Virtual Memory

[21:49] Interactive chat annotation

[23:46] Memory elasticity in AI

[27:33] GPU networking tests

[29:12] GPU Scheduling workflow optimization

[32:18] Kubernetes Extensions and Tools

[37:14] GPU bottleneck analysis

[42:04] Economical memory strategies

[45:14] Elastic memory management strategies

[47:57] Problem-solving approach

[50:15] AI infrastructure elasticity evolution

[52:33] RDMA and RoCE explained

[54:14] Wrap up

Gideon Mendels is the Chief Executive Officer at Comet, the leading solution for managing machine learning workflows. How to Systematically Test and Evaluate Your LLMs Apps

// MLOps Podcast #269 with Gideon Mendels, CEO of Comet.

// Abstract

When building LLM Applications, Developers need to take a hybrid approach from both ML and SW Engineering best practices. They need to define eval metrics and track their entire experimentation to see what is and is not working. They also need to define comprehensive unit tests for their particular use case so they can confidently check if their LLM App is ready to be deployed.

// Bio

Gideon Mendels is the CEO and co-founder of Comet, the leading solution for managing machine learning workflows from experimentation to production. He is a computer scientist, ML researcher and entrepreneur at his core. Before Comet, Gideon co-founded GroupWize, where they trained and deployed NLP models processing billions of chats. His journey with NLP and Speech Recognition models began at Columbia University and Google, where he worked on hate speech and deception detection.

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://www.comet.com/site/

All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s

Opik by Comet: https://www.comet.com/site/products/opik/

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Gideon on LinkedIn: https://www.linkedin.com/in/gideon-mendels/

Timestamps:

[00:00] Gideon's preferred coffee

[00:17] Takeaways

[01:50] A huge shout-out to Comet ML for sponsoring this episode!

[02:09] Please like, share, leave a review, and subscribe to our MLOps channels!

[03:30] Evaluation metrics in AI

[06:55] LLM Evaluation in Practice

[10:57] LLM testing methodologies

[16:56] LLM as a judge

[18:53] OPIC track function overview

[20:33] Tracking user response value

[26:32] Exploring AI metrics integration

[29:05] Experiment tracking and LLMs

[34:27] Micro Macro collaboration in AI

[38:20] RAG Pipeline Reproducibility Snapshot

[40:15] Collaborative experiment tracking

[45:29] Feature flags in CI/CD

[48:55] Labeling challenges and solutions

[54:31] LLM output quality alerts

[56:32] Anomaly detection in model outputs

[1:01:07] Wrap up

Raj Rikhy is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce.

// MLOps Podcast #268 with Raj Rikhy, Principal Product Manager at Microsoft.

// Abstract

In this MLOps Community podcast, Demetrios chats with Raj Rikhy, Principal Product Manager at Microsoft, about deploying AI agents in production. They discuss starting with simple tools, setting clear success criteria, and deploying agents in controlled environments for better scaling. Raj highlights real-time uses like fraud detection and optimizing inference costs with LLMs, while stressing human oversight during early deployment to manage LLM randomness. The episode offers practical advice on deploying AI agents thoughtfully and efficiently, avoiding over-engineering, and integrating AI into everyday applications.

// Bio

Raj is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce.

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://www.microsoft.com/en-us/research/focus-area/ai-and-microsoft-research/

--------------- ✌️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 Raj on LinkedIn: https://www.linkedin.com/in/rajrikhy/

Timestamps:

[00:00] Raj's preferred coffee

[00:16] Takeaways

[00:23] Join the AI Agents in Production Conference on November 13th!

[01:25] Categorizing different agents

[06:59] Agent environment frameworks

[15:52] Debugging Strategies for Complex Systems

[22:26] Evaluating Agent Frameworks Effectively

[28:30] Defining success in projects

[31:45] Process simplification benefits

[35:32] Agent workflow use cases

[39:29] Tinder for clothing recommendation

[44:20] Speed Reliability Trade-offs in ML

[48:06] Brilliant minds and doubts

[48:50] Wrap up

//Abstract

The AI Dream Team: Strategies for ML Recruitment and Growth // MLOps Podcast #267 with Jelmer Borst, Analytics & Machine Learning Domain Lead, and Daniela Solis, Machine Learning Product Owner, of Picnic.

// Abstract

Like many companies, Picnic started out with a small, central data science team. As this grows larger, focusing on more complex models, it questions the skillsets & organisational setup. Use an ML platform, or build ourselves? A central team vs. embedded? Hire data scientists vs. ML engineers vs. MLOps engineers. How to foster a team culture of end-to-end ownership to balance short-term & long-term impact

// Bio

Jelmer Borst

Jelmer leads the analytics & machine learning teams at Picnic, an app-only online groceries company based in the Netherlands. Whilst his background is in aerospace engineering, he was looking for something faster-paced and found that at Picnic. He loves the intersection of solving business challenges using technology & data. In his free time loves to cook food and tinker with the latest AI developments.

Daniela Solis Morales

As a Machine Learning Lead at Picnic, I am responsible for ensuring the success of end-to-end Machine Learning systems. My work involves bringing models into production across various domains, including Personalization, Fraud Detection, and Natural Language Processing.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

--------------- ✌️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 Jelmer on LinkedIn: https://www.linkedin.com/in/japborst

Connect with Daniela on LinkedIn: https://www.linkedin.com/in/daniela-solis-morales/

Timestamps:

[00:00] Jelmer and Daniela's preferred coffee

[00:37] Takeaways

[03:46] Please like, share, leave a review, and subscribe to our MLOps channels!

[03:58] Use case evolution review

[08:24] Centralized ML strategy

[11:53] Managing zombie models effectively

[15:52] Clean data and collaboration

[21:07] Snowflake ML Integration options

[22:49] MLOps infrastructure components

[25:36] Pull vs. Push Adoption

[27:03] ML Model Monitoring Roles

[31:56] Inventory prediction

[36:00] Scaling machine learning teams

[42:09] Team expansion and structure

[48:20] Exploring effective team organization

[51:43] Blog reading insights

[54:25] Playing hard mode

[57:33] Wrap up

Francisco Ingham, LLM consultant, NLP developer, and founder of Pampa Labs.Making Your Company LLM-native

// MLOps Podcast #266 with Francisco Ingham, Founder of Pampa Labs.

// Abstract

Being an LLM-native is becoming one of the key differentiators among companies in vastly different verticals. Everyone wants to use LLMs, and everyone wants to be on top of the current tech, but what does it really mean to be LLM-native?

LLM-native involves two ends of a spectrum. On the one hand, we have the product or service that the company offers, which surely offers many automation opportunities. LLMs can be applied strategically to scale at a lower cost and offer a better experience for users.

But being LLM-native not only involves the company's customers, it also involves each stakeholder involved in the company's operations. How can employees integrate LLMs into their daily workflows? How can we, as developers, leverage the advancements in the field not only as builders but as adopters?

We will tackle these and other key questions for anyone looking to capitalize on the LLM wave, prioritizing real results over the hype.

// Bio

Currently working at Pampa Labs, where we help companies become AI-native and build AI-native products. Our expertise lies on the LLM-science side, or how to build a successful data flywheel to leverage user interactions to continuously improve the product. We also spearhead Pampa-friends - the first Spanish-speaking community of AI Engineers.

Previously worked in management consulting, was a TA in fastai in SF, and led the cross-AI + dev tools team at Mercado Libre.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: pampa.ai

--------------- ✌️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 Francisco on LinkedIn: https://www.linkedin.com/in/fpingham/

Timestamps:

[00:00] Francisco's preferred coffee

[00:13] Takeaways

[00:37] Please like, share, leave a review, and subscribe to our MLOps channels!

[00:51] A Literature Geek

[02:41] LLM-native company

[03:54] Integrating LLM in workflows

[07:21] Unexpected LLM applications

[10:38] LLMs in the development process

[14:00] Vibe check to evaluation

[15:36] Experiment tracking optimizations

[20:22] LLMs as judges discussion

[24:43] Presentaciones automatizadas para podcast

[27:48] AI operating system and agents

[31:29] Importance of SEO expertise

[35:33] Experimentation and evaluation

[39:20] AI integration strategies

[41:50] RAG approach spectrum analysis

[44:40] Search vs Retrieval in AI

[49:02] Recommender Systems vs RAG

[52:08] LLMs in recommender systems

[53:10] LLM interface design insights

Simba Khadder is the Founder & CEO of Featureform. He started his ML career in recommender systems, where he architected a multi-modal personalization engine that powered 100s of millions of users’ experiences.

Unpacking 3 Types of Feature Stores // MLOps Podcast #265 with Simba Khadder, Founder & CEO of Featureform.

// Abstract

Simba dives into how feature stores have evolved and how they now intersect with vector stores, especially in the world of machine learning and LLMs. He breaks down what embeddings are, how they power recommender systems, and why personalization is key to improving LLM prompts. Simba also sheds light on the difference between feature and vector stores, explaining how each plays its part in making ML workflows smoother. Plus, we get into the latest challenges and cool innovations happening in MLOps.

// Bio

Simba Khadder is the Founder & CEO of Featureform. After leaving Google, Simba founded his first company, TritonML. His startup grew quickly, and Simba and his team built ML infrastructure that handled over 100M monthly active users. He instilled his learnings into Featureform’s virtual feature store. Featureform turns your existing infrastructure into a Feature Store. He’s also an avid surfer, a mixed martial artist, a published astrophysicist for his work on finding Planet 9, and he ran the SF marathon in basketball shoes.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: featureform.com

BigQuery Feature Store // Nicolas Mauti // MLOps Podcast #255: https://www.youtube.com/watch?v=NtDKbGyRHXQ&ab_channel=MLOps.community

--------------- ✌️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 Simba on LinkedIn: https://www.linkedin.com/in/simba-k/

Timestamps:

[00:00] Simba's preferred coffee

[00:08] Takeaways

[02:01] Coining the term 'Embedding'

[07:10] Dual Tower Recommender System

[10:06] Complexity vs Reliability in AI

[12:39] Vector Stores and Feature Stores

[17:56] Value of Data Scientists

[20:27] Scalability vs Quick Solutions

[23:07] MLOps vs LLMOps Debate

[24:12] Feature Stores' current landscape

[32:02] ML lifecycle challenges and tools

[36:16] Feature Stores bundling impact

[42:13] Feature Stores and BigQuery

[47:42] Virtual vs Literal Feature Store

[50:13] Hadoop Community Challenges

[52:46] LLM data lifecycle challenges

[56:30] Personalization in prompting usage

[59:09] Contextualizing company variables

[1:03:10] DSPy framework adoption insights

[1:05:25] Wrap up

Stefano Bosisio is an accomplished MLOps Engineer with a solid background in Biomedical Engineering, focusing on cellular biology, genetics, and molecular simulations. Reinvent Yourself and Be Curious

// MLOps Podcast #264 with Stefano Bosisio, MLOps Engineer at Synthesia.

// Abstract

This talk goes through Stefano's experience, to be an inspirational source for whoever wants to jump into a career in the MLOps sector. Moreover, Stefano will also introduce his MLOps Course on the MLOps community platform.

// Bio

Stefano Bosisio is an MLOps Engineer with a versatile background that ranges from biomedical engineering to computational chemistry and data science. Stefano got an MSc in biomedical engineering from the Polytechnic of Milan, focusing on cellular biology, genetics, and molecular simulations. Then, he landed in Scotland, in Edinburgh, to earn a PhD in chemistry from the University of Edinburgh, where he developed robust physical theories and simulation methods to understand and unlock the drug discovery problem. After completing his PhD, Stefano transitioned into Data Science, where he began his career as a data scientist. His interest in machine learning engineering grew, leading him to specialize in building ML platforms that drive business success. Stefano's expertise bridges the gap between complex scientific research and practical machine learning applications, making him a key figure in the MLOps field. Bonus points beyond data: Stefano, as a proper Italian, loves cooking and (mainly) baking, playing the piano, crocheting, and running half-marathons.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://medium.com/@stefanobosisio1First

MLOps Stack Course: https://learn.mlops.community/courses/languages/your-first-mlops-stack/

--------------- ✌️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 Stefano on LinkedIn: https://www.linkedin.com/in/stefano-bosisio1/

Timestamps:

[00:00] Stephano's preferred coffee

[00:12] Takeaways

[01:06] Stephano's MLOps Course

[01:47] From Academia to AI Industry

[09:10] Data science and platforms

[16:53] Persistent MLOps challenges

[21:23] Internal evangelization for success

[24:21] Adapt communication skills to diverse individual needs

[29:43] Key components of ML pipelines are essential

[33:47] Create a generalizable AI training pipeline with Kubeflow

[35:44] Consider cost-effective algorithms and deployment methods

[39:02] Agree with Dream Platform; LLMs require a simple microservice

[42:48] Auto scaling: crucial, tricky, prone to issues

[46:28] Auto-scaling issues with Apache Beam data pipelines

[49:49] Guiding students through MLOps with practical experience

[53:16] Bulletproof Problem Solving: Decision trees for problem analysis

[55:03] Evaluate tools critically; appreciate educational opportunities

[57:01] Wrap up

Global Feature Store: Optimizing Locally and Scaling Globally at Delivery Hero

// MLOps Podcast #263 with Delivery Hero's Gottam Sai Bharath, Senior Machine Learning Engineer & Cole Bailey, ML Platform Engineering Manager.

// Abstract

Delivery Hero innovates locally within each department to develop MLOps practices most effective in that particular context. We also discuss our efforts to reduce redundancy and inefficiency across the company. Hear about our experiences in creating multiple micro feature stores within our departments, and our goal to unify these into a Global Feature Store that is more powerful when combined.

// Bio

Sai Bharath Gottam

With a passion for translating complex technical concepts into practical solutions, Sai excels at making intricate topics accessible and engaging. As a Senior Machine Learning Engineer at Delivery Hero, Sai works on cutting-edge machine learning platforms that guarantee seamless delivery experiences. Always eager to share insights and innovations, Sai is committed to making technology understandable and enjoyable for all.

Cole Bailey

Bridging data science and production-grade software engineering.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://www.deliveryhero.com/

--------------- ✌️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 Sai on LinkedIn: https://www.linkedin.com/in/sai-bharath-gottam/

Connect with Cole on LinkedIn: www.linkedin.com/in/cole-bailey

Timestamps:

[00:00] Sai and Cole's preferred coffee

[00:42] Takeaways

[01:51] Please like, share, leave a review, and subscribe to our MLOps channels!

[02:08] Life changes in Delivery Hero

[05:21] Global Feature Store and Pandora

[12:21] Tech integration strategies

[20:08] Defining Feature and Feature Store

[22:46] Feature Store vs Data Platform

[26:26] Features are discoverable

[32:56] Onboarding and Feature Testing

[36:00] Data consistency

[41:07] Future Vision Feature Store

[44:17] Multi-cloud strategies

[46:33] Wrap up

Adam Kamor is the Co-founder of Tonic, a company that specializes in creating mock data that preserves secure datasets.

RAG Quality Starts with Data Quality // MLOps Podcast #262 with Adam Kamor, Co-Founder & Head of Engineering of Tonic.ai.

// Abstract

Dive into what makes Retrieval-Augmented Generation (RAG) systems tick—and it all starts with the data. We’ll be talking with an expert in the field who knows exactly how to transform messy, unstructured enterprise data into high-quality fuel for RAG systems. Expect to learn the essentials of data prep, uncover the common challenges that can derail even the best-laid plans, and discover some insider tips on how to boost your RAG system’s performance. We’ll also touch on the critical aspects of data privacy and governance, ensuring your data stays secure while maximizing its utility. If you’re aiming to get the most out of your RAG systems or just curious about the behind-the-scenes work that makes them effective, this episode is packed with insights that can help you level up your game.

// Bio

Adam Kamor, PhD, is the Co-founder and Head of Engineering of Tonic.ai. Since completing his PhD in Physics at Georgia Tech, Adam has committed himself to enabling the work of others through the programs he develops. In his roles at Microsoft and Kabbage, he handled UI design and led the development of new features to anticipate customer needs. At Tableau, he played a role in developing the platform’s analytics/calculation capabilities. As a founder of Tonic.ai, he is leading the development of unstructured data solutions that are transforming the work of fellow developers, analysts, and data engineers alike.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://www.tonic.ai

Various topics about RAG and LLM security are available on Tonic.ai's blogs: https://www.tonic.ai/bloghttps://www.tonic.ai/blog/how-to-prevent-data-leakage-in-your-ai-applications-with-tonic-textual-and-snowpark-container-serviceshttps://www.tonic.ai/blog/rag-evaluation-series-validating-the-rag-performance-of-the-openais-rag-assistant-vs-googles-vertex-search-and-conversation

https://www.youtube.com/watch?v=5xdyt4oRONUhttps://www.tonic.ai/blog/what-is-retrieval-augmented-generation-the-benefits-of-implementing-rag-in-using-llms

--------------- ✌️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 Adam on LinkedIn: https://www.linkedin.com/in/adam-kamor-85720b48/

Timestamps:

[00:00] Adam's preferred coffee

[00:24] Takeaways

[00:59] Huge shout-out to Tonic.ai for supporting the community!

[01:03] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:18] Naming a product

[03:38] Tonic Textual

[08:00] Managing PII and Data Safety

[10:16] Chunking strategies for context

[14:19] Data prep for RAG

[17:20] Data quality in AI systems

[20:58] Data integrity in PDFs

[27:12] Ensuring chatbot data freshness

[33:02] Managed PostgreSQL and Vector DB

[34:49] RBAC database vs file access

[37:35] Slack AI data leakage solutions

[42:26] Hot swapping

[46:06] LLM security concerns

[47:03] Privacy management best practices

[49:02] Chatbot design patterns

[50:39] RAG growth and impact

[52:40] Retrieval Evaluation best practices

[59:20] Wrap up