Agentic Conversations (formally mlops.community)

Sophia Rowland is a Senior Product Manager focusing on ModelOps and MLOps at SAS. In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics.

David Weik has a passion for data and creating integrated customer-centric solutions. Thinking data and people first to create value-added solutions.

Extending AI: From Industry to Innovation // MLOps Podcast #247 with Sophia Rowland, Senior Product Manager and David Weik, Senior Solutions Architect of SAS.

Huge thank you to SAS for sponsoring this episode. SAS - http://www.sas.com/

// Abstract

Organizations worldwide invest hundreds of billions into AI, but they do not see a return on their investments until they are able to leverage their analytical assets and models to make better decisions. At SAS, we focus on optimizing every step of the Data and AI lifecycle to get high-performing models into a form and location where they drive analytically driven decisions. Join experts from SAS as they share learnings and best practices from implementing MLOps and LLMOPs at organizations across industries, around the globe, and using various types of models and deployments, from IoT CV problems to composite flows that feature LLMs.

// Bio

Sophia Rowland

Sophia Rowland is a Senior Product Manager focusing on ModelOps and MLOps at SAS. In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics. As an active speaker and writer, Sophia has spoken at events like All Things Open, SAS Explore, and SAS Innovate as well as written dozens of blogs and articles. As a staunch North Carolinian, Sophia holds degrees from both UNC-Chapel Hill and Duke, including bachelor’s degrees in computer science and psychology and a Master of Science in Quantitative Management: Business Analytics from the Fuqua School of Business. Outside of work, Sophia enjoys reading an eclectic assortment of books, hiking throughout North Carolina, and trying to stay upright while ice skating.

David Weik

David joined SAS in 2020 as a solutions architect. He helps customers to define and implement data-driven solutions. Previously, David was a SAS administrator/developer at a German insurance company working with the integration capabilities of SAS, Robotic Process Automation, and more.

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// Related Links

http://www.sas.com/

--------------- ✌️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 Sophia on LinkedIn: https://www.linkedin.com/in/sophia-rowland/

Connect with David on LinkedIn: https://www.linkedin.com/in/david-weik/

Timestamps:

[00:00] Sophia & David's preferred coffee

[00:19] Takeaways

[02:11] Please like, share, leave a review, and subscribe to our MLOps channels!

[02:55] Hands-on MLOps and AI

[05:14] Next-Gen MLOps Challenges

[07:24] Data scientists adopting software

[11:48] Taking a different approach

[13:43] Zombie Model Management

[16:36] Optimizing ML Revenue Allocation

[18:39] Other use cases - Lockout - Tagout procedure

[21:43] Vision Model Integration Challenges

[26:16] Costly errors in predictive maintenance

[27:25] Integration of Gen AI

[34:32] Governance challenges in AI

[38:00] Governance in Gen AI vs Governance with Traditional ML

[41:53] Evaluation challenges in industries

[46:49] Interface frustration with Chatbots

[51:25] Implementing AI Agent's success

[54:18] Usability challenges in interfaces

[57:03] Themes in High-Performing AI Teams

[1:00:51] Wrap up

Matar Haller is the VP of Data & AI at ActiveFence, where her teams own the end-to-end automated detection of harmful content at scale, regardless of the abuse area or media type. The work they do here is engaging, impactful, and tough, and Matar is grateful for the people she gets to do it with.

AI For Good - Detecting Harmful Content at Scale // MLOps Podcast #246 with Matar Haller, VP of Data & AI at ActiveFence.

// Abstract

One of the biggest challenges facing online platforms today is detecting harmful content and malicious behavior. Platform abuse poses brand and legal risks, harms the user experience, and often represents a blurred line between online and offline harm. So how can online platforms tackle abuse in a world where bad actors are continuously changing their tactics and developing new ways to avoid detection?

// Bio

Matar Haller leads the Data & AI Group at ActiveFence, where her teams are responsible for the data, algorithms, and infrastructure that fuel ActiveFence’s ability to ingest, detect, and analyze harmful activity and malicious content at scale in an ever-changing, complex online landscape. Matar holds a Ph.D. in Neuroscience from the University of California at Berkeley, where she recorded and analyzed signals from electrodes surgically implanted in human brains. Matar is passionate about expanding leadership opportunities for women in STEM fields and has three children who surprise and inspire her every day.

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// Related Links

activefence.comhttps://www.youtube.com/@ActiveFence

--------------- ✌️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 Matar on LinkedIn: https://www.linkedin.com/company/11682234/admin/feed/posts/

Timestamps:

[00:00] Matar's preferred coffee

[00:13] Takeaways

[01:39] The talk that stood out

[06:15] Online hate speech challenges

[08:13] Evaluate harmful media API

[09:58] Content moderation: AI models

[11:36] Optimizing speed and accuracy

[13:36] Cultural reference AI training

[15:55] Functional Tests

[20:05] Continuous adaptation of AI

[26:43] AI detection concerns

[29:12] Fine-Tuned vs Off-the-Shelf

[32:04] Monitoring Transformer Model Hallucinations

[34:08] Auditing process ensures accuracy

[38:38] Testing strategies for ML

[40:05] Modeling hate speech deployment

[42:19] Improving production code quality

[43:52] Finding balance in Moderation

[47:23] Model's expertise: Cultural Sensitivity

[50:26] Wrap up

Catherine Nelson is a freelance data scientist and writer. She is currently working on the forthcoming O’Reilly book "Software Engineering for Data Scientists”.

Why All Data Scientists Should Learn Software Engineering Principles // MLOps podcast #245 with Catherine Nelson, a freelance Data Scientist.

A big thank you to LatticeFlow AI for sponsoring this episode! LatticeFlow AI - https://latticeflow.ai/

// Abstract

Data scientists have a reputation for writing bad code. This quote from Reddit sums up how many people feel: “It's honestly unbelievable and frustrating how many Data Scientists suck at writing good code.” But as data science projects grow, and because the job now often includes deploying ML models, it's increasingly important for DSs to learn fundamental SWE principles such as keeping your code modular, making sure your code is readable by other people, and so on. The exploratory nature of DS projects means that you can't be sure where you will end up at the start of a project, but there's still a lot you can do to standardize the code you write.

// Bio

Catherine Nelson is the author of "Software Engineering for Data Scientists", a guide for data scientists who want to level up their coding skills, published by O'Reilly in May 2024. She is currently consulting for GenAI startups and providing mentorship and career coaching to data scientists. Previously, she was a Principal Data Scientist at SAP Concur. She has extensive experience deploying NLP models to production and evaluating ML systems, and she is also a co-author of the book "Building Machine Learning Pipelines", published by O'Reilly in 2020. In her previous career as a geophysicist, she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Master's of Earth Sciences from Oxford University.

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// Related LinksSoftware Engineering for Data Scientists book by Catherine Nelson:https://learning.oreilly.com/library/view/software-engineering-for/9781098136192/https://www.amazon.com/Software-Engineering-Data-Scientists-Notebooks/dp/1098136209

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

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

Follow us on Twitter: @mlopscommunity

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

Connect with Catherine on LinkedIn: https://www.linkedin.com/in/catherinenelson1/

Timestamps:

[00:00] Catherine's preferred coffee

[00:15] Takeaways

[02:38] Meeting magic: Embracing serenity

[06:23] The Software Engineering for Data Scientists book

[10:41] Exploring ideas rapidly

[12:52] Bridging Data Science gaps

[16:17] Data poisoning concerns

[18:26] Transitioning from a data scientist to a machine learning engineer

[21:53] Rapid Prototyping vs Thorough Development

[23:45] Data scientists take ownership

[25:53] Data scientists' role balance

[30:30] Understanding system design process

[36:00] Data scientists and Kubernetes

[41:33 - 43:03] LatticeFlow AI Ad

[43:05] The Future of Data Science

[45:09] Data scientists analyzing models

[46:46] Tools gaps in prompt tracking

[50:44] Learnings from writing the book

Meta GenAI Infra Blog Review // Special MLOps Podcast episode by Demetrios.

[00:00] Meta handles trillions of AI model executions

[07:01] Meta creating AGI, ethical and sustainable

[08:13] Concerns about energy use in training models

[12:22] Network, hardware, and job optimization for reliability

[17:21] Highlights of Arista and Nvidia hardware architecture

[20:11] Meta's clusters optimized for efficient fabric

[24:40] Varied steps, careful checkpointing in AI training

[28:46] Meta is maintaining huge GPU clusters for AI

[29:47] AI training is faster and more demanding

[35:27] Ops planner orchestrates a million operations and reduces maintenance

[37:15] Ops planner ensures safety and well-tested changes

Sean Wei, the CEO and co-founder of RealChar, shares his journey from working in the autonomous vehicle industry to creating an open-source voice assistant project called RealChar, which eventually evolved into Rivia, a voice AI assistant focused on managing personal phone calls.

The Future of AI and Consumer Empowerment // MLOps podcast #244 with Shaun Wei, CEO & Co-Founder of RealChar.

A big thank you to LatticeFlow for sponsoring this episode! LatticeFlow - https://latticeflow.ai/

// Abstract

Explore the groundbreaking work RealChar is doing with its consumer application, Rivia. This discussion focuses on how Rivia leverages Generative AI and Traditional Machine Learning to handle mundane phone calls and customer service interactions, aiming to free up human time for more meaningful tasks. The product, currently in beta, embodies a forward-thinking approach to AI, where the technology offloads day-to-day burdens like scheduling appointments and making calls.

// Bio

Shaun Wei is a well-connected technology professional with a rich background in developing and analyzing artificial intelligence systems. In 2018, Shaun played a pivotal role in the advent and deployment of Google Duplex, a remarkable AI capable of handling natural conversations and performing tasks such as booking hair salon appointments and restaurant reservations via telephone. His involvement wasn't just limited to the developmental side; Shaun also uniquely positioned himself on the receiving end, gathering insights by interviewing users directly impacted by the technology. This dual perspective has enabled Shaun to grasp both the technical underpinnings and the human-centric applications of AI, making him a valuable asset in the tech industry.

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// Related Links

https://www.rivia.tech/

https://realchar.ai/

--------------- ✌️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 Shaun on LinkedIn: https://www.linkedin.com/in/shaunwei/

Timestamps:

[00:00] Shaun's preferred coffee

[00:28] Takeaways

[03:30] Please like, share, leave a review, and subscribe to our MLOps channels!

[03:57] AI in Production: Challenges & Insights

[06:13] AI Scheduling and Assistance

[08:00] Technical Challenges in AI

[12:36 - 14:06] LatticeFlow Ad

[14:09] Handling Challenges in AI

[15:52] Learning driving and technical aspects

[19:04] Self-Driving Cars: Multimodal Integration

[23:41] Processing data with Transformers

[26:46] Real-time phone data gathering

[30:49] Real-time observability in AI

[35:09] Time to first token

[37:26] Preferred vs. Dynamic Model Selection

[40:12] Event-driven architecture basics

[42:06] Navigating challenges together

[44:02] Challenges with Inconsistent Responses

[45:40] Importance of product reliability

[47:47] Training Data and Model Performance

[50:02] Exploring AI in Customer Service

[51:34] Navigating challenges in AI

[53:15] Excited Launch Strategy Advice

[57:10] Wrap up

Join us at our first in-person conference today, all about AI Quality: https://www.aiqualityconference.com/

ML and AI as Distinct Control Systems in Heavy Industrial Settings // MLOps podcast #243 with Richard Howes, CTO of Metaformed.

Richard Howes is a dedicated engineer who is passionate about control systems, whether it be embedded systems, industrial automation, or AI/ML in a business application.

Huge thank you to AWS for sponsoring this episode. AWS - https://aws.amazon.com/

// Abstract

How can we balance the need for safety, reliability, and robustness with the extreme pace of technology advancement in heavy industry? The key to unlocking the full potential of data will be to have a mixture of experts, both from an AI and human perspective, to validate anything from a simple KPI to a Generative AI Assistant guiding operators throughout their day. The data generated by heavy industries like agriculture, oil & gas, forestry, real estate, civil infrastructure, and manufacturing is underutilized and struggles to keep up with the latest and greatest - and for good reason. They provide the shelter we live and work in, the food we eat, and the energy to propel society forward. Compared to the pace of AI innovation, they move slowly, have extreme consequences for failure, and typically involve a significant workforce. During this discussion, we will outline the data ready to be utilized by ML, AI, and data products in general, as well as some considerations for creating new data products for these heavy industries. To account for complexity and uniqueness throughout the organization, it is critical to engage operational staff, ensure safety is considered from all angles, and build adaptable ETL needed to bring the data to a usable state.

// Bio

Richard Howes is a dedicated engineer who is passionate about control systems, whether it be embedded systems, industrial automation, or AI/ML in a business application. All of these systems require a robust control philosophy that outlines the system, its environment, and how the controller should function within it. Richard has a bachelor's of Electrical Engineering from the University of Victoria, where he specialized in industrial automation and embedded systems. Richard is primarily focused on the heavy industrial sectors like energy generation, oil & gas, pulp/paper, forestry, real estate, and manufacturing. He works on both physical process control and business process optimization using the control philosophy principles as a guiding star.

Richard has been working with industrial systems for over 10 years, designing, commissioning, operating, and maintaining automated systems. For the last 5 years, Richard has been investing time into the data and data science-related disciplines, bringing the physical process as close as possible to the business, taking advantage of disparate data sets throughout the organization. Now with the age of AI upon us, he is focusing on integrating this technology safely, reliably, and with distinct organizational goals and ROI.

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// Related Links

AWS Trainium: https://aws.amazon.com/machine-learning/trainium/

AWS Inferentia: https://aws.amazon.com/machine-learning/inferentia/

--------------- ✌️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 Richard on LinkedIn: https://www.linkedin.com/in/richardhowes/

Join us at our first in-person conference on June 25, all about AI Quality: https://www.aiqualityconference.com/

Accelerating Multimodal AI // MLOps podcast #241 with Ethan Rosenthal, Member of Technical Staff of Runway.

Huge thank you to AWS for sponsoring this episode. AWS - https://aws.amazon.com/

// Abstract

We’re still trying to figure out systems and processes for training and serving “regular” machine learning models, and now we have multimodal AI to contend with! These new systems present unique challenges across the spectrum, from data management to efficient inference. I’ll talk about the similarities, differences, and challenges that I’ve seen by moving from tabular machine learning to large language models to generative video systems. I’ll also talk about the setups and tools that I have seen work best for supporting and accelerating both the research and productionization process.

// Bio

Ethan works at Runway Building Systems for media generation. Ethan's work generally straddles the boundary between research and engineering without falling too hard on either side. Prior to Runway, Ethan spent 4 years at Square. There, he led a small team of AI Engineers training large language models for Conversational AI. Before Square, Ethan freelanced, consulted, and worked at a couple of e-commerce startups. Ethan found his way into tech by way of a Physics PhD.

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// Related LinksWebsite: https://www.ethanrosenthal.com

Ethan's mangum opus: https://www.ethanrosenthal.com/2020/08/25/optimal-peanut-butter-and-banana-sandwiches/

Real-time Model Inference in a Video Streaming Environment // Brannon Dorsey // Coffee Sessions #98: https://youtu.be/TNO6rYwP3yg

Feature Stores for Self-Service Machine Learning: https://www.ethanrosenthal.com/2021/02/03/feature-stores-self-service/

Gen-1: The Next Step Forward for Generative AI: https://research.runwayml.com/gen1

Machine Learning: The High Interest Credit Card of Technical Debt by D. Sculley et al.: https://research.google/pubs/machine-learning-the-high-interest-credit-card-of-technical-debt/

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

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

Follow us on Twitter: @mlopscommunity

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Catch all episodes, blogs, newsletters, and more: https://mlops.community/

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

Connect with Ethan on LinkedIn: https://bsky.app/profile/ethanrosenthal.com

Timestamps:

[00:00] Ethan's preferred coffee

[00:11] Takeaways

[02:07] Falling into LLMs

[03:16] Advanced AI Tech Capabilities

[04:40] AI-powered video editing tool

[06:56] Transition to AI: Diffusion Models

[09:09] Multimodal Feature Store breakdown

[15:33] Multimodal Feature Stores Evolution

[18:09] Benefits of Multimodal Feature Store

[25:09] Centralized Training Data Repository

[27:33] Large-scale distributed training

[32:37 - 33:39] AWS Ad

[33:45] Dealing with researchers on productionizing

[43:52] Infrastructure for Researchers and Engineers

[47:04] Generative DevOps movement

[49:21] Structuring teams

[52:06] Multimodal Feature Stores Efficiency

[54:02] Wrap up

Join us at our first in-person conference on June 25, all about AI Quality: https://www.aiqualityconference.com/

Navigating the AI Frontier: The Power of Synthetic Data and Agent Evaluations in LLM Development // MLOps podcast #241 with Boris Selitser, Co-Founder and CTO/CPO of Okareo.

A big thank you to LatticeFlow for sponsoring this episode! LatticeFlow - https://latticeflow.ai/

// Abstract

Explore the evolving landscape of building LLM applications, focusing on the critical roles of synthetic data and agent evaluations. Discover how synthetic data enhances model behavior description, prototyping, testing, and fine-tuning, driving robustness in LLM applications. Learn about the latest methods for evaluating complex agent-based systems, including RAG-based evaluations, dialog-level assessments, simulated user interactions, and adversarial models. This talk delves into the specific challenges developers face and the tradeoffs involved in each evaluation approach, providing practical insights for effective AI development.

// Bio

Boris is the Co-Founder and CTO/CPO at Okareo. Okareo is a full-cycle platform for developers to evaluate and customize AI/LLM applications. Before Okareo, Boris was Director of Product at Meta/Facebook, leading teams building internal platforms and ML products. Examples include a copyright classification system across the Facebook apps and an engagement platform for over 200K developers, 500K+ creators, and 12M+ Oculus users. Boris has a bachelor’s in Computer Science from UC Berkeley.

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// Related Links

https://docs.okareo.com/blog/data_loophttps://docs.okareo.com/blog/agent_eval

The Real E2E RAG Stack // Sam Bean // MLOps Podcast #217 - https://youtu.be/8uZst7pgOw0

RecSys at Spotify // Sanket Gupta // MLOps Podcast #232 - https://youtu.be/byH-ARJA4gk

--------------- ✌️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 Boris on LinkedIn: https://www.linkedin.com/in/selitser/

Timestamps:

[00:00] Boris' preferred coffee

[00:37] Takeaways

[02:32] Please like, share, leave a review, and subscribe to our MLOps channels!

[02:48] Software Engineering and Data Science

[06:01] AI Transformative Potential Explained

[10:31] Prompt Injection Protection Strategies

[17:03] Agent's metrics for Jira

[24:11] Data and Metrics Evolution

[27:54] Evaluation Focus Enhances Systems

[31:22 - 32:52] LatticeFlow AD

[32:55] Custom Evaluation and Synthetic Data

[36:23] Synthetic data for expansion, evaluation, and map

[41:06] Diverse agents' personalities for readiness

[44:25] Agent functions

[46:17] Optimizing Routing Agents

[50:04] Adapting to tool output for decision-making

[52:56] Agent framework evolution

[55:41] Agent framework for delivering value

[57:03] Wrap up

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/

Join us at our first in-person conference on June 25, all about AI Quality: https://www.aiqualityconference.com/

Miguel Fierro is a Principal Data Science Manager at Microsoft and holds a PhD in robotics.

From Robotics to Recommender Systems // MLOps Podcast #240 with Miguel Fierro, Principal Data Science Manager at Microsoft.

Huge thank you to Zilliz for sponsoring this episode. Zilliz - https://zilliz.com/.

// Abstract

Miguel explains the limitations and considerations of applying ML in robotics, contrasting its use against traditional control methods that offer exactness, which ML approaches generally approximate. He discusses the integration of computer vision and machine learning in sports for player movement tracking and performance analysis, highlighting collaborations with European football clubs and the role of artificial intelligence in strategic game analysis, akin to a coach's perspective.

// Bio

Miguel Fierro is a Principal Data Science Manager at Microsoft Spain, where he helps customers solve business problems using artificial intelligence. Previously, he was CEO and founder of Samsamia Technologies, a company that created a visual search engine for fashion items, allowing users to find products using images instead of words, and founder of the Robotics Society of Universidad Carlos III, which developed different projects related to UAVs, mobile robots, humanoid robots, and 3D printers. Miguel has also worked as a robotics scientist at Universidad Carlos III of Madrid (UC3M) and King’s College London (KCL) and has collaborated with other universities like Imperial College London and IE University in Madrid. Miguel is an Electrical Engineer from UC3M, PhD in robotics by UC3M in collaboration with KCL, and graduated from MIT Sloan School of Management.

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// Related Links

Website: https://miguelgfierro.com

GitHub: https://github.com/miguelgfierro/

RecSys at Spotify // Sanket Gupta // MLOps Podcast #232 - https://youtu.be/byH-ARJA4gk

Recommenders joins LF AI & Data as new Sandbox project: https://cloudblogs.microsoft.com/opensource/2023/10/10/recommenders-joins-lf-ai-data-as-new-sandbox-project/

--------------- ✌️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 Miguel on LinkedIn: https://www.linkedin.com/in/miguelgfierro/

Timestamps:

[00:00] Miguel's preferred coffee

[00:11] Takeaways

[02:25] Robotics

[10:44] Simpler solutions over ML

[15:11] Robotics and Computer Vision

[19:15] Basketball object detection

[22:43 - 23:50] Zilliz Ad

[23:51] Mr. Recommenders and Recommender systems' common patterns

[31:35] Embeddings and Feature Stores

[42:34] Experiment ROI for leadership

[47:17] Hi ROI investments

[51:13] LLMs in Recommender Systems

[54:51] Wrap up