Agentic Conversations (formally mlops.community)

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

Amritha Arun Babu Mysore has been an expert in the field of consumer electronics, software products, and online marketplaces for the past 15 years. She has experience developing supply chains from the ground up, delivering AI-based products to millions of users, and advocating for ethical AI across Amazon, Wayfair, Salesforce, and NetApp.

Abhik Choudhury is a Senior Analytics Managing Consultant and Data Scientist with 11 years of experience in designing and implementing scalable data solutions for organizations across various industries.

Huge thank you to  @latticeflow  for sponsoring this episode. LatticeFlow - https://latticeflow.ai/

MLOps podcast #221 with Amritha Arun Babu Mysore, ML Product Leader at Klaviyo and Abhik Choudhury, Managing Consultant, Analytics at IBM, MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases.

// Abstract

As machine learning (ML) and large language models (LLMs) continue permeating industries, robust ML infrastructure and operations (MLOps) are crucial to deploying these AI systems successfully. This podcast discusses best practices for building reusable, scalable, and governable ML Ops architectures tailored to ML and LLM use cases.

// Bio

Amritha Arun Babu Mysore

Amritha is an accomplished technology leader with over 12 years of experience spearheading product innovation and strategic initiatives at both large enterprises and rapid-growth startups.

Leveraging her background in engineering, supply chain, and business, Amritha has led high-performing teams to deliver transformative solutions solving complex challenges. She has driven product road mapping, requirements analysis, system design, and launch execution for advanced platforms in domains like machine learning, logistics, and e-commerce.

Abhik Choudhury

Abhik is a Senior Analytics Managing Consultant and Data Scientist with 11 years of experience in designing and implementing scalable data solutions for organizations across various industries. Throughout his career, Abhik developed a strong understanding of AI/ML, Cloud computing, database management systems, data modeling, ETL processes, and Big Data Technologies. Abhik's expertise lies in leading cross-functional teams and collaborating with stakeholders at all levels to drive data-driven decision-making in longitudinal pharmacy and medical claims and wholesale drug distribution areas.

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// MLOps Swag/Merch

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// Related LinksAI Quality in Person Conference in collaboration with Kolena: https://www.aiqualityconference.com/

LatticeFlow website: https://latticeflow.ai/

--------------- ✌️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 Abhik on LinkedIn: https://www.linkedin.com/in/abhik-choudhury-35450058

Connect with Amritha on LinkedIn: https://www.linkedin.com/in/amritha-arun-babu-a2273729/

Timestamps:

[00:00] Amritha and Abhik's preferred coffee

[00:30] Takeaways

[02:42] Please like, share, leave a review, and subscribe to our MLOps channels!

[04:46] Abhik's background

[05:38] Amritha's TLDR Journey

[06:29] New Challenges in MLOps

[08:49] ML Workflow Maturity Levels

[11:25] Dev & Deploy Process Overview

[14:51] Maturity Metrics and Progress

[18:18] Automated ML Comparison: Semi vs. Fully

[22:40] LLMs vs Traditional ML

[28:38] Design MLOps for Usability

[30:34] LatticeFlow Ad

[33:30] Metrics Impact Assessment

[35:31] Spark Learning Risks Analysis

[36:37] MLOps User Journeys

[45:44] AI Engineer Transition Guide

[52:09] Data Compliance Challenge

[55:00] Wrap up

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

Demetrios Brinkmann is the founder of the MLOps Community. Brinkmann fell into the Machine Learning Operations world, and since then, has interviewed the leading names around MLOps, Data Science, and Machine Learning.

Huge thank you to Weights & Biases for sponsoring this episode. Weights & Biases - https://wandb.ai/site

MLOps podcast #220 with our very own Founder of MLOps Community, Demetrios Brinkmann, Looking Back on 4 Years of the MLOps Community.

// Abstract

In this lively podcast episode, Mihail Eric hosts Demetrios Brinkmann, the founder of the MLOps Community, discussing its origin, structure, and challenges.

Demetrios shares amusing tales of job hunting on LinkedIn and building the community despite lacking technical expertise, emphasizing the value of sharing and humor.

They delve into the practicalities of hosting events, transitioning from self-funded to sponsorship-based, and tease upcoming activities with renowned speakers.

Mihail and Demetrios explore job dynamics, the importance of sustained relationships, and diverse engagement methods like newsletters and volunteering.

Demetrios reflects on his journey to Germany post-company closure, envisioning a global hub for AI learning, embodying the community's mission.

// Bio

At the moment, Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building Lego houses with his daughter.

// MLOps Jobs board

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// MLOps Swag/Merch

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

// Related LinksAI Quality in Person Conference in collaboration with Kolena: https://www.aiqualityconference.com/

Weights & Biases Free Course: https://wandb.ai/telidavies/ml-news/reports/Introducing-W-B-MLOps-Courses-Free-Course-Effective-MLOps-Model-Development--VmlldzozMDk2ODA2

What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2: https://youtu.be/l52sRMVPVk0

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

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

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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 Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/

Timestamps:

[00:00] Demetrios preferred coffee and bizarre listening

[01:44] The MLOps Community Brainchild

[04:22] The MLOps Community today

[07:15] AI Quality in Person Conference on June 25th!

[08:42] Community Quality

[10:00] Community Learnings and the Genesis

[17:55] The 600 Mark

[20:15] The Feedback form

[22:52] Demetrios' Journey and Learnings

[29:01] Building full tolerance

[29:55] Weights & Biases Free Course Ad

[34:52] Building community involvement for professional success and networking

[38:52] Balance in Community Growth

[43:56] Collection of volunteers

[49:00] Events Challenges

[53:28] The future of MLOps Community

[59:40] "Caveman" lifestyle choice

[1:00:45] Stronger Hallucinogen

[1:02:30] Wrap up

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

Huge thank you to ⁠Databricks⁠ AI for sponsoring this episode.

Bandish Shah is an Engineering Manager at MosaicML/Databricks, where he focuses on making generative AI training and inference efficient, fast, and accessible by bridging the gap between deep learning, large-scale distributed systems, and performance computing.

Davis Blalock is a Research Scientist and the first employee of Mosaic ML: a GenAI startup acquired for $1.3 billion by Databricks.MLOps podcast #219 with Databricks' Engineering Manager, Bandish Shah and Research Scientist Davis Blalock, The Art and Science of Training Large Language Models.

// Abstract

What's hard about language models at scale? Turns out...everything. MosaicML's Davis and Bandish share war stories and lessons learned from pushing the limits of LLM training and helping dozens of customers get LLMs into production. They cover what can go wrong at every level of the stack, how to make sure you're building the right solution, and some contrarian takes on the future of efficient models.

// Bio

Bandish Shah

Bandish Shah is an Engineering Manager at MosaicML/Databricks, where he focuses on making generative AI training and inference efficient, fast, and accessible by bridging the gap between deep learning, large-scale distributed systems, and performance computing. Bandish has over a decade of experience building systems for machine learning and enterprise applications. Prior to MosaicML, Bandish held engineering and development roles at SambaNova Systems where he helped develop and ship the first RDU systems from the ground up, and Oracle where he worked as an ASIC engineer for SPARC-based enterprise servers.

Davis Blalock

Davis Blalock is a research scientist at MosaicML. He completed his PhD at MIT, advised by Professor John Guttag. His primary work is designing high-performance machine learning algorithms. He received his M.S. from MIT and his B.S. from the University of Virginia. He is a Qualcomm Innovation Fellow, NSF Graduate Research Fellow, and Barry M. Goldwater Scholar.

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// Related Links

AI Quality In-person Conference: AI Quality in Person Conference: https://www.aiqualityconference.com/

Website: http://databricks.com/Davis Summarizes Papers ⁠Newsletter signup linkDavis' Newsletters: Learning to recognize spoken words from five unlabeled examples in under two seconds: https://arxiv.org/abs/1609.09196

Training on data at 5GB/s in a single thread: https://arxiv.org/abs/1808.02515

Nearest-neighbor searching through billions of images per second in one thread with no indexing: https://arxiv.org/abs/1706.10283

Multiplying matrices 10-100x faster than a matrix multiply (with some approximation error): https://arxiv.org/abs/2106.10860

Hidden Technical Debt in Machine Learning Systems: https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf

--------------- ✌️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 Davis on LinkedIn: https://www.linkedin.com/in/dblalock/

Connect with Bandish on LinkedIn: https://www.linkedin.com/in/bandish-shah/

// Abstract

Frank Liu is the Director of Operations & ML Architect at Zilliz, where he serves as a maintainer for the Towhee open-source project.

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

Dr. Petar Tsankov is a researcher and entrepreneur in the field of Computer Science and Artificial Intelligence.

MLOps podcast #218 with Petar Tsankov, Co-Founder and CEO at LatticeFlow AI, A Decade of AI Safety and Trust.

// Abstract

Embark on a decade-long journey of AI safety and trust. This conversation delves into key areas such as the transition towards more adversarial environments, the challenges in model robustness and data relevance, and the necessity of third-party assessments in the face of companies' reluctance to share data. It further covers current shifts in AI trends, emphasizing problems associated with biases, errors, and a lack of transparency, particularly in generative AI and third-party models. This episode explores the origins and mission of LatticeFlow AI to provide trustworthy solutions for new AI applications, encompassing their participation in safety competitions and their focus on proving the properties of neural networks. The profound conversation concludes by touching upon the importance of data quality, robustness checks, application of emerging standards like ISO 5259 and ISO 40001, and a peek into the future of AI regulation and certifications. Safe to say, it's a must-listen for anyone passionate about trust and safety in AI.

// Bio

Co-founder & CEO at LatticeFlow AI, building the world's first product enabling organizations to build performant, safe, and trustworthy AI systems.

Before starting LatticeFlow AI, Petar was a senior researcher at ETH Zurich working on the security and reliability of modern systems, including deep learning models, smart contracts, and programmable networks.

// MLOps Jobs board

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// MLOps Swag/Merch

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

// Related Links

Website: https://latticeflow.ai/

ERAN, the world's first scalable verifier for deep neural networks: https://github.com/eth-sri/eran

VerX, the world's first fully automated verifier for smart contracts: https://verx.ch

Securify, the first scalable security scanner for Ethereum smart contracts: https://securify.ch

DeGuard, de-obfuscates Android binaries: http://apk-deguard.com

SyNET, the first scalable network-wide configuration synthesis tool: https://synet.ethz.ch

--------------- ✌️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 Petar on LinkedIn: https://www.linkedin.com/in/petartsankov/

Timestamps:

[00:00] Petar's preferred coffee

[00:29] Takeaways

[03:15] Shout out to LatticeFlow for sponsoring this episode!

[03:22] Please like, share, leave a review, and subscribe to our MLOps channels!

[03:42] Expansion

[05:16] Zurich ETH

[07:06] AI Safety

[09:24] Optimizing one metric, no fixed data sets

[12:19] Trust life-changing issues

[14:59] So much interest in GenAI

[16:45] Explosion of GenAI Trust and Safety

[21:14] Red Teaming

[25:22] Trustworthy AI in Industry

[27:43] DataOps Challenges

[33:42] Trusting Third-Party Models

[37:00] Testing Open Source Models

[41:41] Specialized ML for Leasing

[43:04] Regulation and Financial Incentives

[45:30] Regulations Drive Innovation Balance

[47:23] Regulations vs Certification: Voluntary Proof

[52:24] Workflow Transparency: Trust & Efficiency

[53:20] Engineers Balance Compliance Risks

[54:53] Pushing Deep Learning Limits

[57:31] Wrap up

Thank you to Zilliz, our wonderful sponsors of this episode create some amazing stuff with Zilliz RAG - https://zilliz.com/vector-database-use-cases/llm-retrieval-augmented-generation

Sam Bean is a seasoned AI and machine learning expert, specializing in Large Language Models (LLMs) and search tech.

With a computer science background and a drive for innovation, Sam leads the team at Rewind AI in leveraging advanced tech to tackle complex challenges.MLOps podcast #217 with Sam Bean, Software Engineer (Applied AI) at Rewind.ai, The Real E2E RAG Stack.

// Abstract

What does a fully operational LLM + Search stack look like when you're running your own retrieval and inference infrastructure? What does the flywheel really mean for RAG applications? How do you maintain the quality of your responses? How do you prune/dedupe documents to maintain your document quality?

// Bio

Sam has been training, evaluating, and deploying production-grade inference solutions for language models for the past 2 years at You.com. Prior to that, he built personalization algorithms at StockX.

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// Related Links

Website: https://github.com/sam-h-bean/

REinforced Self Training (REST) - https://arxiv.org/pdf/2308.08998.pdf

REST meets REACT - https://arxiv.org/pdf/2312.10003.pdf

--------------- ✌️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 Sam on LinkedIn: https://www.linkedin.com/in/samuel-h-bean/

Timestamps:

[00:00] Sam's preferred coffee

[00:11] Takeaways

[03:52] A competitive coding pinball player

[07:18] Sam's MLOps journey

[10:33] Search Challenges with ML

[15:04] Expensive evaluation

[21:04] Labeling Parties Boost Data Quality

[24:10] Zeno's Paradox of Motion

[25:51] Sam's job at Rewind AI

[29:35] Multimodal RAG

[30:59 - 32:06] Zilliz Ad

[32:07] University of Prague paper leak

[36:38] Signals behind the scenes

[39:28] Content Over Metadata Approach

[43:22] Optionality around evaluation and search

[48:35] Incremental Robustness Building

[51:33] Solid Foundations for Success

[53:42] Production RAGs

[1:00:06] Thoughts on DSPy

[1:05:40] Using DSPy in Production

[1:08:26] Wrap up

Anass Bensrhir is the Associate Partner of McKinsey & Company, Casablanca. Anu Arora is the Principal Data Engineer at McKinsey & Company.

Check out mckinsey.com/quantumblackMLOps podcast #214 with QuantumBlack AI by McKinsey's Principal Data Engineer, Anu Arora and Associate Partner, Anass Bensrhir, Managing Data for Effective GenAI Application, brought to you by our Premium Brand Partner QuantumBlack AI by  @McKinsey.

// Abstract

Generative AI is poised to bring impact across all industries and business functions. Across industries, while many companies pilot GenAI, only a few have deployed GenAI use cases, e.g., retailers are producing videos to answer common customer questions using ChatGPT. A majority of organizations are facing challenges to industrialize and scale, with data being one of the biggest inhibitors. Organizations need to strengthen their data foundations, given that among leading organizations, 72% noted managing data among the top challenges preventing them from scaling impact. Furthermore, leaders noticed that +31% of their staff's time is spent on non-value-added tasks due to poor data quality and availability issues.

// Bio

Anu Arora

Data architect(~12 years) and have experience in Big data technologies, API development, building scalable data pipelines including DevOps and DataOps, and building GenAI solutions.

Anass Bensrhir

Anass leads QuantumBlack in Africa. He specializes in the Financial sector and helps organizations deliver successful large Data transformation programs.

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// Related Links

Website: https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients

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

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

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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 Anu on LinkedIn: https://uk.linkedin.com/in/anu-arora-072012

Connect with Anass on LinkedIn: https://www.linkedin.com/in/abensrhir/

Timestamps:

[00:00] Anass and Anu's preferred coffee

[00:35] Takeaways

[04:02] Please like, share, leave a review, and subscribe to our MLOps channels!

[04:09] Huge shout-out to our sponsor QuantumBlack!

[04:29] Anu's tech background

[06:31] Anass' tech background

[07:28] The landscape of data

[10:37] Dealing with unstructured data

[15:51] Data lakes and ETL processes

[22:19] Data Engineers' Heavy Workload

[29:49] Data privacy and PII in the new LLMs paradigm

[36:13] Balancing LLM Adoption Risk

[44:06] Effective LMS Implementation Strategy

[49:00] Decisions: Create or Wait

[50:39] Wrap up

Alex Volkov serves as the AI Evangelist with Weights & Biases, Host of ThursdAI, Founder and CEO of Targum, and AI Consultant GPU POOR Def. not an owl.

MLOps podcast #215 with Alex Volkov, AI Evangelist at Weights & Biases, Becoming an AI Evangelist.

// Abstract

Follow Alex's journey into the world of AI, from being interested in running his first AI models to founding an AI startup, running a successful weekly AI news podcast & newsletter, and landing a job with  @WeightsBiases.

// Bio

Alex Volkov is an AI Evangelist at Weights & Biases, celebrated for his expertise in clarifying the complexities of AI and advocating for its beneficial uses. He is the founder and host of ThursdAI, a weekly newsletter and podcast that explores the latest in AI, its practical applications, open-source, and innovation. With a solid foundation as an AI startup founder and 20 years in full-stack software engineering, Alex offers a deep well of experience and insight into AI innovation.

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// Related LinksEvaluation Survey: https://hq.yougot.us/primary/WebInterview/3AW6LW5D/Start

Website: https://thursdai.news

Alex on X (+X spaces are also there) - https://twitter.com/altryne/

ThursdAI podcast/newsletter - https://sub.thursdai.news

Denver local AI tinkerers meetup - https://denver-boulder.aitinkerers.org/

Weights & Biases Growth Team hack week review - https://www.youtube.com/watchInterview

w/ Crew AI creator Joao Moura - https://sub.thursdai.news/p/jan14-sunday-special-deep-dives

The Future of Search in the Era of Large Language Models // Saahil Jain // MLOps Podcast #150: https://youtu.be/hMoMvK89iog

DSPy: Transforming Language Model Calls into Smart Pipelines // Omar Khattab // MLOps Podcast #194: https://youtu.be/NoaDWKHdkHg

--------------- ✌️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 Alex on LinkedIn: https://www.linkedin.com/in/alex-volkov-/

Timestamps:

[00:00] Alex's preferred beverage

[00:17] Takeaways

[03:19] Take the Evaluation Survey!

[03:55] Alex's journey for the past 15 years

[08:10] Career moves

[15:15] Building communities

[20:02] AI/MLOps Growth in COVID

[27:23] Recent developments and insights

[31:58 - 33:03] WandB Ad

[33:54] Multimodal RAG and Lucid Dreaming

[39:55] Evaluation Practices in MLOps

[43:27] Evaluating AI models effectively

[52:52] Embedding models and updates

[56:14] AI model trade-offs

[1:01:13] Optimizing LLM user experience

[1:03:56] Perceived performance optimization

[1:05:45] Agents' hype and reality

[1:11:31] Exploring DSPy for evaluation

[1:14:13] Wrap up

// Abstract

From startups achieving significant value with minor capabilities to AI revolutionizing sales calls and raising sales by 30%, we explore a series of interesting real-world use cases. Understanding the objectives and complexities of various industries, exploring the challenges of launching products, and highlighting the vital integration of the human touch with technology, this episode is a treasure trove of insights.// BioGreg Kamradt - ModeratorGreg has mentored thousands of developers and founders, empowering them to build AI-centric applications. By crafting tutorial-based content, Greg aims to guide everyone from seasoned builders to ambitious indie hackers. Some of his popular works: 'Introduction to LangChain Part 1, Part 2' (+145K views), and 'How To Question A Book' featuring Pinecone (+115K Views). Greg partners with companies during their product launches, feature enhancements, and funding rounds. His objective is to cultivate not just awareness, but also a practical understanding of how to optimally utilize a company's tools. He previously led Growth @ Salesforce for Sales & Service Clouds in addition to being early on at Digits, a FinTech Series-C company.Agnieszka Mikołajczyk-BarełaSenior AI Engineer@Chaptr working on LLMs. PhD, author of datasets, scientific papers, and publications with over 1800 citations, holding numerous scholarships and awards. Daily, she conducts her research on her grant "Detecting and overcoming bias in data with explainable artificial intelligence" Preludium, awarded by Polish National Centre. She is a co-organizer of PolEval2021 and PolEval 2022 tasks with punctuation prediction and restoration.She organizes and actively contributes to the scientific community in her free time: she managed and led the team during the HearAI project focused on modeling Sign Language. A former organizer and a team leader at the open-source project. As an ML Expert, she supports the project "Susana" designed to detect and read product expiry dates to help the Blind "see".Jason LiuJason is a machine learning engineer and technical advisor. Arjun KannanArjun Kannan builds products, businesses, and teams. Currently building ResiDesk, bringing AI copilots to help real estate forecast renewals, reduce turnover, and hit their budget. Arjun built and led product and engineering functions at Climb Credit (serving 100k students, doubling loan growth for 3 years straight) and at BlackRock (creating $400mm in annual revenue), and helped build multiple startups and small companies before that.// Sign up for our Newsletter to never miss an event: https://mlops.community/join/// Watch all the conference videos here: https://home.mlops.community/home/collections// Check out the MLOps Community podcast: https://open.spotify.com/show/7wZygk3mUUqBaRbBGB1lgh?si=242d3b9675654a69// Read our blog: mlops.community/blog// Join an in-person local meetup near you: https://mlops.community/meetups/// MLOps Swag/Merch: https://mlops-community.myshopify.com/// Follow us on Twitter: https://twitter.com/mlopscommunity//Follow us on LinkedIn: https://www.linkedin.com/company/mlopscommunity/