Agentic Conversations (formally mlops.community)

Latest Episodes:

The Rise of Modern Data Management // Chad Sanderson // #226 Apr 23, 2024

Show notes

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

Chad Sanderson is passionate about data quality and fixing the muddy relationship between data producers and consumers. He is a former Head of Data at Convoy, a LinkedIn writer, and a published author. He lives in Seattle, Washington, and is the Chief Operator of the Data Quality Camp.

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

MLOps podcast #226 with Chad Sanderson, CEO & Co-Founder of Gable, The Rise of Modern Data Management.

// Abstract

In this session, Chad Sanderson, CEO of Gable.ai and author of the upcoming O’Reilly book: "Data Contracts," tackles the necessity of modern data management in an age of hyper iteration, experimentation, and AI. He will explore why traditional data management practices fail and how the cloud has fundamentally changed data development. The talk will cover a modern application of data management best practices, including data change detection, data contracts, observability, and CI/CD tests, and outline the roles of data producers and consumers. Attendees will leave with a clear understanding of modern data management's components and how to leverage them for better data handling and decision-making.

// Bio

Chad Sanderson, CEO of Gable.ai, is a prominent figure in the data tech industry, having held key data positions at leading companies such as Convoy, Microsoft, Sephora, Subway, and Oracle. He is also the author of the upcoming O'Reilly book, "Data Contracts,” and writes about the future of data infrastructure, modeling, and contracts in his newsletter “Data Products.”

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

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

// Related Links

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

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

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

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

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

Connect with Chad on LinkedIn: https://www.linkedin.com/in/chad-sanderson/

Timestamps:

[00:00] Chad's preferred coffee

[01:28] Takeaways

[01:38] Changes after a year

[05:13] Implementing data pipelines

[06:10] Wrong data pipelines Implement

[10:00] Promote book visibility strategies

[14:22] Data Detective Dashboard

[25:07 - 26:09] AWS Trainium and Inferentia ad

[27:56] Data lineage tracking solution

[34:30] Putting conditions

[38:47] Magic solution for productivity

[41:41] Surprising features

[43:55] Upstream all the way

[46:03] Data integrity challenges

[48:53] Other surprising features

[51:45] Interfering with DevOps & Data

[54:12] Data security and value

[56:15] AI Quality In-Person Conference

Beyond AGI, Can AI Help Save the Planet? // Patrick Beukema // #225 Apr 19, 2024

Show notes

Patrick Beukema has a Ph.D. in neuroscience and has worked on AI models for brain decoding, which analyzes the brain's activity to decipher what people are seeing and thinking.

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

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

MLOps podcast #225 with Patrick Beukema, Head / Technical Lead of the Environmental AI, Applied Science Organization at AI2, Beyond AGI, Can AI Help Save the Planet?

// Abstract

AI will play a central role in solving some of our greatest environmental challenges. The technology that we need to solve these problems is in a nascent stage -- we are just getting started. For example, the combination of remote sensing (satellites) and high-performance AI operating at a global scale in real-time unlocks unprecedented avenues to new intelligence. MLOPs are often overlooked on AI teams, and typically, there is a lot of friction in integrating software engineering best practices into the ML/AI workflow. However, performance ML/AI depends on extremely tight feedback loops from the user back to the model that enable high iteration velocity and ultimately continual improvement. We are making progress, but environmental causes need your help. Join us in fighting for sustainability and conservation.

// Bio

Patrick is a machine learning engineer and scientist with a deep passion for leveraging artificial intelligence for social good. He currently leads the environmental AI team at the Allen Institute for Artificial Intelligence (AI2). His professional interests extend to enhancing scientific rigor in academia, where he is a strong advocate for the integration of professional software engineering practices to ensure reliability and reproducibility in academic research. Patrick holds a Ph.D. from the Center for Neuroscience at the University of Pittsburgh and the Center for the Neural Basis of Cognition at Carnegie Mellon University, where his research focused on neural plasticity and accelerated learning. He applied this expertise to develop state-of-the-art deep learning models for brain decoding of patient populations at a startup, later acquired by BlackRock. His earlier academic work spanned research on recurrent neural networks, causal inference, and ecology and biodiversity.

// MLOps Jobs board

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// Related Links

Variety of relevant papers/talks/links on Patrick's website: https://pbeukema.github.io/

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

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

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

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

Connect with Patrick on LinkedIn: https://www.linkedin.com/in/plbeukema/

Timestamps:

[00:00] AI Quality Conference

[01:29] Patrick's preferred coffee

[02:00] Takeaways

[04:14] Learning how to learn journey

[07:04] Patrick's day-to-day

[08:39] Environmental AI

[11:07] Environmental AI models

[14:35] Nature Inspires Scientific Advances

[18:11] R&D

[24:58] Iterative Feedback-Driven Development

[26:37 - 28:07] LatticeFlow Ad

[33:58] Balancing Metrics for Success

[38:16] Model Retraining Pipeline

[44:11] Series Models: Versatility

[45:57] Edge Models Enhance Output

[50:22] Custom Models for Specific Data

[53:53] Wrap up

GenAI in Production - Challenges and Trends // Verena Weber // #224 Apr 17, 2024

Show notes

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

Verena Weber believes that GenAI is going to transform the way we work and interact with devices. Her mission is to help companies prepare for this transformation. She has strong expertise in NLP and over 7 years of experience in Machine Learning.

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

MLOps podcast #224 with Verena Weber, Generative AI Consultant at Verena Weber, GenAI in Production - Challenges and Trends.

// Abstract

The goal of this talk is to provide insights into challenges for Generative AI in production as well as trends aiming to solve some of these challenges. The challenges and trends Verena sees are: Model size and moving towards a mixture of experts architectures, context window - new breakthroughs for context lengths from unimodality to multimodality, next step large action models? regulation in the form of the EU AI Act. Verena uses the differences between Gemini 1.0 and Gemini 1.5 to exemplify some of these trends.

// Bio

Verena leverages GenAI in natural language to elevate business competitiveness and navigate its transformative impact. Her varied experience in multiple roles and sectors underpins her ability to extract business value from AI, blending deep technical expertise with strong business acumen. Post-graduation, she consulted in Data Science at Deloitte and then advanced her skills in NLP, Deep Learning, and GenAI as a Research Scientist at the Alexa team, Amazon. Passionate about gender diversity in tech, she mentors women to thrive in this field.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

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

// Related Links

Website: verenaweber.de

Zilliz - https://zilliz.com/

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

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

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

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

Connect with Verena on LinkedIn: https://www.linkedin.com/in/verena-weber-134178b9/

Timestamps:

[00:00] AI Quality Conference

[01:33] Verena's preferred coffee

[02:15] Takeaways

[06:33] Ski Person of Influence

[11:31] Verena's background in the last 5-10 years

[14:24] Tech Evolution: Rapid Transformation

[18:13] Working at Amazon and key challenges

[20:10] Research-inspired suggestions

[22:21] AI Updates Impact Workflows

[22:52] Alexa Query Distribution Analysis

[24:06] Innovative Solutions for Alexa

[25:27] Robust T5 Data Prompting

[27:38] Audio Data Quality Challenges

[28:21-29:28] Zilliz ad

[29:28] Alexa data transcription and data cleaning

[35:38] Considering needs, costs, and complexity

[37:44] ChatGPT is not ideal for classification

[39:32] Comparison of model building using TF, IDF

[45:08] Struggle to boost diversity in conference speakers

[47:30] Creating safe environments helps underrepresented individuals participate

[48:29] Wrap up

Introducing DBRX: The Future of Language Models // [Exclusive] Databricks Roundtable Apr 12, 2024

Show notes

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

MLOps Coffee Sessions Special episode with Databricks, Introducing DBRX: The Future of Language Models, fueled by our Premium Brand Partner, Databricks. DBRX is designed to be especially capable of a wide range of tasks and outperforms other open LLMs on standard benchmarks. It also promises to excel at code and math problems, areas where others have struggled. Our panel of experts will get into the technical nuances, potential applications, and implications of DBRx for businesses, developers, and the broader tech community. This session is a great opportunity to hear from insiders about how DBRX's capabilities can benefit you. // Bio Denny Lee - Co-host Denny Lee is a long-time Apache Spark™ and MLflow contributor, Delta Lake maintainer, and a Sr. Staff Developer Advocate at Databricks. A hands-on distributed systems and data sciences engineer with extensive experience developing internet-scale data platforms and predictive analytics systems. He has previously built enterprise DW/BI and big data systems at Microsoft, including Azure Cosmos DB, Project Isotope (HDInsight), and SQL Server. Davis Blalock Davis Blalock is a research scientist and the first employee at MosaicML. He previously worked at PocketSonics (acquired 2013) and completed his PhD at MIT, where he was advised by John Guttag. 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. He is also the author of Davis Summarizes Papers, one of the most widely-read machine learning newsletters. 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. Abhi Venigalla Abhi is an NLP architect working on helping organizations build their own LLMs using Databricks. Joined as part of the MosaicML team and used to work as a researcher at Cerebras Systems. Ajay Saini Ajay is an engineering manager at Databricks leading the GenAI training platform team. He was one of the early engineers at MosaicML (acquired by Databricks) where he first helped build and launch Composer (an open source deep learning training framework) and afterwards led the development of the MosaicML training platform which enabled customers to train models (such as LLMs) from scratch on their own datasets at scale. Prior to MosaicML, Ajay was co-founder and CEO of Overfit, an online personal training startup (YC S20). Before that, Ajay worked on ML solutions for ransomware detection and data governance at Rubrik. Ajay has both a B.S. and MEng in computer science with a concentration in AI from MIT. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.databricks.com/ Databricks DBRX: https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm --------------- ✌️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/

From MVP to Production // AI in Production Conference Apr 09, 2024

Show notes

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ // Abstract Dive into the challenges of scaling AI models from Minimum Viable Product (MVP) to full production. The panel emphasizes the importance of continually updating knowledge and data, citing examples like teaching AI systems nuanced concepts and handling brand name translations.

User feedback's role in model training, alongside evaluation steps like human annotation and heuristic-based assessment, was highlighted.

The speakers stressed the necessity of tooling for user evaluation, version control, and regular performance updates. Insights on in-house and external tools for annotation and evaluation were shared, providing a comprehensive view of the complexities involved in scaling AI models. // Bio Alex Volkov - Moderator 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. Eric Peter Product management leader and 2x founder with experience in enterprise products, data, and machine learning. Currently building tools for generative AI @Databricks. Donné Stevenson Focused on building AI-powered products that give companies the tools and expertise needed to harness to power of AI in their respective fields. Phillip Carter 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. Andrew Hoh Andrew Hoh is the President and Co-Founder of LastMile AI. Previously, he was a Group PM Manager at Meta AI, driving product for their AI Platform. Previously, he was the Product Manager for the Machine Learning Infrastructure team at Airbnb and a founding team member of Azure Cosmos DB, Microsoft Azure's distributed NoSQL database. He graduated with a BA in Computer Science from Dartmouth College. A big thank you to our Premium Sponsors, @Databricks and @baseten for their generous support! // 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/

Data Engineering in the Federal Sector // Shane Morris // #223 Apr 05, 2024

Show notes

Shane Morris is now a Senior Executive Advisor at Devis.

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

Huge thank you to @WeightsBiases for sponsoring this episode.

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

MLOps podcast #223 with Shane Morris, Senior Executive Advisor of Devis, Data Engineering in the Federal Sector.

// Abstract

Let's focus on autonomous systems rather than automation, and then super-narrow it down to smaller, cheaper, and more accessible autonomous systems.

// Bio

A former music and entertainment data and software person somehow moves into defense and national security, with hilarious and predictable results.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

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

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

Website: https://shanemorris.sucks

TikTok: https://www.tiktok.com/@shanemorrisdotsucks

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

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

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

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

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

Connect with Shane on LinkedIn: https://www.linkedin.com/in/shanetollmanmorris/

Timestamps:

[00:00] Shane's preferred coffee

[00:26] Takeaways

[02:10] Please like, share, leave a review, and subscribe to our MLOps channels!

[02:42] Shane's new job!

[04:22] Being multidimensional

[07:41] Music Data Analytics Influence

[13:17] Money in Technology vs Music

[17:36] Design Document Approval Process

[25:17] Moving on from Booz Allen Hamilton

[28:39] DataX

[30:54 - 31:52] Weights & Biases Free Courses ad

[32:07] Vision vs. Random Exploration

[33:50] FedRAMP Advocacy: Share Success

[37:15] Evangelizing tool efficacy

[39:25] Military vs Civilian Decision-making

[41:26] Strategy Alignment Assessment: Worthwhile Efforts

[43:23] The feedback loop

[45:00] Explaining Data Governance

[50:36] Free Data Analytics Curriculum

[57:58] The Heinz Buffer Ranch joke

[1:02:41] Wrap up

What Business Stakeholders Want to See from the ML Teams // Peter Guagenti // #222 Apr 02, 2024

Show notes

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

⁠Peter Guagenti⁠ is an accomplished business builder and entrepreneur with expertise in strategy, product development, marketing, sales, and operations. Peter has helped build multiple successful start-ups to exits, fueling high growth in each company along the way. He brings a broad perspective, deep problem-solving skills, the ability to drive innovation amongst teams, and a proven ability to convert strategy into action -- all backed up by a history of delivering results.

Huge thank you to AWS for sponsoring this episode.

AWS - https://aws.amazon.com/

MLOps podcast #222 with Peter Guagenti, President & CMO of Tabnine - What Business Stakeholders Want to See from the ML Teams.

// Abstract

Peter Guagenti shares his expertise in the tech industry, discussing topics from managing large-scale tech legacy applications and data experimentation to the evolution of the Internet. He returns to his history of building and transforming businesses, such as his work in the early 90s for People magazine's website and his current involvement in AI development for software companies. Guagenti discusses the use of predictive modeling in customer management and emphasizes the importance of re-architecting solutions to fit customer needs. He also delves deeper into the AI tools' effectiveness in software development and the value of maintaining privacy. Guagenti sees a bright future in AI democratization and shares his company's development of AI coding assistants. Discussing successful entrepreneurship, Guagenti highlights balancing technology and go-to-market strategies and the value of failing fast.

// Bio

Peter Guagenti is the President and Chief Marketing Officer at Tabnine. Guagenti is an accomplished business leader and entrepreneur with expertise in strategy, product development, marketing, sales, and operations. He most recently served as chief marketing officer at Cockroach Labs, and he previously held leadership positions at SingleStore, NGINX (acquired by F5 Networks), and Acquia (acquired by Vista Equity Partners). Guagenti also serves as an advisor to a number of visionary AI and data companies, including DragonflyDB, Memgraph, and Treeverse.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

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

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

Measuring the impact of GitHub Copilot Survey: https://resources.github.com/learn/pathways/copilot/essentials/measuring-the-impact-of-github-copilot/

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

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

AI coding assistants: 8 features enterprises should seek: https://www.infoworld.com/article/3694900/ai-coding-assistants-8-features-enterprises-should-seek.html

Careers at Tabnine: https://www.tabnine.com/careers

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

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

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

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

Connect with Peter on LinkedIn: https://www.linkedin.com/in/peterguagenti/

MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases // Amritha Arun Babu & Abhik Choudhury // #221 Mar 29, 2024

Show notes

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.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

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

// 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

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

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

4 Years of the MLOps Community // Demetrios Brinkmann // #220 Mar 26, 2024

Show notes

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

jobs.mlops.community

// 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

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

The Art and Science of Training LLMs // Bandish Shah and Davis Blalock // #219 Mar 22, 2024

Show notes

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

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