Agentic Conversations (formally mlops.community)

Mohamed Elgendy is the Co-Founder & CEO at Kolena. Additionally, Mohamed Elgendy has had 1 past job as the Director of Product and Engineering at Synapse Technology Corporation.

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

MLOps podcast #228 with Mohamed Elgendy, Co-founder & CEO of Kolena Inc., What is AI Quality?

// Abstract

Delve into the multifaceted concept of AI Quality. Demetrios and Mo explore the idea that AI quality is dependent on the specific domain, equitable to the difference in desired qualities between a $1 pen and a $100 pen. Mo underscores the performance of a product being in sync with its intended functionality and the absence of unknown risks as the pillars of AI Quality. They emphasize the need for comprehensive quality checks and adaptability of standards to differing product traits. Issues affecting edge deployments, like latency, are also highlighted. A deep dive into the formation of gold standards for AI, the nuanced necessities for various use cases, and the paramount need for collaboration among AI builders, regulators, and infrastructure firms form the core of the discussion. Elgendy brings to light their ambitious AI Quality Conference, aiming to set tangible, effective, but innovation-friendly Quality standards for AI. The dialogue also accentuates the urgent need for diversification and representation in the tech industry, the variability of standards and regulations, and the pivotal role of testing in AI and machine learning. The episode concludes with an articulate portrayal of how enhanced testing can streamline the entire process of machine learning.

// Bio

Mohamed is the Co-founder & CEO of Kolena and the author of the book “Deep Learning for Vision Systems”. Previously, he built and managed AI/ML organizations at Amazon, Twilio, Rakuten, and Synapse. Mohamed regularly speaks at AI conferences like Amazon's DevCon, O'Reilly's AI conference, and Google's I/O.

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Website: www.kolena.io

Deep Learning for Vision Systems book: https://www.amazon.com/Learning-Vision-Systems-Mohamed-Elgendy/dp/1617296198/

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

Connect with Mo on LinkedIn: https://www.linkedin.com/in/moelgendy/

Timestamps:

[00:00] Mo's preferred coffee

[00:07] Takeaways

[02:52] See you all in San Francisco on June 25!

[03:04] Please like, share, leave a review, and subscribe to our MLOps channels!

[03:22] AI Quality in Mo's eyes

[08:36] Quality Standards for Software

[14:11] Common Chatbot Functionality

[19:20] The Birth of Innovation

[24:27] Transforming Insights into Standards

[30:27] Testing: One step to quality

[34:58] Two different data points to be harmonized

[37:29] Model cards

[39:12] Test Coverage Democratizes Collaboration

[42:55] Representation matters

[44:50] Wrap up

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

Simon Karasik⁠ is a proactive and curious ML Engineer with 5 years of experience. Developed & deployed ML models at WEB and Big scale for Ads and Tax.Huge thank you to Nebius AI for sponsoring this episode. Nebius AI - https://nebius.ai/

MLOps podcast #228 with Simon Karasik, Machine Learning Engineer at Nebius AI, Handling Multi-Terabyte LLM Checkpoints.

// Abstract

The talk provides a gentle introduction to the topic of LLM checkpointing: why is it hard, and how big are the checkpoints? It covers various tips and tricks for saving and loading multi-terabyte checkpoints, as well as the selection of cloud storage options for checkpointing.

// Bio

Full-stack Machine Learning Engineer, currently working on infrastructure for LLM training, with previous experience in ML for Ads, Speech, and Tax.

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

Connect with Simon on LinkedIn: https://www.linkedin.com/in/simon-karasik/

Timestamps:

[00:00] Simon's preferred beverage

[01:23] Takeaways

[04:22] Simon's tech background

[08:42] Zombie models garbage collection

[10:52] The road to LLMs

[15:09] Trained models Simon worked on

[16:26] LLM Checkpoints

[20:36] Confidence in AI Training

[22:07] Different Checkpoints

[25:06] Checkpoint parts

[29:05] Slurm vs Kubernetes

[30:43] Storage choices lessons

[36:02] Paramount components for setup

[37:13] Argo workflows

[39:49] Kubernetes node troubleshooting

[42:35] Cloud virtual machines have pre-installed mentoring

[45:41] Fine-tuning

[48:16] Storage, networking, and complexity in network design

[50:56] Start simple before advanced; consider model needs.

[53:58] Join us at our first in-person conference on June 25, all about AI Quality

Sol Rashidi is an esteemed executive, leader, and influencer within the AI, Data, and Technology space.  Having helped IBM launch Watson in 2011 as one of the earliest world applications of Artificial Intelligence, Sol has pioneered some of the early advancements in space. 

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 - http://wandb.me/courses_mlops

MLOps podcast #227 with Sol Rashidi, CEO & Co-Founder of ExecutiveAI, Leading Enterprise Data Teams.

//Abstract

In the dynamic landscape of MLOps and data leadership, Sol shares invaluable insights on building successful teams and driving impactful projects. In this podcast episode, Sol delves into the importance of prioritizing relationships, introduces a pragmatic "Wrong Use Cases Formula" to streamline project prioritization, and emphasizes the critical role of effective communication in data leadership. Her wealth of experience and practical advice provides a roadmap for navigating the complexities of MLOps and leading data-driven initiatives to success.

// Bio

With eight (8) patents granted, 21 filed, and received awards that include:

"Top 100 AI People" 2023

"The Top 75 Innovators of 2023"

"Top 65 Most Influential Women in 2023"

"Forbes AI Maverick of the 21st Century" 2022

“Top 10 Global Women in AI & Data”, 2023

"Top AI 100 Award", 2023

“50 Most Powerful Women in Tech”, 2022

“Global 100 Power List” - 2021, 2022, 2023

“Top 20 CDOs Globally” - 2022

"Chief Analytics Officer of the Year" - 2022

"Isomer Innovators of the Year" - 2021, 2022, 2023

"Top 100 Innovators in Data & Analytics” - 2020, 2021, 2022, 2023

"Top 100 Women in Business" - 2022

Sol is an energetic business executive and a goal-oriented technologist, skilled at coupling her technical acumen with storytelling abilities to articulate business value with both startups and Fortune 100s who are leaning into data, AI, and technology as a competitive advantage while wanting to preserve the legacy upon which they were founded. Sol has served as a C-Suite member across several Fortune 100 & Fortune 500 companies, including:

Chief Analytics Officer - Estee LauderChief Data & Analytics Officer - Merck Pharmaceuticals

EVP, Chief Data Officer - Sony MusicChief Data & AI Officer - Royal Caribbean Cruise Lines

Sr. Partner leading the Digital & Innovation Practice- Ernst & Young

Partner leading Watson Go-To-Market & Commercialization - IBM

Sol now serves as the CEO of ExecutiveAI LLC. A company dedicated to democratizing Artificial Intelligence for Humanity, and is considered an outstanding and influential business leader who is influencing the space, traveling the world as a keynote speaker, and serving as the bridge between established Gen

1.0 markets and those evolving into 4.0.

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Sol's Book will be out on April 30, 2024. Your AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments: https://www.amazon.com/Your-Survival-Guide-Real-World-Deployments/dp/1394272634?ref_=ast_author_mpb

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

Connect with Sol on LinkedIn: https://www.linkedin.com/in/sol-rashidi-a672291/

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.”

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

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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

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.

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

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

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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

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.

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Website: verenaweber.de

Sign up for Verena's newsletter: https://verenas-newsletter-63558b.beehiiv.com/

Zilliz - https://zilliz.com/

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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

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/

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.

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.

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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

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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 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

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.

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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

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

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