MLOps.community Demetrios Brinkmann

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

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

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 is 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 enables high iteration velocity and ultimately continual improvement.

We are making progress but environmental causes need your help. Join us fight 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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// Related Links
Variety of relevant papers/talks/links on Patrick's website: https://pbeukema.github.io/

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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 see are:

Model size and moving towards mixture of experts architectures
context window - new breakthroughs for context lengths
from unimodality to multimodality, next step large action models?
regulation in 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 Alexa team, Amazon. Passionate about gender diversity in tech, she mentors women to thrive in this field.

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// Related Links
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 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/


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.

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// Related Links
Website: https://www.databricks.com/
Databricks DBRX: https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm

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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!

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

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// Related Links
AI 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
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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/