Together with Global AI Berlin

Hey guys,

it's that time again. After last year's successful Holy Tech Night, here we go again. Holy Tech Night organized by Janek. Three speakers will once again provide us with nerdy stuff for the evening, covering three different topics from the Microsoft world. During the breaks and afterwards, mulled wine and gingerbread will be available to see out the year in style.

Anyone who wants to is welcome to come in a suitable outfit - some of you still have an ugly sweater at home. 🫣😆

Here ist the agenda:

18:00 doors open 18:15 first talk 19:00 short break 19:05 second talk 19:50 short break 20:00 third talk 20:45 networking

Our speakers and its topics this year:

Michael Greth Psst ... Private Local AI – Turn your Mac into a Private AI Power House

As the year winds down and things get a little quieter, it’s the perfect moment to take a fresh look at what my computer (MacMini M4) can already do. In this session, you’ll discover how to turn it into a private AI powerhouse — without cloud dependencies, subscriptions, or data leaving your device. We’ll explore Small Language Models, LM Studio, MacWhisper, and AnythingLLM, and walk through practical examples for local transcription, document analysis, and fast on-device reasoning. By the end, you’ll know exactly what to play with between Christmas and New Year.

Thomas Stensitzki Holy Hybrid: The Last Jedi of the Exchange World

Exchange Hybrid is more than a temporary solution. It is the strategic bridge between on-premises and the cloud. In this session, you’ll learn why hybrid setups remain relevant in 2025, the common pitfalls to avoid, and how to implement best practices for a smooth migration. We will dive into authentication, connectivity, and the future of Exchange in a cloud-first world. Perfect for IT pros who want to master the middle ground without getting lost in complexity.

Markus Raatz Fabric IQ

With the new Fabric IQ, Microsoft is taking a big leap forward: rather than IT specialists, it is the business users who define and describe how the business works using an ontology, via entities with properties and relationships between them. They then explain to Microsoft Fabric where the relevant data can be found, and a data agent can then be used to query all aspects of the business and analyze them. And because it knows the entire business with its rules and not just one department, it can automatically make decisions that are almost as good as those made by its human colleagues. Not quite clear? No matter, this presentation has some good examples.

Every AI breakthrough starts with a solid data foundation, but too often, companies jump into AI without the infrastructure, strategy, or clarity they need to make it work. In this webinar, Fivetran Field CTO Mark Van de Wiel sits down with leaders from Accenture and Snowflake to talk about what it really means to be AI-ready and how the right data strategy can set your team up to deliver real results.

Key Takeaways:

1️⃣ What separates organizations that succeed with AI from those that stall out 2️⃣ Why data quality, access, and movement are essential to any AI initiative 3️⃣ How modern data infrastructure accelerates AI development 4️⃣ Lessons from the field: what these leaders are seeing across industries

Join us for a straightforward conversation—and walk away with a clearer path to getting your data (and your team) ready for what’s next.

Register here

Busy isn't the same as valuable. Too many data teams fall into the trap of endless activity chasing the next tool, migration, or hype, without ever proving their impact. In this episode of Hub & Spoken, Jason Foster, CEO and Founder of Cynozure, sits down with Maaike van den Branden, former Chief Data Officer at Compare the Market and Mumsnet, to uncover what truly makes a data team successful. Drawing on two decades of experience, Maaike shares the practical ingredients for teams that deliver real value: clarity of purpose, balanced skills, open recognition, and strong culture. Together, they explore how leaders can: Build happy, high-performing teams that last Avoid over-promoting tech at the expense of people Prove business impact without getting lost in the noise If you've ever wondered what separates a high-performing data team from a busy one, this conversation is your answer. 🎧 Listen now to learn the real secret to data team success.

Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Statsig built a complete set of data tools that allow engineering teams to measure the impact of their work. This toolkit is SO valuable to so many teams, that OpenAI - who was a huge user of Statsig - decided to acquire the company, the news announced last week. Talk about validation! Check out Statsig. •⁠ Linear – The system for modern product development. Here’s an interesting story: OpenAI switched to Linear as a way to establish a shared vocabulary between teams. Every project now follows the same lifecycle, uses the same labels, and moves through the same states. Try Linear for yourself. — What does it take to do well at a hyper-growth company? In this episode of The Pragmatic Engineer, I sit down with Charles-Axel Dein, one of the first engineers at Uber, who later hired me there. Since then, he’s gone on to work at CloudKitchens. He’s also been maintaining the popular Professional programming reading list GitHub repo for 15 years, where he collects articles that made him a better programmer.  In our conversation, we dig into what it’s really like to work inside companies that grow rapidly in scale and headcount. Charles shares what he’s learned about personal productivity, project management, incidents, interviewing, plus how to build flexible skills that hold up in fast-moving environments.  Jump to interesting parts: • 10:41 – the reality of working inside a hyperscale company • 41:10 – the traits of high-performing engineers • 1:03:31 – Charles’ advice for getting hired in today’s job market We also discuss: • How to spot the signs of hypergrowth (and when it’s slowing down) • What sets high-performing engineers apart beyond shipping • Charles’s personal productivity tips, favorite reads, and how he uses reading to uplevel his skills • Strategic tips for building your resume and interviewing  • How imposter syndrome is normal, and how leaning into it helps you grow • And much more! If you’re at a fast-growing company, considering joining one, or looking to land your next role, you won’t want to miss this practical advice on hiring, interviewing, productivity, leadership, and career growth. — Timestamps (00:00) Intro (04:04) Early days at Uber as engineer #20 (08:12) CloudKitchens’ similarities with Uber (10:41) The reality of working at a hyperscale company (19:05) Tenancies and how Uber deployed new features (22:14) How CloudKitchens handles incidents (26:57) Hiring during fast-growth (34:09) Avoiding burnout (38:55) The popular Professional programming reading list repo (41:10) The traits of high-performing engineers  (53:22) Project management tactics (1:03:31) How to get hired as a software engineer (1:12:26) How AI is changing hiring (1:19:26) Unexpected ways to thrive in fast-paced environments (1:20:45) Dealing with imposter syndrome  (1:22:48) Book recommendations  (1:27:26) The problem with survival bias  (1:32:44) AI’s impact on software development  (1:42:28) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: •⁠ Software engineers leading projects •⁠ The Platform and Program split at Uber •⁠ Inside Uber’s move to the Cloud •⁠ How Uber built its observability platform •⁠ From Software Engineer to AI Engineer – with Janvi Kalra — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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In this discussion, I sit down with data veterans Remco Broekmans and Marco Wobben to explore why so many data projects fail. They argue that the problem isn't the technology, but a fundamental misunderstanding of communication, culture, and long-term strategy.The conversation goes deep into the critical shift from being a "hardcore techie" to focusing on translating business needs into data models. They use the classic "involved party" data modeling pattern as a prime example of how abstract IT jargon creates a massive disconnect with the business.Marco shares a fascinating (and surprising) case study of the Dutch Railroad organization, which has been engaged in an 18-year information modeling "program" - not a project - to manage its immense complexity. This sparks a deep dive into the cultural and work-ethic differences between the US and Europe, contrasting the American short-term, ROI-driven "project" mindset with the European capacity for long-term, foundational "programs".Finally, they tackle the role of AI. Is it a silver bullet or just the latest shiny object? They conclude that AI's best use is as an "intern" or "assistant", a tool to brainstorm, ask questions, and handle initial prototyping, but never as a replacement for the deep, human-centric work of understanding a business.Timestamps:00:00 - Introduction01:09 - Marco Wobben introduces his 25-year journey in information modeling.01:56 - Remco Broekmans reintroduces himself and his focus on the communication aspect of data.03:22 - The progression from hardcore techie to focusing on communication over technology.08:16 - Why is communication in data and IT projects so difficult? 09:49 - The "Involved Party" Problem: A perfect example of where IT communication goes wrong with the business.13:35 - The essence of IT is automating the communication that happens on the business side.18:39 - Discussing a client with 20,000 distinct business terms in their information model.21:55 - The story of the Dutch Railroad's 18-year information modeling program that reduced incident response from 4 hours to 2 seconds.27:25 - Project vs. Program: A key mindset difference between the US and Europe.34:18 - The danger of chasing shiny new tools like AI without getting the fundamentals right first.39:55 - Where does AI fit into the world of data modeling? 43:34 - Why you can't trust AI to be the expert, especially with specialized business jargon.47:18 - The role of risk in trusting AI, using a self-driving car analogy.53:27 - Cultural differences in work pressure and ethics between the US and the Netherlands.59:29 - Why personality and communication skills are more important than a PhD for data modelers.01:03:38 - What is the purpose of an AI-run company with no human benefit? 01:11:21 - Using AI as an instructive tool to improve your own skills, not just to get an answer.01:14:12 - How AI can be used as a "sidekick" to ask dumb questions and help you think.01:18:00 - Where to find Marco and Remco online

Date and Time

Aug 28, 2025 at 10 AM Pacific

Location

Virtual - Register for the Zoom

Exploiting Vulnerabilities In CV Models Through Adversarial Attacks

As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.

About the Speaker

Elisa Chen is a data scientist at Meta on the Ads AI Infra team with 5+ years of experience in the industry.

EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation

Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments.

In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.

About the Speaker

Md Mostafijur Rahman is a final-year Ph.D. candidate in Electrical and Computer Engineering at The University of Texas at Austin, advised by Dr. Radu Marculescu, where he builds efficient AI methods for biomedical imaging tasks such as segmentation, synthesis, and diagnosis. By uniting efficient architectures with data-efficient training, his work delivers robust and efficient clinically deployable imaging solutions.

What Makes a Good AV Dataset? Lessons from the Front Lines of Sensor Calibration and Projection

Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.

In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.

Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Clustering in Computer Vision: From Theory to Applications

In today’s AI landscape, these techniques are crucial. Clustering methods help organize unstructured data into meaningful groups, aiding knowledge discovery, feature analysis, and retrieval-augmented generation. From k-means to DBSCAN and hierarchical approaches like FINCH, selecting the right method is key: including balancing scalability, managing noise sensitivity, and fitting computational demands. This presentation provides an in-depth exploration of the current state-of-the-art of clustering techniques with a strong focus on their applications within computer vision.

About the Speaker

Constantin Seibold leads research group on the development of machine learning methods in the diagnostic and interventional radiology department at the university hospital Heidelberg. His research aims to improve the daily life of both doctors and patients.

Date and Time

Aug 28, 2025 at 10 AM Pacific

Location

Virtual - Register for the Zoom

Exploiting Vulnerabilities In CV Models Through Adversarial Attacks

As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.

About the Speaker

Elisa Chen is a data scientist at Meta on the Ads AI Infra team with 5+ years of experience in the industry.

EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation

Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments.

In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.

About the Speaker

Md Mostafijur Rahman is a final-year Ph.D. candidate in Electrical and Computer Engineering at The University of Texas at Austin, advised by Dr. Radu Marculescu, where he builds efficient AI methods for biomedical imaging tasks such as segmentation, synthesis, and diagnosis. By uniting efficient architectures with data-efficient training, his work delivers robust and efficient clinically deployable imaging solutions.

What Makes a Good AV Dataset? Lessons from the Front Lines of Sensor Calibration and Projection

Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.

In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.

Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Clustering in Computer Vision: From Theory to Applications

In today’s AI landscape, these techniques are crucial. Clustering methods help organize unstructured data into meaningful groups, aiding knowledge discovery, feature analysis, and retrieval-augmented generation. From k-means to DBSCAN and hierarchical approaches like FINCH, selecting the right method is key: including balancing scalability, managing noise sensitivity, and fitting computational demands. This presentation provides an in-depth exploration of the current state-of-the-art of clustering techniques with a strong focus on their applications within computer vision.

About the Speaker

Constantin Seibold leads research group on the development of machine learning methods in the diagnostic and interventional radiology department at the university hospital Heidelberg. His research aims to improve the daily life of both doctors and patients.

Date and Time

Aug 28, 2025 at 10 AM Pacific

Location

Virtual - Register for the Zoom

Exploiting Vulnerabilities In CV Models Through Adversarial Attacks

As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.

About the Speaker

Elisa Chen is a data scientist at Meta on the Ads AI Infra team with 5+ years of experience in the industry.

EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation

Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments.

In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.

About the Speaker

Md Mostafijur Rahman is a final-year Ph.D. candidate in Electrical and Computer Engineering at The University of Texas at Austin, advised by Dr. Radu Marculescu, where he builds efficient AI methods for biomedical imaging tasks such as segmentation, synthesis, and diagnosis. By uniting efficient architectures with data-efficient training, his work delivers robust and efficient clinically deployable imaging solutions.

What Makes a Good AV Dataset? Lessons from the Front Lines of Sensor Calibration and Projection

Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.

In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.

Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Clustering in Computer Vision: From Theory to Applications

In today’s AI landscape, these techniques are crucial. Clustering methods help organize unstructured data into meaningful groups, aiding knowledge discovery, feature analysis, and retrieval-augmented generation. From k-means to DBSCAN and hierarchical approaches like FINCH, selecting the right method is key: including balancing scalability, managing noise sensitivity, and fitting computational demands. This presentation provides an in-depth exploration of the current state-of-the-art of clustering techniques with a strong focus on their applications within computer vision.

About the Speaker

Constantin Seibold leads research group on the development of machine learning methods in the diagnostic and interventional radiology department at the university hospital Heidelberg. His research aims to improve the daily life of both doctors and patients.

Date and Time

Aug 28, 2025 at 10 AM Pacific

Location

Virtual - Register for the Zoom

Exploiting Vulnerabilities In CV Models Through Adversarial Attacks

As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.

About the Speaker

Elisa Chen is a data scientist at Meta on the Ads AI Infra team with 5+ years of experience in the industry.

EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation

Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments.

In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.

About the Speaker

Md Mostafijur Rahman is a final-year Ph.D. candidate in Electrical and Computer Engineering at The University of Texas at Austin, advised by Dr. Radu Marculescu, where he builds efficient AI methods for biomedical imaging tasks such as segmentation, synthesis, and diagnosis. By uniting efficient architectures with data-efficient training, his work delivers robust and efficient clinically deployable imaging solutions.

What Makes a Good AV Dataset? Lessons from the Front Lines of Sensor Calibration and Projection

Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.

In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.

Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Clustering in Computer Vision: From Theory to Applications

In today’s AI landscape, these techniques are crucial. Clustering methods help organize unstructured data into meaningful groups, aiding knowledge discovery, feature analysis, and retrieval-augmented generation. From k-means to DBSCAN and hierarchical approaches like FINCH, selecting the right method is key: including balancing scalability, managing noise sensitivity, and fitting computational demands. This presentation provides an in-depth exploration of the current state-of-the-art of clustering techniques with a strong focus on their applications within computer vision.

About the Speaker

Constantin Seibold leads research group on the development of machine learning methods in the diagnostic and interventional radiology department at the university hospital Heidelberg. His research aims to improve the daily life of both doctors and patients.

Date and Time

Aug 28, 2025 at 10 AM Pacific

Location

Virtual - Register for the Zoom

Exploiting Vulnerabilities In CV Models Through Adversarial Attacks

As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.

About the Speaker

Elisa Chen is a data scientist at Meta on the Ads AI Infra team with 5+ years of experience in the industry.

EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation

Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments.

In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.

About the Speaker

Md Mostafijur Rahman is a final-year Ph.D. candidate in Electrical and Computer Engineering at The University of Texas at Austin, advised by Dr. Radu Marculescu, where he builds efficient AI methods for biomedical imaging tasks such as segmentation, synthesis, and diagnosis. By uniting efficient architectures with data-efficient training, his work delivers robust and efficient clinically deployable imaging solutions.

What Makes a Good AV Dataset? Lessons from the Front Lines of Sensor Calibration and Projection

Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.

In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.

Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Clustering in Computer Vision: From Theory to Applications

In today’s AI landscape, these techniques are crucial. Clustering methods help organize unstructured data into meaningful groups, aiding knowledge discovery, feature analysis, and retrieval-augmented generation. From k-means to DBSCAN and hierarchical approaches like FINCH, selecting the right method is key: including balancing scalability, managing noise sensitivity, and fitting computational demands. This presentation provides an in-depth exploration of the current state-of-the-art of clustering techniques with a strong focus on their applications within computer vision.

About the Speaker

Constantin Seibold leads research group on the development of machine learning methods in the diagnostic and interventional radiology department at the university hospital Heidelberg. His research aims to improve the daily life of both doctors and patients.

Date and Time

Aug 28, 2025 at 10 AM Pacific

Location

Virtual - Register for the Zoom

Exploiting Vulnerabilities In CV Models Through Adversarial Attacks

As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.

About the Speaker

Elisa Chen is a data scientist at Meta on the Ads AI Infra team with 5+ years of experience in the industry.

EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation

Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments.

In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.

About the Speaker

Md Mostafijur Rahman is a final-year Ph.D. candidate in Electrical and Computer Engineering at The University of Texas at Austin, advised by Dr. Radu Marculescu, where he builds efficient AI methods for biomedical imaging tasks such as segmentation, synthesis, and diagnosis. By uniting efficient architectures with data-efficient training, his work delivers robust and efficient clinically deployable imaging solutions.

What Makes a Good AV Dataset? Lessons from the Front Lines of Sensor Calibration and Projection

Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.

In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.

Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Clustering in Computer Vision: From Theory to Applications

In today’s AI landscape, these techniques are crucial. Clustering methods help organize unstructured data into meaningful groups, aiding knowledge discovery, feature analysis, and retrieval-augmented generation. From k-means to DBSCAN and hierarchical approaches like FINCH, selecting the right method is key: including balancing scalability, managing noise sensitivity, and fitting computational demands. This presentation provides an in-depth exploration of the current state-of-the-art of clustering techniques with a strong focus on their applications within computer vision.

About the Speaker

Constantin Seibold leads research group on the development of machine learning methods in the diagnostic and interventional radiology department at the university hospital Heidelberg. His research aims to improve the daily life of both doctors and patients.

Date and Time

Aug 28, 2025 at 10 AM Pacific

Location

Virtual - Register for the Zoom

Exploiting Vulnerabilities In CV Models Through Adversarial Attacks

As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.

About the Speaker

Elisa Chen is a data scientist at Meta on the Ads AI Infra team with 5+ years of experience in the industry.

EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation

Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments.

In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.

About the Speaker

Md Mostafijur Rahman is a final-year Ph.D. candidate in Electrical and Computer Engineering at The University of Texas at Austin, advised by Dr. Radu Marculescu, where he builds efficient AI methods for biomedical imaging tasks such as segmentation, synthesis, and diagnosis. By uniting efficient architectures with data-efficient training, his work delivers robust and efficient clinically deployable imaging solutions.

What Makes a Good AV Dataset? Lessons from the Front Lines of Sensor Calibration and Projection

Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.

In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.

Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Clustering in Computer Vision: From Theory to Applications

In today’s AI landscape, these techniques are crucial. Clustering methods help organize unstructured data into meaningful groups, aiding knowledge discovery, feature analysis, and retrieval-augmented generation. From k-means to DBSCAN and hierarchical approaches like FINCH, selecting the right method is key: including balancing scalability, managing noise sensitivity, and fitting computational demands. This presentation provides an in-depth exploration of the current state-of-the-art of clustering techniques with a strong focus on their applications within computer vision.

About the Speaker

Constantin Seibold leads research group on the development of machine learning methods in the diagnostic and interventional radiology department at the university hospital Heidelberg. His research aims to improve the daily life of both doctors and patients.

Brought to You By: •⁠ WorkOS — The modern identity platform for B2B SaaS. •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar —  Code quality and code security for ALL code. — In this episode of The Pragmatic Engineer, I sit down with Peter Walker, Head of Insights at Carta, to break down how venture capital and startups themselves are changing. We go deep on the numbers: why fewer companies are getting funded despite record VC investment levels, how hiring has shifted dramatically since 2021, and why solo founders are on the rise even though most VCs still prefer teams. We also unpack the growing emphasis on ARR per FTE, what actually happens in bridge and down rounds, and why the time between fundraising rounds has stretched far beyond the old 18-month cycle. We cover what all this means for engineers: what to ask before joining a startup, how to interpret valuation trends, and what kind of advisor roles startups are actually looking for. If you work at a startup, are considering joining one, or just want a clearer picture of how venture-backed companies operate today, this episode is for you. — Timestamps (00:00) Intro (01:21) How venture capital works and the goal of VC-backed startups (03:10) Venture vs. non-venture backed businesses  (05:59) Why venture-backed companies prioritize growth over profitability (09:46) A look at the current health of venture capital  (13:19) The hiring slowdown at startups (16:00) ARR per FTE: The new metric VCs care about (21:50) Priced seed rounds vs. SAFEs  (24:48) Why some founders are incentivized to raise at high valuations (29:31) What a bridge round is and why they can signal trouble (33:15) Down rounds and how optics can make or break startups  (36:47) Why working at startups offers more ownership and learning (37:47) What the data shows about raising money in the summer (41:45) The length of time it takes to close a VC deal (44:29) How AI is reshaping startup formation, team size, and funding trends (48:11) Why VCs don’t like solo founders (50:06) How employee equity (ESOPs) work (53:50) Why acquisition payouts are often smaller than employees expect (55:06) Deep tech vs. software startups: (57:25) Startup advisors: What they do, how much equity they get (1:02:08) Why time between rounds is increasing and what that means (1:03:57) Why it’s getting harder to get from Seed to Series A  (1:06:47) A case for quitting (sometimes)  (1:11:40) How to evaluate a startup before joining as an engineer (1:13:22) The skills engineers need to thrive in a startup environment (1:16:04) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode:

— See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Hear talks from experts on cutting-edge topics in AI, ML, and computer vision!

Register for the event to reserve your seat.

When and Where

July 16 5:30-8:30 PM

Paris Marriott Opera Ambassador Vendôme Meeting Room 16 Bd Haussmann

Building and working with Small Language Models

This session focuses on practical techniques for using small open-source language models (SLMs) in enterprise settings. We'll explore modern workflows for adapting SLMs with domain-specific pre-training, instruction fine-tuning, and alignment. Along the way, we will introduce and demonstrate open-source tools such as DistillKit, Spectrum, and MergeKit, which implement advanced techniques crucial for achieving task-specific accuracy while optimizing computational costs. We'll also discuss some of the models and solutions built by Arcee AI. Join us to learn how small, efficient, and adaptable models can transform your AI applications.

About the Speaker

Julien Simon, the Chief Evangelist at Arcee.ai, is dedicated to helping enterprise clients develop top-notch and cost-efficient AI solutions using small language models. With over 30 years of tech experience, including more than a decade in cloud computing and machine learning, Julien is committed to daily learning and is passionate about sharing his expertise through code demos, blogs, and YouTube videos. Before joining Arcee.ai, he was Chief Evangelist at Hugging Face and Global AI Evangelist at Amazon Web Services. He also served as a CTO at prominent startups.

Accelerating sustainable inference with Pruna AI

This talk explores how to make AI faster and more sustainable. We’ll look at the high costs and carbon impact of fine-tuning and self deploying models, and show how optimization techniques available in the Pruna library can reduce size and latency with little to no quality loss.

About the Speaker

Gabriel Tregoat is the software lead at Pruna.ai, a specialist company on model inference optimisation with an open source library called “pruna”. He previously was a leader for AI in production at Ekimetrics, and started his career as a data scientist and ml-engineer at Shell Energy. He’s passionate about tech, code and new technologies.

Visual Agents: What it takes to build an agent that can navigate GUIs like humans

We’ll examine conceptual frameworks, potential applications, and future directions of technologies that can “see” and “act” with increasing independence. The discussion will touch on both current limitations and promising horizons in this evolving field.

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

What I Learned About Systematic AI Improvement

Most AI teams go through the same story: fast early progress, and then suddenly things slow down. The AI isn’t broken, but new changes don’t seem to help, and it’s not even clear how to tell if things are getting better. I’ve faced this plateau myself—both in my own work and while helping other teams. In this talk, I’ll share what I’ve learned about getting unstuck: how to build genuine confidence in your AI, what “trust” really means in practice, and practical steps to move from “it kind of works” to “this is actually improving.” My goal is to give you real-world ideas you can use when you hit the same wall.

About the Speaker

Louis Dupont is an AI engineer with over eight years of experience developing AI solutions across multiple industries. Currently, he work directly with companies to build and deploy AI internally, and as a consultant specializing in helping teams overcome common roadblocks in AI development.

Send us a text The AI Advantage: Get Better Results from LLMs with the Perfect Prompt On this episode of Making Data Simple, we’re joined by Jonathan Mast, AI consultant and coach at Whitebeard Strategies and creator of the Perfect Prompting Framework™. Jonathan’s not just riding the AI wave—he’s teaching business leaders and everyday users how to surf it, with simple, actionable tools that unlock meaningful results from large language models. If you've ever stared at a prompt box wondering what to type—or worse, gotten garbage back from AI—this episode is for you. We talk about what works, what doesn’t, and what’s coming next (agents, anyone?). Plus, Jonathan breaks down his 4-step framework that’s helping 300K+ community members and clients scale AI with clarity and confidence. ⏱️ Episode Timestamps 01:34 Introducing Jonathan Mast04:13 Digital Agency05:29 Whitebeard Strategies08:06 ADD09:57 Back to Whitebeard14:51 The Perfect Prompting Framework21:36 The Four Step Method24:58 What if You Don't Use AI?28:37 Agents30:08 Whitebeard Engagements32:42 Getting Started36:39 What's True But Not a Consensus?37:23 For Fun🔗 Connect with Jonathan LinkedIn: https://www.linkedin.com/in/jonathanjmast/Website: https://whitebeardstrategies.com#MakingDataSimple #PerfectPromptingFramework #AIforBusiness #AIProductivity #JonathanMast #PromptEngineering #LLMs #AIAgents #WhitebeardStrategies #TechPodcast #DataSimplified Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Take your DAGs in Apache Airflow to the next level? This is an insightful session where we’ll uncover 5 transformative strategies to enhance your data workflows. Whether you’re a data engineering pro or just getting started, this presentation is packed with practical tips and actionable insights that you can apply right away. We’ll dive into the magic of using powerful libraries like Pandas, share techniques to trim down data volumes for faster processing, and highlight the importance of modularizing your code for easier maintenance. Plus, you’ll discover efficient ways to monitor and debug your DAGs, and how to make the most of Airflow’s built-in features. By the end of this session, you’ll have a toolkit of strategies to boost the efficiency and performance of your DAGs, making your data processing tasks smoother and more effective. Don’t miss out on this opportunity to elevate your Airflow DAGs!

Are you a cybersecurity professional navigating the challenges of building or joining high-performing teams? Whether you're scaling a SOC, hiring your next pentester, or preparing for your next move, this edition of the Berlin Cybersecurity Social is for you.

Join us for an evening of real talk on one of the most mission-critical — yet overlooked — elements of security work: hiring.

Agenda:

• 7:00 PM - 7:15 PM: Welcome

• 7:15 PM – 7:45 PM: Lightning Talk – “The Hiring Gap: Why Great People Get Overlooked (and What to Do About It)” * The stakes are high in cybersecurity — and the cost of a bad hire, or a missed opportunity, can ripple across the entire organization. * In this talk, Konstanty Sliwowski, Founder and Lead Trainer of The Hiring School, breaks down what he calls the hiring gap — the disconnect between how hiring actually works in tech vs. how it could work with just a bit more clarity and intention. * Drawing on 20+ years of technical hiring experience, Konstanty will explore: - Why so many strong candidates don’t make it past the interview process - The hidden biases and missteps in tech hiring — especially in cybersecurity roles - How both hiring managers and candidates can take a more intentional\, effective approach

• For managers, it’s about getting sharper on how you define success, how you structure interviews, and how you make decisions that actually strengthen your team. * For candidates, it’s about learning how to position yourself, spot real opportunities, and lead conversations that uncover what you need to make the right choice. * Whether you’re hiring, interviewing — or doing both — this session will give you practical ways to shift the hiring game in your favour.

• Who Should Attend: * - Cybersecurity professionals of all kinds - Hiring managers and team leads - Candidates navigating the job market - Anyone curious about building better teams and careers *

• All experience levels welcome!

• 7:30 PM - 9:45 PM: Icebreaker & DIY Networking

Mingle with fellow professionals from the cybersecurity industry. Share insights, discuss recent developments, and exchange ideas

NB: c-base is a cash only venue. Soft drinks and beers are available to purchase.

Why Attend?

• Connect with professionals in the cybersecurity field.
• Share your experiences and insights.
• Expand your professional network.
• Discover collaboration opportunities.
• Enjoy a relaxed evening and make new friends

This meetup is open to cybersecurity professionals of all levels, from beginners to experts. Whether you're a seasoned pro or just starting your journey in the field, this event is the perfect opportunity to connect with others who share your passion for cybersecurity.

About the Speaker: Konstanty Sliwowski

Konstanty Sliwowski is the founder of School of Hiring and a globally recognized expert in hiring strategy and talent development. With over two decades of experience, he’s interviewed more than 12,000 candidates and hired over 1,000 professionals across technical and cybersecurity roles.

A 3x founder, Konstanty has built and led high-performing tech teams, scaled a search firm through a successful merger and sale, and now trains leaders to approach hiring with more clarity, intention, and impact. His work bridges the gap between business needs and technical talent.

About the Location: c-base e. V. is a non-profit association located in Berlin, Germany which has about 300 members. The purpose of this association is to increase knowledge and know-how regarding computer software, hardware and data networks.

The premises of the association are also used by other initiatives in and around Berlin as an event location or as function rooms, for example the wireless community network freifunk.net, the Chaos Computer Club or the Wikipedia group in Berlin.

About Berlin Cybersecurity Social: This meetup is open to cybersecurity professionals of all levels, from beginners to experts. Whether you're a seasoned pro or just starting your journey in the field, this event is the perfect opportunity to connect with others who share your passion for cybersecurity.

Getting started with data and AI governance in the modern data stack? Unity Catalog is your gateway to secure, discoverable and well-governed data and AI assets. In this session, we’ll break down what Unity Catalog is, why it matters and how it simplifies access control, lineage, discovery, auditing, business semantics and secure, open collaboration — all from a single place. We’ll explore how it enables open interoperability across formats, tools and platforms, helping you avoid lock-in and build on open standards. Most importantly, you’ll learn how Unity Catalog lays the foundation for data intelligence — by unifying governance across data and AI, enabling AI tuned to your business. It helps build a deep understanding of your data and delivers contextual, domain-specific insights that boost productivity for both technical and business users across any workload.

Today, I’m responding to a listener's question about what it takes to succeed as a data or AI product manager, especially if you’re coming from roles like design/BI/data visualization, data science/engineering, or traditional software product management. This reader correctly observed that most of my content “seems more targeted at senior leadership” — and had asked if I could address this more IC-oriented topic on the show. I’ll break down why technical chops alone aren’t enough, and how user-centered thinking, business impact, and outcome-focused mindsets are key to real success — and where each of these prior roles brings strengths and/or weaknesses. I’ll also get into the evolving nature of PM roles in the age of AI, and what I think the super-powered AI product manager will look like.

Highlights/ Skip to:

Who can transition into an AI and data product management role? What does it take? (5:29) Software product managers moving into  AI product management (10:05) Designers moving into data/AI product management (13:32) Moving into the AI PM role from the engineering side (21:47) Why the challenge of user adoption and trust is often the blocker to the business value (29:56) Designing change management into AI/data products as a skill (31:26) The challenge of value creation vs. delivery work — and how incentives are aligned for ICs  (35:17) Quantifying the financial value of data and AI product work(40:23)

Quotes from Today’s Episode

“Who can transition into this type of role, and what is this role? I’m combining these two things. AI product management often seems closely tied to software companies that are primarily leveraging AI, or trying to, and therefore, they tend to utilize this AI product management role. I’m seeing less of that in internal data teams, where you tend to see data product management more, which, for me, feels like an umbrella term that may include traditional analytics work, data platforms, and often AI and machine learning. I’m going to frame this more in the AI space, primarily because I think AI tends to capture the end-to-end product than data product management does more frequently.” — Brian (2:55)

“There are three disciplines I’m going to talk about moving into this role. Coming into AI and data PM from design and UX, coming into it from data engineering (or just broadly technical spaces), and then coming into it from software product management. I think software product management and moving into the AI product management - as long as you’re not someone that has two years of experience, and then 18 years of repeating the second year of experience over and over again - and you’ve had a robust product management background across some different types of products; you can show that the domain doesn’t necessarily stop you from producing value. I think you will have the easiest time moving into AI product management because you’ve shown that you can adapt across different industries.” - Brian (9:45)

“Let’s talk about designers next. I’m going to include data visualization, user experience research, user experience design, product design, all those types of broad design, category roles. Moving into data and/or AI product management, first of all, you don’t see too many—I don’t hear about too many designers wanting to move into DPM roles, because oftentimes I don’t think there’s a lot of heavy UI and UX all the time in that space. Or at least the teams that are doing that work feel that’s somebody else’s job because they’re not doing end-to-end product thinking the way I talk about it, so therefore, a lot of times they don’t see the application, the user experience, the human adoption, the change management, they’re just not looking at the world that way, even though I think they should be.” - Brian (13:32)

“Coming at this from the data and engineering side, this is the classic track for data product management. At least that is the way I tend to see it. I believe most companies prefer to develop this role in-house. My biggest concern is that you end up with job title changes, but not necessarily the benefits that are supposed to come with this. I do like learning by doing, but having a coach and someone senior who can coach your other PMs is important because there’s a lot of information that you won’t necessarily get in a class or a course. It’s going to come from experience doing the work.” - Brian (22:26)

“This value piece is the most important thing, and I want to focus on that. This is something I frequently discuss in my training seminar: how do we attach financial value to the work we’re doing? This is both art and science, but it’s a language that anyone in a product management role needs to be comfortable with. If you’re finding it very hard to figure out how your data product contributes financial value because it’s based on this waterfalling of “We own the model, and it’s deployed on a platform.” The platform then powers these other things, which in turn power an application. How do we determine the value of our tool? These things are challenging, and if it’s challenging for you, guess how hard it will be for stakeholders downstream if you haven’t had the practice and the skills required to understand how to estimate value, both before we build something as well as after?” - Brian (31:51)

“If you don’t want to spend your time getting to know how your business makes money or creates value, then [AI and data product management work] is not for you. It’s just not. I would stay doing what you’re doing already or find a different thing because a lot of your time is going to be spent “managing up” for half the time, and then managing the product stuff “down.” Then, sitting in this middle layer, trying to explain to the business what’s going to come out and what the impact is going to be, in language that they care about and understand. You can't be talking about models, model accuracy, data pipelines, and all that stuff. They’re not going to care about any of that. - Brian (34:08)

Managing customer interactions across multiple disconnected platforms creates inefficiencies and delays in resolving support tickets. At WHOOP, support agents had to manually navigate through siloed data across payments, ERP, and ticketing systems, slowing down response times and impacting customer satisfaction.In this session, Matt Luizzi (Director of Business Analytics, WHOOP) and Brendan Farley (Sales Engineer, Snowflake) will showcase how WHOOP:

Consolidated fragmented data from multiple systems into a unified customer support app. Enabled real-time access to customer history, allowing agents to quickly surface relevant insights. Eliminated the need for custom engineering by leveraging Sigma’s no-code interface to build interactive workflows. Accelerated ticket resolution by allowing support teams to take action directly within Sigma, reducing dependency on multiple SaaS tools. Improved forecasting and decision-making by implementing AI-powered analytics on top of Snowflake. Before Sigma, getting a full view of customer issues required navigating across multiple tools—now, WHOOP’s customer support team can access, analyze, and act on real-time data in a single interface. Join us for an inside look at how WHOOP and Snowflake partnered to build a modern customer support data app that enhances efficiency and customer experience.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

➡️ Sign up for your free trial: https://www.sigmacomputing.com/go/free-trial?utm_source=youtube&utm_medium=video&utm_campaign=free_trial&utm_content=free_trial

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