talk-data.com

Let's fix ChatGPT's greatest design sins. We'll design and build a working app that makes ChatGPT multi-modal and multi-model. And no, you don't need to know what those words mean to use it.

Download the source code: https://github.com/bholmesdev/fixgpt

References from this video: - Try https://warp.dev to vibe code your own solution - Watch Scott and Mark's podcast episode, "how to not ship the org chart:" https://www.youtube.com/watch?v=Z1yYcUFzH2A - Read "Why is AI marketing so, so bad?" by Evan Armstrong at The Leverage: https://www.gettheleverage.com/p/why-is-ai-marketing-so-so-bad

In business, ideas don't stand alone; they have to be communicated effectively to drive action. Whether through reports, presentations, or product pitches, success comes from not just having data expertise but knowing how to make it matter. Yet many in the data world focus on just establishing a "personal brand" rather than developing something tangible. So, what does it take to turn expertise into a real product? In this episode with Scott Taylor, The Data Whisperer, we take a behind-the-scenes look at how creativity and communication shape success in the data space beyond just technical skills or personal branding. Listen for a raw and insightful conversation on navigating creativity, career growth, and the business of turning data expertise into something real. What You'll Learn: Why communication is a superpower in business and data The balance between technical skills and storytelling in shaping ideas How creativity applies in a structured B2B context Content, branding, and the tools of a data creator—beyond just a LinkedIn presence  The challenge of crafting the perfect one-sentence pitch   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Will generative artificial intelligence lead to nirvana or dystopia? Great question, which we don’t exactly answer in this week’s podcast, but we do weigh the most critical downstream effects of the technology based on recent research done by urban economists Frank Levy and Scott Abrahams. We assess how GenAI impacts the benefits of a college degree, the nation’s political dynamics, and which metro area economies will win (think Savannah) and lose (think San Francisco). Guests: Frank Levy, Visitor in the Strategy Group of the Fuqua School of Business, Duke University, and Scott Abrahams, Professor of Economics at Louisiana State University Read Frank and Scott's recent research on Gen AI here: From San Francisco to Savannah? The Downstream Effects of Generative AI (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4874104) Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

S1 Ep#34: The Future of Data Mesh The Data Product Management In Action podcast, brought to you by executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. 

In Season 01, Episode 34, Join host Michael Toland as he welcomes Tom DeWolf, a data mesh expert with a PhD in distributed systems and years of experience in software engineering. Tom shares insights from his four-year journey in data mesh, emphasizing the need for self-service in data products, the benefits of an evolutionary architecture, and the challenges of governance in multi-organization environments. He also discusses key lessons from past failures, highlighting the critical role of user engagement in building successful data ecosystems. Don't miss this deep dive into the future of data management!

About our Host Michael Toland: Michael is a Product Management Coach and Consultant with Pathfinder Product, a Test Double Operation. He has worked in product officially since 2016, where he worked at Verizon on large scale system modernizations and migration initiatives for reference data and decision platforms. Outside of his professional career, Michael serves as the Treasurer for the New Leaders Council, mentors fellows with Venture for America, sings in the Columbus Symphony, writing satire posts for his blog Dignified Product or Test Double, depending on the topic, and is excited to be chatting with folks on Data Product Management. Connect with Michael on LinkedIn.

About our guest Tom DeWolf: Tom is an experienced hands-on architect and serves as the innovation lead, spearheading new innovative initiatives for ACA Group. His expertise lies in data mesh platform and platform engineering, leveraging his background in software engineering and experience in designing various architectures, including software, microservices, data platforms, evolutionary architectures, among others. Tom is the founder and host of Data Mesh Belgium meetup and the new Data Mesh Live conference, and active Data Mesh community thought leader. Connect with Tom on LinkedIn.

All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else. 

Join the conversation on LinkedIn. 

Apply to be a guest or nominate someone that you know. 

Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights! 

Register to reserve your spot!

Date and Time

Nov 22, 2024 from 5:30 PM to 8:30 PM

Location

The Meetup will take place at MotionLab.Berlin, Bouchéstraße 12/Halle 20 in Berlin

When the Medium is the Message: Addressing Input Biases in Multimodal/Multilingual Models

An embedding model is trained to produce outputs that ensure that semantic similarity is preserved as distance in embedding spaces — like is near like and far from unlike. But models trained with diverse kinds of inputs, i.e. different media and different languages, learn to treat those properties as semantic properties. Two pictures are more “semantically alike” than a picture and a descriptive text that matches it. Similar problems arise with multilingual models: Two English sentences are more alike than an English sentence and a Chinese translation. This undermines the general utility of embedding models. This presentation shows evidence of where this comes from and offers approaches to mitigate the problem.

About the Speaker

Scott Martens is a long-term veteran of AI and NLP research, having started working at AI start-ups in 1994, and a KU Leuven graduate with a doctorate in linguistics. His background includes machine translation development and the intersection between linguistics, philology, and modern AI. Dr. Martens is a Senior Content Manager and Evangelist at Jina AI in Berlin.

Vector Streaming: The Memory Efficient Indexing for Vector Databases

Vector databases are everywhere, powering LLMs. Indexing vectors, especially multivector embeddings like ColPali and Colbert, at a bulk is memory intensive. Vector streaming solves this problem by parallelizing the tasks of parsing, chunking, and embedding generation and indexing it continuously chunk by chunk instead of bulk. This not only increase the speed but also makes the whole task more optimized and memory efficient. Supports, Weaviate, Elastic and Pinecone.

About the Speaker

Sonam Pankaj is a GenerativeAI Evangelist at Articul8-ai and the co-creator and maintainer of the open-source library called Embed-Anything, which helps to create local dense, splade, and multimodal embeddings and index them to vector databases; it’s built-in Rust for speed and efficiency . She worked previously at Qdrant Engine and Rasa. Previously, she also worked as an AI researcher at Saama and has worked extensively on clinical trial analytics. She is passionate about topics like metric learning and biases in language models. She has also published a paper in the most reputed journal of computational linguistics, COLING, in ACL Anthology.

How to Unlock More Value from Self-Driving Datasets

AV/ADAS is one of the most advanced fields in Visual AI. However, getting your hands on a high quality dataset can be tough, let alone working with them to get a model to production. In this talk, I will show you the leading methods and tools to help visualize as well take these datasets to the next level. I will demonstrate how to clean and curate AV datasets as well as perform state of the art augmentations using diffusion models to create synthetic data that can empower the self driving car models of the future,

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.

When the Medium is the Message: Addressing Input Biases in Multimodal/Multilingual Models

Thanks to deep learning, autonomous cars equipped with cameras and LiDAR can accurately recognize common objects such as cars, streets, and pedestrians, significantly enhancing their understanding of the environment. However, these models often display overconfidence, which can result in misidentifications. For example, consider an exaggerated scenario where an elephant on the street might be mistakenly identified as a trunk because the model has not been trained to recognize elephants. This issue stems from the models’ design to make decisions rather than acknowledge uncertainty by saying ‘I don’t know.’ In this talk, we will discuss how models can recognize their limitations and avoid making uncertain decisions, particularly through the lens of an autonomous car

About the Speaker

Hanieh Shojaei is a PhD researcher at the Institute of Cartography and Geoinformatics (IKG) at Leibniz University Hannover, specializing in uncertainty estimation and reliability of AI models. Her research focuses on using deep learning for LiDAR scene segmentation to enhance environmental perception and assess prediction reliability for autonomous vehicles.

Hello London Gophers! 👋

Welcome to the description page of another amazing Go event! Are you ready for the biggest Go event this side of the Thames?

📜 All London Gophers events operate under the Go Community Code of Conduct - https://golang.org/conduct

• Treat everyone with respect and kindness.
• Be thoughtful in how you communicate.
• Don’t be destructive or inflammatory.

Please do not message members without their consent

If you encounter an issue, please mail [email protected] or [email protected]

==== 📓 Agenda📓 =====

6:30 - 7:00pm: Arrival, Food & Refreshments

7:00pm: Talks Start

🗣️ Scott Nicholas Allan Smith - Cognit-Go Have you ever jumped into a codebase (new or old!) and felt like it was resisting every attempt you were making to understand it? Do you feel pity for newly onboarded colleagues because you know it's going to take them weeks or months to wrap their heads around the code?

There are many sources of complexity in software systems. Here we'll discuss one source of complexity, a metric to measure that complexity called cognitive complexity, a tool to calculate this metrics on Go code, some real examples of how this metric can be used to push code toward being easier to understand, and some approaches to taming this complexity in both greenfield and legacy projects..

🗣️ Qi Xiao - How to test your programming language and terminal app by inventing a DSL and a VS Code plugin I'll talk about an interesting testing technique for a programming language I develop (Elvish, https://elv.sh, it's also a shell but that's irrelevant for the talk) in Go.

In order to test the interpreter, I invented a small DSL that's basically a transcript of interactions with the interpreter. The testing framework will take those transcripts, run the input and see if the output matches. To make writing those tests super easy, I also created a VS Code extension that can fill in the output from the input. A video is worth 1000 words: https://drive.google.com/file/d/1VDMDaEC0IYw30eWqQdt9UgYeAXmGWsjH/view?usp=sharing

\~8:30 - 9:00pm: Closing and Head to the Pub

==== 💡 Priority Queue 💡 =====

We now reserve 20% of the attendee spots at our events for those who are underrepresented in tech.

If they join the waitlist and there is a reserved spot open they will be bumped into going!

These spots are reversed until the last Sunday before the event.

How do we define underrepresented? We use public surveys done by the tech community such as the ones linked below.

https://survey.stackoverflow.co/2022/#section-demographics

https://www.jetbrains.com/lp/devecosystem-2022/#gender-and-development

==== 📢 Become a Speaker! 📢 =====

Have something to say? We want to listen! We are always looking for new speakers who want to share their adventures with Go and have mentors who can help.

You can sign up to be a speaker here: https://gophers.london/apply

==== 🎉 Prizes! 🎉 =====

JetBrains Raffle! - We have 3 free JetBrains Product licenses to giveaway to some of our lucky attendees.

==== 📝 Update Your RSVPS! 📝 =====

We monitor attendance and keep track of no-shows. Please if you can no longer make it to the event update your RSVP!

==== 📞 How To Reach Us 📞 =====

Email: [email protected] Linkedin: https://www.linkedin.com/company/london-gophers/ YouTube: https://www.youtube.com/c/LondonGophers

This event is a collaboration with our friends from Meetup.ai, check out their Meetup page.

------ Dear Friends, colleagues, and fellow technologists,

Meetup.ai is back with yet another great event! This time with one of the key issues Startups face — how to talk/collaborate with industry players. There is much to be gained from partnering across the divide; many try but struggle to get traction.

We are happy to invite you to our "BS! - Board to Start Up Talks" on September 12th, at 17:30, once again co-hosted by our great friends at ThoughtWorks Berlin. We will talk about practical tips for founders and corps alike and have a panel discussion with leaders on both sides. So don't miss out as we will have some fantastic experts ready for you. Thoughtworks is a global technology consultancy that integrates strategy, design, and software engineering. It is also a community of passionate, purpose-led individuals.

Speakers:

• Nicholas Borsotto - Head of Lenovo AI Innovators - "Bullsh*t Startups Say"

Panelists:

• Maren Lesche - Founder of Startup Colors & Vision Health Pioneer and Applied Data Incubators
• Pavel Romanenko - Head of GenAI Innovation at Capgeminni Invent
• Travis Scott - BDM Led at Finetech

Agenda: 17:30 - Walk in. Let's kick off the event with a refreshing drink! 18:00 - Welcome word 18:10 - Talk by Nicholas 18:30 - Small break 18:45 - Expert Panel 19:30 - Networking

For the community:

• Please contact Soraya Maan on LinkedIn to talk about future topics for the meetup, and collaborations or to recommend speakers.
• Contact Mario Savovski for pitching in our community round.

------ Code of Conduct We adhere to the Berlin Code of Conduct to ensure a welcoming and respectful environment for all participants. The event space operates under largely compatible Thoughtworks Meetups & Events CoC.

Accessibility The Location is accessible for wheelchair users. This includes the entrance (no steps to get into the location), toilets and the stage.

10 things you (probably) don't know about Databricks

Description

Learn 10 tricks, tips and hacks to better leverage databricks in your platforms.

Learn everything from how to configure your workspace with an undocumented to how to better optimize your delta tables

There should be something new for everyone

From the creator of @dailydatabricks on twitter

Snowflake and Databricks both aim to provide data science toolkits for machine learning workflows, albeit with different approaches and resources. While developing ML models is technically possible using either platform, the Hitachi Solutions Empower team tested which solution will be easier, faster, and cheaper to work with in terms of both user experience and business outcomes for our customers. To do this, we designed and conducted a series of experiments with use cases from the TPCx-AI benchmark standard. We developed both single-node and multi-node versions of these experiments, which sometimes required us to set up separate compute infrastructure outside of the platform, in the case of Snowflake. We also built datasets of various sizes (1GB, 10GB, and 100GB), to assess how each platform/node setup handles scale.

Based on our findings, on the average, Databricks is faster, cheaper, and easier to use for developing machine learning models, and we use it exclusively for data science on the Empower platform. Snowflake’s reliance on third party resources for distributed training is a major drawback, and the need to use multiple compute environments to scale up training is complex and, in our view, an unnecessary complication to achieve best results.

Talk by: Michael Green and Don Scott

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Summary

Five years of hosting the Data Engineering Podcast has provided Tobias Macey with a wealth of insight into the work of building and operating data systems at a variety of scales and for myriad purposes. In order to condense that acquired knowledge into a format that is useful to everyone Scott Hirleman turns the tables in this episode and asks Tobias about the tactical and strategic aspects of his experiences applying those lessons to the work of building a data platform from scratch.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you're ready to build your next pipeline, or want to test out the projects you hear about on the show, you'll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don't forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more. Your host is Tobias Macey and today I'm being interviewed by Scott Hirleman about my work on the podcasts and my experience building a data platform

Interview

Introduction How did you get involved in the area of data management?

Data platform building journey

Why are you building, who are the users/use cases How to focus on doing what matters over cool tools How to build a good UX Anything surprising or did you discover anything you didn't expect at the start How to build so it's modular and can be improved in the future

General build vs buy and vendor selection process

Obviously have a good BS detector - how can others build theirs So many tools, where do you start - capability need, vendor suite offering, etc. Anything surprising in doing much of this at once How do you think about TCO in build versus buy Any advice

Guest call out

Be brave, believe you are good enough to be on the show Look at past episodes and don't pitch the same as what's been on recently And vendors, be smart, work with your customers to come up with a good pitch for them as guests...

Tobias' advice and learnings from building out a data platform:

Advice: when considering a tool, start from what are you act

Maybe you got Access as part of Microsoft Office and wonder what it can do for you and your household; maybe you're a small business manager and don't have a techie on staff to train the office in Microsoft Access. Regardless, you want to quickly get your feet wet--but not get in over your head--and Access 2003 for Starters: The Missing Manual is the book to make it happen. Far more than a skimpy introduction but much less daunting than a weighty tech book, Access 2003 for Starters: The Missing Manual demystifies databases and explains how to design and create them with ease. It delivers everything you need--and nothing you don't--to use Access right away. It's your expert guide to the Access features that are most vital and most useful, and it's your trusted advisor on the more in-depth features that are best saved for developers and programmers. Access is sophisticated and powerful enough for professional developers but easy and practical enough for everyday users like you. This Missing Manual explains all the major features of Access 2003, including designing and creating databases, organizing and filtering information, and generating effective forms and reports. Bestselling authors, database designers, and programmers Scott Palmer, Ph.D., and Kate Chase are your guides for putting the world's most popular desktop data management program to work. Their clear explanations, step-by-step instructions, plenty of illustrations, and timesaving advice help you get up to speed quickly and painlessly. Whether you're just starting out or you know you've been avoiding aspects of the program and missing out on much of what it can do, this friendly, witty book will gently immerse you in Microsoft Access. Keep it handy, as you'll undoubtedly refer to it again and again.