Alice Zhao

Fun little profile piece on the Funnel blog about the roundabout way that I came to be doing what I now do (which I love doing, BTW). Check it out at https://lnkd.in/eztMS2w8

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Ever struggled with container layouts in Tableau? You're not alone. Today our Next-Level Tableau class tackled container mastery head-on. We transformed messy bike accident data into an insighrtful, responsive dashboard for the City of Austin. Trust me...Managing containers is the secret sauce behind professional-looking dashboards. Want to see how we did it? Download our workbook and explore the container setup yourself. 👉 Since you read this far, what's your biggest container challenge in Tableau? Share below! https://lnkd.in/edhxmbkm

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How much are people spending on platforms that do data lineage tracing? I was able to build a Jupyter Notebook that uses LLM services to read view SQL and trace the lineage back recursively to through logic, CTEs, subqueries, joins, nested views, and ultimately to the data dictionary to build a lineage JSON structure. That JSON then gets converted to a plain English definition of the field. Very useful! #GenAI

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Whoever claims building RAG systems is easy has never built one at scale. 🤯 There is a significant gap in the literature and existing frameworks. RAG architectures are designed for POCs, not for hundreds of thousands of documents or supporting tens of thousands of users. Designing these systems requires a cross-functional team with software engineering and data science skills blended with a product mindset. Earlier this summer, I spoke at the Databricks AI Summit, highlighting guiding principles we are taking at CVS Health for building enterprise-grade RAG systems. Every company is unique, and while there is no one-size-fits-all solution, here are three principles that will help you on your journey: 👉 Spend time on discovery—It's tempting to dive in and start building immediately, but you may get lost in the desert without doing your due diligence and understanding what delivers the most ROI. Our team spent approximately a quarter on discovery. 👉 Design for modularity—The LLM space is changing rapidly, and you want to build reusable components that can easily be swapped out or upgraded when something better comes along. 👉 Humans + Machines—Technology alone can not solve the knowledge search problem. Develop processes and governance to help teams curate the data, which will be ingested into your system. These lessons are just the tip of the iceberg. Watch my full Databricks presentation and leave your comments and questions below to learn more tips and tricks. #RAGSystems #EnterpriseAI #DataEngineering #MachineLearning #AIArchitecture #AI #DataScience #LLM #SoftwareEngineering #AIAtScale #KnowledgeSearch #TechLeadership #AIFrameworks #AIInnovation #DatabricksAISummit #ProductMindset #CVSHealth

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Excited to try this one out! I'll be speaking at Minnesota Technology Association Tech Connect in a few weeks on Open Source Large Language Models and will discuss this and many more. Take read into why Snowflake's Arctic should be a model you explore with Streamlit. I'll give you a hint. "Arctic is on-par or better than both Llama 3 8B and Llama 2 70B on all metrics, while using less than ½ of the training compute budget." Look for covering in the upcoming Applied Artificial Intelligence Weekly News issue! https://lnkd.in/gM-wECeu

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https://lnkd.in/gSAcfbxa Part 10 of my series Why Data Science Projects Fail is available today on INFORMS Analytics Magazine. Tom Davenport once said that "Models make the enterprise smarter, but models embedded in mission-critical business processes and systems make the enterprise more economically efficient." That is the end game of data science models--- generating significant business value and economic impact, and to achieve that, repeatably, reliably, and sustainably, requires that models are deployed as systems. Today's installment addresses the most challenging part of implementing model-based systems: Getting from Sandbox Model to Production System. Part 10 encapsulates many aspects of data science project complexity that were addressed in prior installments such as data availability, project management, and change management, and introduces new dimensions that determine how long it will take and how much it will cost to get from a sandbox model to a fully deployed and operational enterprise-grade production system. Levels of Dynamism, Integration, Mission-Criticality, and Problem and Model Complexity are addressed as four of the key determinants of how easy or difficult it will be to get your model from the sandbox to being a fully deployed, enterprise system, and integrated as part of the overall corporate technology ecosystem. The article provides examples of projects that span the continuum of relatively easy and inexpensive to deploy to extraordinarily complex and expensive to deploy. Successfully deploying and operating data science models as part of enterprise systems requires a holistic, comprehensive team of not only data scientists, but also data engineers and analysts, business process (domain SMEs) analysts, managers and executives, cloud engineers, ML Pipeline & Ops engineers, change management pros, software engineers and system integrators to fully realize the benefits such models promise.

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Do you think Tableau is easy for me? Think again! #MakeoverMonday was a struggle! Goal: Replicate the original viz. I knew I need to create path to connect the US at the center to each country. I wanted to make the US the center of the of the map. Sounds simple, right? I made it way harder than it needed to be. • I overthought the data prep. • I forgot how to create the paths. • I couldn't get the math right to offset the longitude. The coloring and labeling was straightforward for me. Adding the US as a circle in the middle was pretty simple. To overcome the problems I faced • I looked at a previous visualization I created using paths. • I opened the workbook and the data to see the configuration. • I replicated that set up by adding a second set of records to the data set. • Viewers on the livestream helped me figure out the math to offset the longitude. The simplest way to explain the location of each country is to think of the lat/lon for the US as 0,0. Then you compare that to the lat/lon for every other country to create the offset. I got there in the end. What do you think? Was the juice worth the squeeze? ➡️ Viz - https://lnkd.in/etYR2Jbg 🎥 Livestream - https://lnkd.in/ePxGQT_a (if you want to see me struggle A LOT)

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Ten years ago, 𝗛𝗮𝗱𝗹𝗲𝘆 𝗪𝗶𝗰𝗸𝗵𝗮𝗺’𝘀 𝙏𝙞𝙙𝙮 𝘿𝙖𝙩𝙖 revolutionized R coding. Since then, tidyverse has changed the game: • *𝟳𝟬% 𝗼𝗳 𝗥 𝘂𝘀𝗲𝗿𝘀* 𝗻𝗼𝘄 𝗱𝗲𝗽𝗲𝗻𝗱 𝗼𝗻 𝘁𝗶𝗱𝘆𝘃𝗲𝗿𝘀𝗲 𝗳𝗼𝗿 𝗱𝗮𝘁𝗮 𝘄𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴. • 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝘀𝗮𝘃𝗲 𝘂𝗽 𝘁𝗼 *𝟱𝟬%* 𝗼𝗳 𝘁𝗵𝗲𝗶𝗿 𝘁𝗶𝗺𝗲 𝗼𝗻 𝗰𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝘁𝗮𝘀𝗸𝘀. • 𝗜𝘁'𝘀 𝗺𝗮𝗱𝗲 𝗥 𝘂𝘀𝗲𝗿-𝗳𝗿𝗶𝗲𝗻𝗱𝗹𝘆, 𝗲𝘃𝗲𝗻 𝗳𝗼𝗿 𝗻𝗼𝗻-𝗰𝗼𝗱𝗲𝗿𝘀. The verdict? Tidy data = better insights. Thank you, tidyverse, for bringing order to our data chaos! #tidyverse #rstats #tidydata #datacleaning #datawrangling #datascience #dplyr #ggplot2 #dataanalysis #statstools #rstudiotips #dataworkflow #codebetter #dataviz #datatools #Rprogramming #posit #cleanercode

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Producer, over Zoom: Pitch me. Me: It's a sitcom about two data scientists who are trapped together in a tiny apartment during a pandemic, frantically working on a deep learning problem while trying to not kill each other. It's called "Socially MNISTancing." Producer: Get out. #iykyk #ai #deeplearning #machinlearning #artificialintelligence

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🏃‍♂️I'm a little late to the game, but I'd like to thank IMDb.com for featuring my Breakfast Club viz among several others on their website as part of the #dataplusmovies project with Tableau. 🙏 It's an honor to have my work alongside such noted Tableau developers. Links in comments. 🏗→🧠 Build to Learn! 💭🚶‍♀️🚶‍♂️ Follow for more. #tableau #data #analytics #VizoftheRay

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🤔 How many times have you reinvented the wheel in Tableau?   I thought the radial jittering in my IronViz qualifier was pretty cool.   But it looks like my IronViz mentor, Samuel Parsons, had done the same thing 5 years prior.   At least I settled on the same chart name as he did, so that's what it is - a radial jitter.   These are good for infographics showing a distribution that concentrates around a central point.   📝 In the near future, I hope to make a simple dashboard showing three variations of this chart type, including evenly distributed dots as well as dots on the periphery.   In this #makeovermonday case, I thought the idea of concentrated dots meshed well with firearms - I got the idea from seeing the bee swarms by Shangruff Raina and Sherzodbek I.   Anyhow, I adjusted the worksheet size so that the output is slightly elliptical to fit well in the target image.   ⚖️ I thought I'd go down the route of comparison to domestic purchases because I knew those numbers were high. They've been higher than 2022, but I used that year because it was the last year of the other range and I wanted something semi-current.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #makeovermonday #tableau #data #analytics #VizoftheRay

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🤔 How many times have you reinvented the wheel in Tableau?   🦚 I thought the radial jittering in my IronViz qualifier was pretty cool.   😲 But it looks like my IronViz mentor, Samuel Parsons, had done the same thing 5 years prior.   🤷 At least I settled on the same chart name as he did, so that's what it is - a radial jitter.   These are good for infographics showing a distribution that concentrates around a central point.   🛠️ I made another simple dashboard showing three variations of this chart type, including evenly distributed dots as well as dots on the periphery.   In this makeovermonday case, I thought the idea of concentrated dots meshed well with firearms - I got the idea from seeing the bee swarms by Shangruff Raina and Sherzodbek Ibragimov.   Anyhow, I adjusted the worksheet size so that the output is slightly elliptical to fit well in the target image.   ⚖️ I thought I'd go down the route of comparison to domestic purchases because I knew those numbers were high. They've been higher in the past than in 2022, but I used that year because it was the last year of the other range and I wanted something semi-current.   🏈 This is a Throwback Thursday viz!   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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Your Tableau graphs don’t work for slideshows. Unless you design with that destination in mind. Let me show you a SMDH example and provide some clear advice on how to make this work.

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I have been utilizing and maximizing Tableau containers in building my dashboards at work but I have learned and appreciated it more during today's Next Level Tableau session with Andy Kriebel. Thanks, Andy and NLT-mates for the productive learning session! 🤗🙏🏼 #NLT #Tableau #containers

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🔮 Identifying Emerging Trends: Pro Tips in Future-Proofing Your Career Want to stay ahead of the curve? Here's my playbook for spotting the next big thing: 1️. Look for Pain Points: Databricks didn't just create technology - they solved a massive problem in data management and AI integration that companies were struggling with. 2️. Follow the Smart Money: When top VCs and tech leaders start clustering around a technology, pay attention. Databricks' early backing from major investors was a clear signal. 3️. Watch Ecosystem Dynamics: Notice how Databricks seamlessly integrated with cloud providers, machine learning frameworks, and enterprise needs? That's a hallmark of a trend with staying power. 4️. Observe Talent Migration: When top engineers and researchers start gravitating toward a platform, it's a strong indicator of potential breakthrough technology. 5️. Track Solving Complexity: The most promising trends don't just add features - they dramatically simplify complex processes. Databricks did this for big data and AI workflows. 🏆 Pro Tip: Trends aren't just about technology - they're about solving real-world problems at scale. 💡 Your competitive advantage isn't just knowing the trend - it's understanding the underlying shifts driving it and how to use it to impact business. Who else is tracking the next big technological wave? 👇 Comment below.

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Graphic from McKinsey & Company I saw this weekend. Regardless of industry at least 70% of companies are exploring or already leveraging AI. The largest portion falls into "tried" it phased. This strikes me because it's what I see with a lot of clients. Jump on the bandwagon without a plan, alignment, or long term view. They don't see results right away, or don't know how to quantify them. Make sure you are executing for the short term, but planning for the long term. https://lnkd.in/eXmnFeXH #aws #startegy #innovation

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What's in a name? Falko Schulz creates a SAS Visual Analytics infographic on the popular names in the US collected by the Social Security Administration from 1920 to 2022. There's even a link to the interactive report if you want to delve into the data. #infographic #dataviz #SAS #SASviya #SASusers #SAStip #data https://lnkd.in/guhBVUPU

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NotebookLM doing Podcasts is the same sort of thing Mark Craddock has been doing recently. But kudos to the person who fed his last credit card statement into it and got a lecture that he spends too much on Uber. Original (amazing) post. https://lnkd.in/eu9V5FEX Google's NotebookLM is the current best "wow this is amazing & useful" demo of AI Here I gave it the entire text of my book, it turned it into a podcast, a study guide, FAQ, timeline & quite accurate chat Listen to the first few minutes of the "podcast." Seriously, just listen.

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