John Kim

Looking forward to Amazon Research Days on Nov 21 (Thur) featuring an exciting lineup of talks from Industry (Anthropic, Sarvam AI, Amazon) and Academia (UNC Chappel Hill, Australian National University). Not surprisingly, this year, we have multiple talks on training foundational GenAI models, and applying multimodal GenAI models to a wide range of domains such as product image generation, 3D construction, speech recognition, etc. Amazon Research Days is an ML conference held annually that brings together researchers and practitioners from academia and industry to exchange and brainstorm new ideas, learn about state-of-the-art ML techniques, and network to build a strong ML community. #amazon #amazonscience #amazonresearchdays #ai #ml

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Meet Lucy Zou, a Machine Learning Scientist III at Expedia Group. Lucy shares with WiDS Puget Sound insights from her education and career in data science - here are 3 key takeaways: 🔑 Adaptability and continuous learning are crucial for navigating career transitions in tech. 🔑 Building a diverse network and seeking mentorship are invaluable for career development. 🔑 Embrace challenges, take calculated risks, and seize growth opportunities to thrive in the dynamic tech industry https://bit.ly/3ydfRVz

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#PET #OWNERSHIP? Reminder of proxy variables Looking at map of pet dog ownership tells… … easily mistaken story. The clear geographical delineation in graph (matching the cold war Iron curtain line) can easily sway you into cultural root cause. 🤦🏻‍♂️ And that would be a great example of the correlation fellacy. Because 25 years after the Iron curtain is down is 2-3 generations of the dogs, long enough time for the political factors to wear off, if they ever had effect. 📖 The real learning of this graph is that there might be hidden factor that is not obvious and still it has profound effect on the actual “target variable”. 🐕 In our dog situation the underlying hidden root cause for the pet ownership is the share of agriculture on total labor market 1.5-2 generations back. Because if you had a farm, you were more likely to get used to living with animals or even needed some (shepherd dogs anybody?). The 2 generations lag is important, because you don’t make pet purchase decisions until you live in your own household and that is roughly, when you start your family (hence when you pass on the generation baton). 🔗 This phenomena is also known as #proxy #parameter and has significant impact also (in positive means) in #MachineLearning. Because proxy parameters are strong predictors, but are insights that markets tend to oversee. So if you know them, proxy parameters give you edge. 💡 Now you know the phenomenon I have a #puzzle for you: It is proven by data in many insurance companies (I worked on one of such studies) that #Zodiac sign is very strong predictor of the #car #accident rate. Yes, really true! Can you guess what it is proxy parameter of?

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Certus is currently interviewing Analytics Engineers to join our team, and I think it's fun to drop bread crumbs for those who are doing their research. If you end up interviewing with me, I will be looking to see how you answer a whiteboard challenge. Do you think like a data engineer, focused on where the data is coming from, how it is staged, and what the ingestion pipelines will need? Do you think like a data analyst, focused on using the data to support end user insights? Or do you fit in the middle asking the right questions to know where the data is coming from, which tools we can use for ingestion, and what business transformations are needed to support end user insights and capabilities? Hint: Do the third. It's what the job will require and the skill set that will make you a super hero at Certus. 😁

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What does marketing in a post-cookies world look like? https://lnkd.in/gN7BPfjQ #datascientist #dataanalyst #cfo #cmo #productmanagers #founders #cofounders #cxo #ceo #cpo #bizops #revops #operations #b2bsaas #marketing #marketingmanagers

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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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https://lnkd.in/gYbfckU7 #RecommendationEngine : I have analyzed Big Basket's customer buying behavior data and created a recommendation engine that suggests items you might be interested in. this is programmed in R language and hosted in Shinny. Check it out! I would greatly appreciate any feedback.

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Seismic’s AI enablement assistant, Aura Copilot, is now available for early access. Teams can now get faster access to answers and content, automate tedious tasks, and gain insights for better business decisions. Aura also now integrates with Microsoft Teams and Microsoft 365 Copilot to give teams in-the-moment answers and content recommendation. https://lnkd.in/d_mSbDXK

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This is the first post in the series of "What I'm learning as a manager". The first episode is on accountability. This first post on accountability was written two years ago and it was heavily inspired by Jigang Wang, who introduced me to the concept by sending me some papers from doers, and Ben Hills, who showed me how to focus on learning instead of the blame-game. https://lnkd.in/e6TbXKUR

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My first dbt Coalesce conference was an amazing time. It was good to catch up with TJ Tripp, Zoe (Yi) Liang, and Zachary Lancaster, finally meet Kevin Hu, PhD and Jacob Mulligan in real life, and meet lots of new cool people in the data world along the way. My top 3 Learnings from the conference are: 📈 The semantic layer has come a long way in the past year and it's the gateway to the future of analytics (such as powering AI analytics assistants). 📖 Data literacy isn't a one-and-done training but its an ongoing mission. 🎤 Extensive slide notes are really helpful when 100 people are starting at you and your mind goes blank. What did y'all enjoy at Coalesce this year?

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Came across an insightful article that delves into the methods used to understand population parameters from sample statistics in a simple manner. Check out the article here: https://lnkd.in/g_C9_r9y While statistical significance is the buzzword in town, keeping the context of practical significance in mind while inferring conclusions is an important aspect.

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🚀 In Data Science: Progress Over Perfection Data science teams often fall into the trap of striving for the "perfect" model but in today’s fast-paced world, speed, and adaptability win. A good model deployed today beats a perfect model delivered too late. Iteration, feedback, and agility drive real impact. Focus on solving problems, not chasing perfection. Progress is the path to success. Let’s build, adapt, and improve! 💡 #DataScience #Agility #ProgressNotPerfection #BusinessImpact #Innovation https://lnkd.in/gwBsf37F

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"If it takes less than 2 minutes to get something done, you should do it right away". This is the effective 2-minute productivity hack. It takes less than 2 minutes to RSVP for the TUG, details are in the original post from Jenny. In return, you get to attend this event and takeaway valuable Tableau Prep tips! Thank you Jenny and the Tableau User Group team for the opportunity 🙏 #DataFam #Tableau #Learning

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So I found this podcast on - Supercharging AI with Master Data Management Here is a maybe slightly overly simple analogy... Imagine your AI is a race car. Your data? That's the fuel. And MDM? It's the refinery that ensures your fuel is pure, powerful, and perfect for peak performance. Here's how MDM unlocks the true potential of Generative AI: 1️⃣ Enhanced Predictive Analytics: More accurate forecasts and insights 2️⃣ Automated Content Generation: Personalized, accurate, and consistent 3️⃣ Intelligent Process Automation: Smarter workflows and decision-making 4️⃣ Advanced NLP: Better understanding and generation of human language 5️⃣ Real-time Decision Support: Instant insights based on trusted data 🚀 Success Story: A global retailer increased customer engagement by 40% using Gen AI powered by robust MDM. 🤔 How could AI supercharged by MDM transform your business? Share your thoughts! #GenerativeAI #MDM #AIStrategy #DataDrivenAI #FutureOfBusiness https://lnkd.in/ghUz7P2e

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Sigma's Q3 product launch is happening tomorrow (9/17)! We're making announcements on multiple topics that spark joy for my Data Engineering background. Data Models: Semantic modeling in Sigma has never been easier, with our new launch of Data Models and integrations with partners like dbt labs and cube.dev. SQL Editor: Write complex SQL (lateral array-flatten anyone?) and reference Sigma objects in the SQL editor. All integrated natively as a first class experience in our UI. Ecosystem Integrations: Our new integration with Glean bridges the structured <--> unstructured data divide. Glean is the best enterprise search product in the business, the power of our products together is going make a lot of people's lives much easier. There's plenty more - join up using the link in the comments!

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Today data science teams are facing constantly shifting demands and challenges. That said, I wanted to share why so many Fortune 500 companies are working with Abacus.AI. We're uniquely positioned to help you overcome these challenges and drive innovation. 🔗 Complex Integrations? Simplify your workflow with Abacus. Our platform integrates effortlessly with your existing data pipelines, freeing you from technical bottlenecks and letting you focus on what matters—insight and innovation. 📈 Scaling Woes? Whether you're expanding a small project or managing extensive AI deployments, Abacus scales with your needs, ensuring your models perform optimally without the usual growing pains. 🛠️ Need Cutting-Edge Tools? Stay ahead of the curve with our comprehensive suite of advanced tools and libraries. Abacus empowers you to experiment, iterate, and deploy models faster, turning your ideas into reality with unmatched efficiency. 🤝 Feeling Isolated? Join a thriving community of data science professionals and benefit from our dedicated support team. At Abacus, you're never alone in your journey—we're here to help you tackle any challenge. 🔒 Security Concerns? Rest easy knowing your data is safeguarded with top-tier security measures and compliance with global standards, allowing you to innovate with confidence. 🚀 Join Us in Redefining AI Excellence! At Abacus, we're not just providing a platform; we're offering a comprehensive solution to the everyday challenges faced by data science professionals. Let's push the boundaries of AI together and transform the world, one algorithm at a time. Let's connect and explore how Abacus can elevate your AI projects and help you conquer your toughest challenges! Read more here: https://abacus.ai/ hashtag #DataScience hashtag #AI hashtag #MachineLearning hashtag #Innovation hashtag #AbacusAI hashtag #Technology hashtag #MLOps hashtag #ArtificialIntelligence hashtag #Data

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Another week of interesting mentoring sessions. The recurrent theme was vulnerability. My thoughts on how embracing vulnerability can help you lead with Authenticity. To book an appointment please click here - topmate.io/syed_atif_umar/ https://lnkd.in/grm2km9p

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📖 Weekend Reading 📖 This week we had profiles on two highly influential figures in tech: Palantir Technologies CEO Alex Karp, and Anduril Industries founder Palmer Luckey. They are in some ways very different individuals, but in others they are radically aligned. 1️⃣ The first piece, on Karp, comes courtesy of Maureen Dowd in The New York Times. Karp appears to have an odd background to be leading a defense-oriented technology company. But what allows the German philosophy doctor to steer Palantir is an extremely principled and unapologetic geopolitical stance: “we have a consistently pro-Western view that the West has a superior way of living and organizing itself.” An interesting observation: “I think we’re in an age when nuclear deterrent is actually less effective because the West is very unlikely to use anything like a nuclear bomb, whereas our adversaries might…Where you have technological parity but moral disparity, the actual disparity is much greater than people think.” Interestingly, while Karp is vocal in his criticisms of some of the Democrats’ policies, he is firmly in the Biden / Harris camp (as a voter and donor) and often butts heads against Palantir’s chairman Peter Thiel when it comes to politics. https://lnkd.in/dMz7BWiV 2️⃣ Next up, Jeremy Stern (Tablet Magazine) profiles Palmer Luckey, another eccentric, highly principled, endlessly fascinating individual. Luckey, who created the Oculus Rift at 19 in a camper trailer, favors anime and collecting decommissioned nuclear missile bases over Karp’s tai chi. But like Karp, he is steadfast in his belief that the West must continue to out-innovate its geopolitical foes (Iran, Russia, China). He also has similar views on rebuilding deterrence, an idea that is core to the company’s mission: “Anduril was founded on the idea the US government had to be reformed so that it could buy deterrence again—and that the only way to deter wars is to be so clearly capable of winning them that no rival power tries to harm core US interests through the use of force.” Anduril is also backed by Peter Thiel, and here, while pursuing what appears like the same objective, Luckey finds himself on the opposite end of the political spectrum as Karp, having famously supported Donald Trump since his first real electoral bid in 2016. I'd recommend this article even just to get a more holistic view of what happened during that time, when Luckey was ousted from Facebook. https://lnkd.in/dpRpnGRF #palantir #ai #llm #defensetech #americandynamism #ukraine #anduril #peterthiel #vr #oculus #fintech #payments #upi

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