Ishwarya Ravikumar

This blog post was inspired by a conversation with Abhishek Maheshwarappa, sparked by his thoughtful question about how to earn the trust of a team you didn't hire yourself compared to a team filled with your own hires. The TL;DR: basically the same way, but you'll get there faster if you understand the differences in context. https://lnkd.in/gJ2TqipK

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🔍 The Critical Role of Product Ownership in AI/ML Too often, organizations focus solely on technical excellence in AI/ML while overlooking the crucial role of sophisticated product ownership. Strong AI/ML product owners are the bridge between complex technical systems and business stakeholders. Key responsibilities of AI/ML product owners: 1. Risk Management - Set clear performance expectations - Define operational boundaries - Establish monitoring frameworks 2. Stakeholder Communication - Translate technical complexity - Build understanding of capabilities - Manage expectations effectively 3. System Design Oversight - Guide architecture decisions - Ensure scalable solutions - Balance technical debt The difference between success and failure in AI/ML often comes down to product owners who can navigate both the technical and business landscapes while maintaining stakeholder trust. Remember: Technical excellence alone doesn't guarantee business value. Strong product ownership transforms AI/ML capabilities into sustainable business solutions. #AI #ProductManagement #MachineLearning #Leadership #DataScience

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A lot can be read from this article. One enlightening point is the gain from inference time. This opens up new opportunities in services and businesses.

Crunching numbers by day 📊 and microwaving dinner by night 🌜, my life oscillates between data dashboards and recipes. Recently, my inner data geek decided to spice up a typical Friday evening with a little price comparison experiment—because nothing says 'weekend' like a good old showdown between Swiggy and Zomato over a Sicilian Stuffed Grilled Chicken . Here's the scoop: armed with memberships to both, I popped the same dish into my cart on each app. Initial score? Swiggy at ₹490, undercutting Zomato’s ₹520—a clean ₹30 win for Swiggy 💰. But hold onto your hats (and forks 🍴): the plot thickened at checkout. After the sneaky add-ons like GST and 'because-we-can' charges, the gap shrunk faster than my wallet, down to ₹547 for Swiggy and ₹560 for Zomato, just ₹15 more. And who did I crown the chicken champ for the night? Drumroll... Zomato! Why? Because when hunger strikes, time is money, folks . Zomato promised a 45-minute delivery versus Swiggy's leisurely 60. True to their word, Zomato had me devouring deliciousness in under 45 minutes⏱️. Moral of the story? While Swiggy played the cheaper date, Zomato got me to the good part quicker. And when it’s just me, my couch, and my growling stomach, those extra 15 minutes are golden 🌟. Stay tuned for more epic showdowns in the saga of ‘Where to Waste My Wages This Week’—because in this competitive market, it's all about savvy choices 🤔. May the best app win! 🏆 Zomato Swiggy

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I recently shared my views about "What GenAI can do" in a discussion with cross-functional teams, including business and product managers, designers, engineers, and data scientists. It was great to hear what they want and why they're interested in #GenAI. It was amazing to hear about its impact in democratizing accessibility and lowering entry barriers to problem-solving for coders and non-coders alike. It brought forth distinct preferences for coders and non-coders to tailor GenAI to meet the specific needs across diverse user groups. Non-coders prefer Personalized Assistants and Planners for tailored support to make daily tasks easier and more efficient, while coders lean towards Automation Agents and Co-Pilots to streamline development processes and boost productivity. While significant majority want to build new applications to harness the power of human-like cognition such as reading, writing, conversation, image generation, and speech synthesis. A few on either side of the spectrum doubt adaptability, potentially leading to unequal outcomes or discrimination and a lack of transparency and bias in decision-making. In the trilogy, GenAI with Large Language Models (LLMs) provides techniques and strategies to craft prompts that enhance the quality and relevance of responses, adding context through simple conversations to summarize, infer, transform, and expand on them. The sequel comes with a host of new capabilities while expanding beyond prompting, extending chain-of-thoughts and responses with multi-agent workflows to reflecting, critiquing, and correcting itself, driving huge demand for GenAI human-like pairs and companions. It’s fun for non-coders to build, keeping it less predictable and unconstrained than traditional systems, without getting bogged down about codifying it. The coders are inherently poised and want more methods to madness, and actions to fine-tune its behavior to be specific. Despite diverse needs and motivations, the discussion got swayed by unraveling expectations from this trilogy: - When can GenAI solve for itself, the need to pre-process human-generated training data and align itself to further fine-tune with human values and preferences? - What legal and compliance frameworks affect HOP (human-in-loop) as critically important or a close approximation for trust and ethics? - What would the GenAI + HOP (human-in-loop) pair look like from a future workforce perspective, obviously in terms of HR and other benefits? In those ending minutes, it turned philosophical – Does a universal context of understanding and reasoning human values and conscience exist beyond a doubt? Having watched “Oppenheimer” earlier helped, like Nolan’s mastery with end scene, the movie turned out, a blockbuster. 🎯 🙌 #GenAI #LLMs #DemocratizingAccessibility #EnhancingProductivity #CodersAndNonCoders #PersonalizedAssistants #CoPilots #AutomationAgents #MultiAgentWorkflows #TrustEthics #HumanInLoop #FutureWorkforce #OppenheimerMovie

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Excellent, thought provoking article from Karim at Digital Data Design (D^3) Institute at Harvard. Be sure to read and share. 💻 Will your firm’s core expertise be democratized through AI? ⚖️ What are durable ways to create value that can be augmented, but not replaced, by AI? Interested in hearing perspectives from #healthcare peers! #digitaltransformation #digitalstrategy #aistrategy #automation #ai #ml #datascience #expertsystems NVIDIA OpenAI Anthropic Google Microsoft #hospitals Becker's Healthcare Scottsdale Institute The Health Management Academy Harvard Business Review Massachusetts Institute of Technology STAT

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Check out this Q&A with Capital One’s Head of AI Product, @Aparna Sinha, where she shares advice on building enterprise AI/ML capabilities. The conversation touches on the importance of product management in AI, Capital One’s AI/ML priorities, advice for those looking to break into the industry and so much more! Read it now on the Capital One tech blog

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Sharing some insights from this thought-provoking article by Ganes Kesari on building a data-driven culture! Key takeaways include: 1) the critical importance of aligning data initiatives with business objectives, 2) mastering the art of storytelling to drive adoption, 3) and defining success metrics early on for sustained success. Kesari's emphasis on executive support, grassroots initiatives, and meticulous execution resonates deeply with leaders navigating the data landscape. Let's leverage these insights to cultivate thriving data-driven cultures within our organisations. #DataDrivenLeadership #BusinessStrategy #LearningFromData

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For Optimization problem, getting a well behaved Cost-function, is like finding a right soul-mate. Smooth, Continuous, Differentiable, Bounded, Convex.. Cost-function ! The one, which is loyal to a single Global-Minima, and never gets trapped into shady local-minimas.. Sarv-gun sampan! Just thinking about it can dilate Mathematicians' pupils, secret oxytocin and increases dopamine-levels. #optimization #statistics #datascience #funatwork

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Women in Big Data Virtual is hosting a webinar on 15-November at 7.30 am ET! Join Shoshi Kalderon to uncover how you can ignite your confidence and purpose by embracing the power of risk-taking in your career! This engaging session will motivate you to step outside your comfort zone to ignite your confidence and inspire purposeful action. To register go here: https://lnkd.in/eBWQvPyx hashtag #Data hashtag #WomenInData hashtag #WomenInBigData hashtag #BigData hashtag #DataScience hashtag #Career hashtag #RiskTaking hashtag #CareerGrowth hashtag #WomenLeaders hashtag #WomenInTechnology Women in Big Data …more Activate to view larger image, Activate to view larger image,

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Join me for an insightful session on May 10th, 2024, where I'll be sharing how Games24x7 has leveraged Databricks to simplify the creation and deployment of ML models. I'll discuss our approach to automating MLOps pipelines and developing a solution supporting practitioners and managers throughout the ML lifecycle. From data preprocessing to model training, deployment, and monitoring, we'll explore how automation and AI can streamline these processes, freeing up time for experimentation and innovation. Looking forward to having you join us for this engaging discussion! #ML #AI #Databricks #Games24x7 Registration link: https://lnkd.in/gNTvMw_D

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Kartik’s words hold very true: “It's almost impossible to be AI forward if 4 different groups need to say Yes.“ In the current uncertain economic environment change inertia is the norm. The hype is beginning to “dissipate” as executives realize that there is a lot of effort and cross functional coordination needed to unlock big value from AI (not just GenAI). Data/AI teams are challenged to move past PoCs or small scale pilots as a result. I am interested to learn how organizations are dealing with this inertia. Please reply with your thoughts.

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Haven't written code in a long time, and I approached my first Python and Data Science project with great caution. Today, I received a full score of 60/60 on the FoodHub EDA project and couldn't resist sharing. I hope this encourages everyone who wants to learn but hasn't started yet. Believe in yourself and take that first step! #Python #DataScience #AIML #Seaborn #ExploratoryDataAnalysis #Numpy #Pandas

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𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐓𝐢𝐦𝐞 𝐙𝐨𝐧𝐞𝐬 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚: 𝐌𝐲 𝐆𝐥𝐨𝐛𝐚𝐥 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 When I took on a global role managing data science and analytics teams from the US West Coast, with team members in India and stakeholders across the US, I knew I was in for a challenge. What I didn't expect was how it would transform my approach to leadership and work-life integration. 🌏 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐂𝐡𝐞𝐜𝐤: My first month was a whirlwind. 6 AM calls with stakeholders in India and the US East Coast, full days engaging with local clients and team, and late evenings aligning again with the team in India. I was living in all time zones at once, and something had to change - not just our processes, but my entire leadership philosophy. 🔄 𝐓𝐡𝐞 𝐏𝐚𝐫𝐚𝐝𝐢𝐠𝐦 𝐒𝐡𝐢𝐟𝐭: Instead of trying to be everywhere at once, I focused on building a culture of autonomy, trust, and continuous innovation. Here's what worked: 1. "𝐅𝐨𝐥𝐥𝐨𝐰-𝐭𝐡𝐞-𝐒𝐮𝐧" 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰: We implemented a 24-hour work cycle, with each team advancing projects during their day. Result: 30% faster project completion and seamless client support. 2. 𝐂𝐮𝐥𝐭𝐮𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Invested time to help team members understand each other's work cultures, crucial for our India-US collaboration. Outcome: Conflict resolution time reduced by 50%. 3. 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞 "𝐂𝐨𝐫𝐞 𝐇𝐨𝐮𝐫𝐬": Established a 2-hour overlap for all teams, allowing flexibility outside this window. This meant early mornings for me, but empowered autonomy for the team. 4. 𝐃𝐚𝐢𝐥𝐲 𝐒𝐭𝐚𝐧𝐝-𝐮𝐩 𝐌𝐞𝐞𝐭𝐢𝐧𝐠𝐬: Implemented daily check-ins to ensure the team understands the tasks at hand. This created an agile work environment, improving responsiveness and alignment across time zones. 5. 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐚𝐤𝐢𝐧𝐠: Leveraged our analytics expertise to optimize our processes, from meeting scheduling to workload distribution. 🚀 𝐓𝐡𝐞 𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡: Six months in, we didn't just adapt - we thrived. Our cross-continental team outperformed localized teams by 25% on our last project, delighting our West Coast clients with round-the-clock innovation. 🧘‍♂️ 𝐓𝐡𝐞 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐉𝐨𝐮𝐫𝐧𝐞𝐲: This role taught me that balance isn't about perfect time allocation - it's about energy management. I now start my day with strategic planning, not just reacting to overnight emails. Counter-intuitively, this has made me more responsive and resilient. 🤔 𝐓𝐡𝐞 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Global leadership in data science isn't just about managing across time zones; it's about creating a unified vision that transcends geographical boundaries and leverages diverse perspectives to drive innovation. It's challenging, but the personal and professional growth is unparalleled. What's your experience with global data science roles? How do you balance local client needs with a global team?

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Analogy : Bollywood and Data-Science led Growth-Optimization, for Digital Lending A typical superhit Bollywood-love-story 1) Hero likes a girl. There is an-equally-weirdo Hero's-friend. Who suggests Hero to write her a love-letter and express his holy feelings. 2) They arrange her address through college-records and stalking. 3) They also rented a professional-kabootar with a succesful track-record of distributing love-letters (unlike Tuffy, stupid pomeranian ). However, Hero is worried too. He self talks, WHAT IF : 4) Letter reaches Heroine's-place at wrong-time? (Timing should be such that Heroine is at home and Amrishpuri-jee should be out for feeding pigeons.) Or, 5) Heroine throws it without even opening, assuming it as a pamphlet seeking chanda for bhandara ? Or, 6) Heroine just hates Kabootar. She shared with her therapist that Bauji likes kabootar more than her Or, 7) She Opens letter, but doesn't know how to read Hero's Haryanwi letter Or, 8) She reads and finds it too bland to respond ! No poem, No shayari, No pampering.. Or, 9) Heroine expects a rose, a choclate , a teddy not just a letter and what if, Heroine is materialistic and expects a latest iphone Or, 10) She likes everything but doesnt know ki ab karna kya hai. While Pigeon is dumb literally, what if she is metaphorically. Or, 11) She got trust issues and thinks its a prank 12) Our hero continues thinking, "if she doesnt respond, should I daily send pigeon with a letter, or send multiple pigeons at a time." "What if she just gets annoyed by doing this!" Aur utney paisey bhi to nahi hai.. Asli bollywood hero to gareeb hi hota hai! (Refer comments to see the analogy)

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The New Power Players in Business: Thinkers Who Do There’s a major shift underway in companies around the world, transforming how talent is valued and rewarded. In the past, we had “idea people” who spotted opportunities and “execution people” who made things happen. But today, those divisions are dissolving, and companies are looking for a different kind of professional—someone who can both identify opportunities and roll up their sleeves to bring them to life. Ideas are everywhere. Strategy decks, vision documents, and proposals abound, but without action, they’re just well-dressed wishful thinking. Simply building things, closing out tickets, or running analyses may look productive, but unless these efforts drive meaningful outcomes, they’re simply checking boxes. Organizations are recognizing that without a direct connection to impact, the old metrics of “busy work” don’t move the needle. Enter AI: the game-changer fast-tracking this transition. Artificial intelligence is reshaping how we work, fueling the demand for hybrid roles. AI tools can quickly analyze data, reveal insights, and even automate repetitive tasks, freeing professionals to focus on higher-level strategy and execution. But here’s the key: AI doesn’t replace the need for vision. It amplifies the need for professionals who can interpret insights, connect the dots, and act on them. Those who can leverage AI to enhance their skills are leading the charge, turning raw data into real impact. Leading companies are evolving their approach to work and talent accordingly. They’re not simply counting tasks completed; they’re looking for impact—measurable outcomes that benefit customers, increase revenue, or streamline operations. Amazon’s “two-pizza teams” exemplify this approach. These small, cross-functional groups own a product end-to-end, staying close to both the customer and the outcome. They’re empowered to pivot, experiment, and above all, deliver. This shift toward hybrid roles isn’t just a trend; it’s a response to a more dynamic, AI-driven world where the old boundaries between ideation and execution simply can’t keep up. When professionals are able to both think strategically and execute effectively, they become irreplaceable. For those leading this transformation, you’re setting a new standard. The future belongs to those who blend insight with action, who understand the “why” as deeply as the “how.” For anyone looking to thrive in their career, don’t just focus on acquiring skills or knowledge in isolation. Develop the ability to see the big picture, leverage AI to deliver value, and make a tangible impact. Blend strategy with execution, because that’s where real value lies. This is the future of impactful work. #leadership #innovation #careerdevelopment #futureofwork #AI

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If you are at OpenSearch Con SF this week, do check out these 2 awesome talks on Data Prepper !! https://lnkd.in/gFCjVs94 https://lnkd.in/gS_ipG4T David Venable Taylor Gray Dinu John have done some great work on adding these functionalities to Data Prepper and Amazon OpenSearch Ingestion over the last 1 year and it will be very insightful to understand their journey. #opensearch #amazon #aws

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I recently came across some MMLU (Massive Multitask Language Understanding) benchmarking data that compares performance of different Large Language Models (it tests ChatGPT models on things like math and chemistry and history). While it's interesting to see which models score well (spoiler: GPT-4o outperforms others on average), what caught my eye is Chain-of-Thought (CoT) prompting is MUCH more effective that direct prompting across different models. Learn more about different prompting techniques here. https://lnkd.in/eCak8WCn Data source: https://lnkd.in/eWQu-B2W #AI, #GenerativeAI, #Analytics, #FutureofWork

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Major AI adoption and visibility, can be achieved, By calling your teams' existing-models as AI Models. #ai #machinelearning #analytics #predictivemodeling #selfmockery

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