Michael(tm) Smith ED09 presentation
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The document discusses best practices for semantic markup. It provides guidance on using semantic elements like <div>, <span>, <a>, <img>, lists, forms, and specific semantic elements properly. It also covers topics like accessibility, maintainability, validation, HTML vs XHTML, and keeping up with HTML5 and browser standards. The document emphasizes using semantic markup that encodes meaning, is device-interoperable, and accessible to all users.
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At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. ๐
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! ๐
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Common symptoms of an ineffective Client Partnership (CP).
Key reasons why CP fails.
Step-by-step roadmap for building this function (processes, roles, metrics).
Business outcomes of CP implementation based on examples of companies sized 50-500.
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In this deck, you'll discover:
- What AI leadership means for product managers
- The fundamental paradigm shift required for AI product development.
- A framework for identifying high-value AI opportunities for your products.
- How to transition from user stories to AI learning loops and hypothesis-driven development.
- The essential AI product management framework for defining, developing, and deploying intelligence.
- Technical and business metrics that matter in AI product development.
- Strategies for effective collaboration with data science and engineering teams.
- Framework for handling AI's probabilistic nature and setting stakeholder expectations.
- A real-world case study demonstrating these principles in action.
- Practical next steps to begin your AI product leadership journey.
This presentation is essential for Product Managers, aspiring PMs, product leaders, innovators, and anyone interested in understanding how to successfully build and manage AI-powered products from idea to impact. The key takeaway is that leading AI products is about creating capabilities (intelligence) that continuously improve and deliver increasing value over time.
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Everyoneโs talking about AI but is anyone really using it to create real value?
Most companies want to leverage AI. Few know ๐ต๐ผ๐.
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What exactly should you ask to find real AI opportunities?
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Which AI techniques actually fit your business?
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Is your data even ready for AI?
If youโre not sure, youโre not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.
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Mobile App Development Company in Saudi Arabia
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What is Model Context Protocol(MCP) - The new technology for communication bw...
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