『DataScience Show Podcast』のカバーアート

DataScience Show Podcast

DataScience Show Podcast

著者： Mirko Peters
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10月19日まで。※適用条件あり
Welcome to The DataScience Show, hosted by Mirko Peters — your daily source for everything data! Every weekday, Mirko delivers fresh insights into the exciting world of data science, artificial intelligence (AI), machine learning (ML), big data, and advanced analytics. Whether you’re new to the field or an experienced data professional, you’ll get expert interviews, real-world case studies, AI breakthroughs, tech trends, and practical career tips to keep you ahead of the curve. Mirko explores how data is reshaping industries like finance, healthcare, marketing, and technology, providing actionable knowledge you can use right away. Stay updated on the latest tools, methods, and career opportunities in the rapidly growing world of data science. If you’re passionate about data-driven innovation, AI-powered solutions, and unlocking the future of technology, The DataScience Show is your essential daily listen. Subscribe now and join Mirko Peters every weekday as he navigates the data revolution! Keywords: Daily Data Science Podcast, Machine Learning, Artificial Intelligence, Big Data, AI Trends, Data Analytics, Data Careers, Business Intelligence, Tech Podcast, Data Insights.

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Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.Mirko Peters 経済学
エピソード
  • From Proof to Product: The Executive Playbook for AI Product Management

    From Proof to Product: The Executive Playbook for AI Product Management
    2026/07/25
    Many enterprises stall at pilots because they treat machine learning as a project, not a product. This episode gives C-level leaders a practical playbook for building AI product management as a repeatable capability: a governance-backed lifecycle that aligns discovery, data and feature ownership, model delivery, product metrics, monetization, and cross-functional funding. Mirko walks listeners through role definitions, roadmaps, success metrics that tie to business KPIs, and organizational patterns that turn prototypes into durable business units. You’ll hear concrete trade-offs—speed vs. robustness, centralization vs. embedded teams—and real decisions leaders must make when balancing risk, cost, and time-to-value. The focus is operational: how to structure investment, define SLAs for data and features, embed product managers with engineering and domain teams, and measure causal impact. Practical, executive-level guidance for turning experimentation into predictable outcomes and measurable ROI.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.

    I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions.
    Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.
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    9 分
  • Causal ROI: An Executive Playbook to Measure Real Business Impact of AI

    Causal ROI: An Executive Playbook to Measure Real Business Impact of AI
    2026/07/24
    Many AI initiatives report technical metrics but fail to prove business impact. This episode gives C-level leaders a practical, non-technical playbook for turning models into accountable investments by embedding causal measurement, controlled experimentation, and incremental rollout strategies into enterprise AI programs. Mirko walks listeners through real-world executive decisions: choosing causal vs. correlational evaluation, designing business-aligned A/B and quasi-experiments, instrumenting metrics and guardrails, and creating governance that insists on measurable outcomes before scale. The episode explains trade-offs between speed and statistical rigor, how to interpret heterogeneous treatment effects for different customer segments, and governance patterns that convert measurement into funding and de-risking mechanisms. Leaders will come away with concrete steps to require causal evidence, avoid common measurement traps, and structure organizations so product, analytics, and engineering jointly own ROI. Practical, tactical, and immediately actionable for executives responsible for AI investments.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.

    I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions.
    Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.
    続きを読む 一部表示
    7 分
  • Model Observability for Executives: Turning Alerts into Business Confidence

    Model Observability for Executives: Turning Alerts into Business Confidence
    2026/07/23
    Executives routinely hear about model drift, data skew, and false positives, but struggle to understand which signals actually matter for the business. This episode walks senior leaders through a pragmatic, product-minded approach to model observability: how to pick the right metrics, translate technical telemetry into business KPIs, design escalation and runbooks, and fund operational controls that reduce risk and unlock value. The monologue explains concrete observability layers (data, prediction, feature, and outcome), examples of meaningful SLOs and alerts, and governance patterns that make monitoring auditable and actionable. Listeners will gain an executive checklist to hold teams accountable, a decision framework for investments in monitoring and tooling, and practical guidance on measuring ROI from reduced incidents, improved model uptime, and faster remediation. The tone is operational, strategic, and directly applicable for C-level leaders responsible for production AI.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.

    I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions.
    Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.
    続きを読む 一部表示
    8 分
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