All with intro texts

Author: kavaivaleri

_podcast/no-timestamps/s01e03-building-ds-team.md

@@ -14,15 +14,22 @@ links:
   anchor: https://anchor.fm/datatalksclub/episodes/Building-a-Data-Science-Team---Dat-Tran-enlmef
   spotify: https://open.spotify.com/episode/0daFpY1z2J4Uop1XdMNsnY
   apple: https://podcasts.apple.com/us/podcast/building-a-data-science-team-dat-tran/id1541710331?i=1000502061864
-intro: In this episode, Dat Tran, Partner and CTO at DATANOMIQ, shares his journey
-  from economics and gaming to leading AI and data science teams at companies like
-  idealo and Axel Springer. He discusses how to scale AI from prototype to production,
-  build strong product cultures, and balance generalists vs. specialists when hiring.
-  Drawing on his experience founding Priceloop, Dat dives into MLOps in production,
-  open-source collaboration, explainable AI, and how to retain top talent in competitive
-  markets. Packed with lessons on leadership, data strategy, and sustainable AI systems,
-  this episode is a must-listen for data professionals aiming to build real impact
-  with machine learning.
+intro: How do you build an MLOps‑ready data team while shipping a transparent, white‑box
+  dynamic pricing product for a startup? In this episode Dat Tran—Partner & CTO at
+  DATANOMIQ, former Head of Data at idealo, and co‑founder of Priceloop—walks through
+  the practical tradeoffs of moving from prototypes to production ML. <br><br> Dat
+  traces his path from economics and early coding to production ML at Accenture, Axel
+  Springer and idealo, and explains the “day‑two” operations mindset required for
+  model maintenance and MLOps. We cover building a Head of Data role, hiring strategies
+  for early‑stage startups (T‑shaped generalists first, specialists later), and how
+  to align hiring with product uncertainty. Dat also outlines Priceloop’s white‑box
+  AI approach to dynamic pricing—human‑centric systems that augment pricing managers
+  rather than replace them—and the role of open research and open‑source in competitive
+  advantage. <br><br> Tune in for concrete guidance on team composition (ML engineers,
+  data engineers, PMs), take‑home assessments, project prioritization, retention,
+  and educating leadership on realistic AI capabilities. Listeners will leave with
+  actionable steps to create production‑grade MLOps teams and build transparent dynamic
+  pricing solutions.
 transcript:
 - header: Intro
 - line: Today we have pleasure to have Dat as a guest. Dat needs no introduction.

_podcast/no-timestamps/s01e04-standing-out-as-a-data-scientist.md

@@ -1067,4 +1067,18 @@ transcript:
   who: Alexey
 description: Master data scientist resume, portfolio & interview tactics to get interviews,
   prove business impact and negotiate higher salary with recruiter tips.
+intro: 'How do you get hired — or hire — for a data scientist role when expectations,
+  titles, and hiring processes differ so widely? In this episode Luke Whipps, co‑founder
+  of Neural.AI and host of the AI Game Changer podcast, draws on 8+ years recruiting
+  data, analytics and AI talent to answer that question. We walk through a six‑stage
+  recruitment workflow from role definition to offer, and tackle practical hiring
+  and job‑seeking topics: writing a data scientist resume and CV (format, length,
+  audience fit), building a portfolio that links tech stack to concrete projects,
+  and shaping a career narrative that demonstrates real business impact. Luke breaks
+  down shortlist and interview preparation, candidate funnel strategies, junior hiring
+  tips, targeted outreach (email, LinkedIn) and focus strategies for approaching fewer
+  companies. He also covers salary signals and negotiation, transitioning from academia
+  or web development, job‑hopping concerns, and how to align job titles without misrepresenting
+  experience. Listen to gain actionable interview preparation, portfolio and salary
+  negotiation strategies for data science hiring and career progression.'
 ---

_podcast/no-timestamps/s01e05-mentoring.md

@@ -19,6 +19,22 @@ links:
   anchor: https://anchor.fm/datatalksclub/episodes/Mentoring---Rahul-Jain-eo7cmu
   spotify: TODO
   apple: TODO
+intro: How do you find a mentor, turn mentoring into paid work, and grow as a technical
+  leader? In this episode Rahul Jain—Senior Solutions Engineer at Snowflake with 15+
+  years in data and AI—walks through practical steps for mentorship and leadership
+  development grounded in his career from mining engineering to data engineering and
+  management. We define mentoring (purpose, types, sponsorship), explore ways to find
+  a mentor via networks, cold outreach, and platforms, and share cold outreach best
+  practices like specificity, background, and follow‑up. Rahul outlines how to prepare
+  effective mentoring sessions (goals, agendas), compares one‑off advice to long‑term
+  relationships, and covers benefits of being a mentor including listening and pattern
+  recognition. Listeners will also learn people‑skills essentials (empathy, avoiding
+  the “advice monster”), balancing technical work with leadership, addressing common
+  mentee challenges like imposter syndrome, and when to use external coaches. Practical
+  guidance on setting boundaries, starting paid mentorship, pricing and accountability,
+  building reciprocal relationships, and maintaining development plans rounds out
+  the episode—ideal for engineers and aspiring technical leaders seeking actionable
+  mentoring and career growth strategies.
 ---
 
 Today we're discussing mentoring with [Rahul Jain](/people/rahuljain.html), a technical leader with about 20 years of experience building and running software products. He currently leads the Business Intelligence and Data Engineering units at Omio, a ticket-booking company, and mentors engineers and managers through The Mentoring Club.

_podcast/no-timestamps/s02e01-writing.md

@@ -21,6 +21,23 @@ links:
   anchor: https://anchor.fm/datatalksclub/episodes/The-Importance-of-Writing-in-a-Tech-Career---Eugene-Yan-ep17du
   spotify: TODO
   apple: TODO
+intro: How do you publish developer-focused posts weekly without sacrificing depth
+  or your day job? In this episode Eugene Yan — an Applied Scientist at Amazon who
+  builds pragmatic ML systems and previously led data science teams at Lazada and
+  uCare.ai — walks through a practical, outline-first approach to sustainable developer
+  blogging and building a technical portfolio. <br><br> We cover Eugene’s career pivot
+  into public writing, motivations for sharing knowledge, and how to target readers,
+  peers, and future teammates. Listen for his 7-day weekly writing cadence, time-budgeting
+  advice (including tips to avoid over-editing), and the outline-first method for
+  filtering ideas and rewriting from memory. He also breaks down idea sourcing, title
+  and length decisions, getting started tactics, and recommended blogging tools (Medium,
+  Substack, WordPress, Jekyll/GitHub Pages). You’ll hear routines for morning reps
+  and weekend deep work, distribution strategies via Twitter and LinkedIn, and how
+  to translate work artifacts into press-release-style docs, decision logs, and clearer
+  technical documentation. Plus, actionable portfolio best practices—clear README,
+  quick-start guide, and repo tours—to make your code and writing discoverable. <br><br>
+  Tune in to learn a repeatable workflow for weekly developer blogging, technical
+  writing, and portfolio building that scales with your career.
 ---
 
 Today we're discussing technical writing, logging, documentation, and more. Our special guest is [Eugene Yan](/people/eugeneyan). Eugene works at the intersection of machine learning and product, building pragmatic ML systems while writing and speaking about effective data science, ML in production, and career growth.

_podcast/no-timestamps/s02e02-developer-advocacy.md

@@ -951,4 +951,20 @@ transcript:
   who: Alexey
 description: 'Discover DevRel tactics for Data Science: community growth, reproducibility,
   and content strategy—practical metrics, safety practices, and career growth tips.'
+intro: How do you practice developer relations for data science while balancing reproducibility,
+  community growth, and content strategy? In this episode Elle O’Brien — a data scientist
+  at Iterative (working on DVC and CML) and a lecturer at the University of Michigan
+  with a PhD in neuroscience and computational modeling from UW — walks through practical
+  DevRel for data-focused tools and teaching. <br><br> We cover her shift from a viral
+  StyleGAN project into DevRel, the scope of a solo developer advocate (product work,
+  docs, PRs, videos, hiring), and how she prioritizes releases versus evergreen content.
+  Elle shares promotion tactics (Hacker News, Reddit, social), approaches to community
+  safety and moderation, and the emotional realities of online work. She explains
+  community metrics, role distinctions between DevRel/advocate/evangelist, and core
+  skills like technical credibility and rapid learning. We also dig into content strategy
+  for teaching—curriculum design, reusable video content, recording lectures as open
+  educational resources, and practical ways to get started blogging and building a
+  developer portfolio. <br><br> Listen to gain actionable guidance on community growth,
+  reproducibility best practices, content planning, and the trade-offs of DevRel work
+  in open source data science.
 ---

_podcast/no-timestamps/s02e03-open-source.md

@@ -26,6 +26,23 @@ links:
   anchor: https://anchor.fm/datatalksclub/episodes/Getting-Started-with-Open-Source---Vincent-Warmerdam-epk60j
   spotify: https://open.spotify.com/episode/1dsbDeVncfsEg3m3cYB927
   apple: https://podcasts.apple.com/us/podcast/getting-started-with-open-source-vincent-warmerdam/id1541710331?i=1000507024598
+intro: 'How do you start contributing to open source ML projects like scikit-learn
+  pipelines—or move from curious user to confident contributor on Rasa’s conversational
+  AI stack? In this episode Vincent Warmerdam, Research Advocate at Rasa and creator
+  of The Algorithm Whiteboard and calmcode.io, walks through practical, hands-on advice
+  for contributing to open source ML. <br><br> Vincent shares his career pivot from
+  design student to data scientist and highlights projects (evol, clumper, memo, whatlies,
+  scikit-lego) that illustrate small-tools-to-impact workflows. We deep-dive into
+  scikit-learn–compatible pipeline components, design principles for low-maintenance
+  APIs, and common mistakes such as publishing to PyPI too early. You’ll get a documentation
+  checklist (README, guides, API reference, examples), guidance on filing reproducible
+  issues, and step-by-step preparation for pull requests: testing, CI, packaging,
+  and pre-commit hooks. <br><br> Listeners will leave with concrete strategies for
+  finding the right project, balancing large vs. small repositories, community stewardship
+  and contribution etiquette, and ways OSS work can boost career visibility through
+  talks, blogs, and meetups. If you want actionable next steps for contributing to
+  open source ML, scikit-learn pipelines, PRs, docs, or Rasa conversational AI, this
+  episode maps the path.'
 ---
 
 Today we're talking open source with our guest, **Vincent Warmerdam**. Vincent is a Research Advocate at Rasa. If you check his LinkedIn, you'll see a lot: he's made Reddit's front page, runs calmcode.io for learning to code, has organized PyData Amsterdam and AI Saturdays Amsterdam, and he's a data evangelist and open-source enthusiast who's created and maintains several open-source packages. And—last but not least—he has over 80 LinkedIn endorsements for "awesomeness." Welcome, Vincent!

_podcast/no-timestamps/s02e04-mlops.md

@@ -1100,4 +1100,19 @@ transcript:
   who: 'Theo:'
 description: 'Master MLOps with Kubeflow: monitor data drift, automate retraining
   and scale pipelines using KFServing, Katib & Prometheus for production-ready ML.'
+intro: How do you detect model drift, trigger retraining, and scale ML pipelines in
+  production? In this episode Theofilos Papapanagiotou — a systems engineer with 20
+  years’ experience (mostly in telcos) now building tools to run ML workloads and
+  an active Kubeflow advocate — walks through practical MLOps patterns and tooling
+  to answer that question. <br><br> We define MLOps as culture, process, and technology,
+  contrast DevOps vs MLOps across model lifecycle and data drift, and unpack monitoring
+  for drift, fairness, and retraining triggers. Hear about monitoring stacks (Prometheus/Grafana,
+  inference sensors), commoditizing inference monitoring, and how monitoring can feed
+  new training data. Theofilos explains team composition and the “MLOps engineer”
+  debate, maturity models from manual training to automated, data‑driven retraining,
+  and traceability via MLMD metadata and model versioning. <br><br> We also explore
+  the Kubeflow ecosystem — Pipelines, KFServing, Feast, Katib, and TFX — plus hyperparameter
+  search, cloud‑managed pipelines, edge/mobile considerations, and practical tips
+  for small teams. Listen to learn concrete approaches to detect model drift, automate
+  retraining, and scale pipelines with Kubeflow and related MLOps practices.
 ---

_podcast/no-timestamps/s02e05-feature-stores.md

@@ -27,6 +27,22 @@ links:
   anchor: https://anchor.fm/datatalksclub/episodes/Feature-Stores-Cutting-through-the-Hype---Willem-Pienaar-ept6m8/a-a4hlg3r
   spotify: https://open.spotify.com/episode/05YnfTWbplXwOwicR2doy3
   apple: https://podcasts.apple.com/us/podcast/feature-stores-cutting-through-the-hype-willem-pienaar/id1541710331?i=1000508782957
+intro: How do you reliably build and serve real‑time features for production ML without
+  rework, duplication, or training/serving skew? In this episode Willem Pienaar —
+  engineering lead at Tecton and creator of Feast — walks through what feature stores
+  solve in MLOps and how they enable real‑time feature engineering. We define feature
+  stores, compare feature creation vs retrieval (SQL, Python, APIs, on‑demand transforms),
+  and illustrate a production real‑time fraud detection lookup. Willem separates hype
+  from value, explains organizational challenges like team silos and speed to production,
+  and outlines the platform role across materialization, serving, and validation.
+  <br><br> You’ll get practical coverage of Feast (open‑source) and Tecton (enterprise),
+  architecture components (transform engine, storage, serving, registry, monitoring),
+  and when online tabular use cases require a feature store versus when it’s overkill.
+  The episode also covers integrations (dbt, Kubeflow, Airflow), streaming vs batch
+  (Flink, Spark), validation and monitoring (drift detection, Great Expectations,
+  TFDV), backfilling strategies, ownership and governance, and getting started resources
+  (feast.dev, Docker). Listen to learn when to adopt a feature store and concrete
+  next steps for productionizing features in your MLOps stack.
 ---
 
 In this episode, we dive deeper into feature stores with Willem, creator of Feast (an open-source feature store). Previously, Willem led the Data Science Platform team at Gojek and now works at Tecton, which develops feature store technology.

_podcast/no-timestamps/s02e06-decision-optimization.md

@@ -18,4 +18,21 @@ links:
 description: 'Learn prescriptive analytics & robust optimization for supply chain
   pricing: align ML predictions to decisions, scale models, pick solvers, and boost
   revenue.'
+intro: 'How do you turn machine learning predictions into better real-world decisions—especially
+  under uncertainty in supply chains and pricing? In this episode Dan Becker, Founder
+  & CEO of Decision AI and former Google data scientist and Product Director at DataRobot,
+  walks through prescriptive analytics and decision optimization for practical business
+  impact. With a background that includes top Kaggle performance and contributions
+  to TensorFlow and Keras, Dan explains how to formulate optimization problems, choose
+  objectives and constraints, and integrate ML forecasts into prescriptive and robust
+  optimization models. <br><br> We cover robust vs. stochastic optimization, aligning
+  loss functions with business objectives, and the solvers and tools that make this
+  work—OR-Tools, Gurobi, Pyomo and open-source options. Dan also digs into scalability,
+  approximation techniques, and deployment: pipelines, monitoring, and feedback loops.
+  Use cases include supply chain optimization, resource allocation, and pricing/bidding
+  strategies, plus operational, legal, and ethical constraints. Listeners will get
+  practical guidance on evaluation metrics, common pitfalls like mis-specified objectives
+  and overfitting decisions, and the cross-functional skills needed—data science,
+  operations research, and software engineering—to get started with prescriptive optimization
+  projects.'
 ---

_podcast/no-timestamps/s02e07-abc-data-science.md

@@ -1246,6 +1246,21 @@ transcript:
 description: 'Master the Data Science ABC Framework: Analyst, Builder, Consultant.
   Get SQL, Python, MLOps career tips, project roadmap, transition strategies to land
   roles.'
+intro: 'How do you pick the right data science path—and actually make the transition?
+  In this episode Danny Ma, a recovering data scientist now focused on ML and data
+  engineering, walks through his ABC Framework (Analyst, Builder, Consultant) and
+  pragmatic steps for career moves. Danny, who runs the #DataWithDanny community (4,500+
+  members) and specializes in analytics, supervised ML, data architecture and digital
+  customer experiments, traces his own shift from SQL/SAS/Excel workflows to Python,
+  Kaggle projects and production systems. <br><br> We cover the ABC Framework origins
+  and definitions: Type A (Analyst) — data exploration, visualization and storytelling;
+  Type B (Builder) — ML engineering, MLOps and production mindset; Type C (Consultant/Leader)
+  — stakeholder persuasion and strategy. Danny shares transition tactics: build projects
+  first, learn theory as needed, core tools (Git, Docker, cloud), practicing engineering
+  via mini-projects and mentorship, portfolio and referral strategies, and when advanced
+  degrees matter. Tune in to get concrete guidance on skills to prioritize, how to
+  gain production experience, and a clear roadmap from SQL → visualization → ML →
+  deep learning to advance your data science career.'
 ---
 
 Links: