Replaced "In this episode " for "In this episode, "

Author: kavaivaleri

_podcast/s01e03-building-ds-team.md

@@ -15,7 +15,7 @@ links:
   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: 'How do you build and scale an ML team that delivers product-driven AI without
-  getting bogged down by tech debt or false promises? In this episode Dat Tran — Partner
+  getting bogged down by tech debt or false promises? In this episode, Dat Tran — Partner
   & CTO at DATANOMIQ and former AI lead at Axel Springer, idealo, and Pivotal — walks
   through practical strategies for hiring, MLOps, and shaping data teams for startups.
   <br><br> Dat draws on a decade of production ML experience to unpack the MLOps mindset

_podcast/s01e05-mentoring.md

@@ -20,7 +20,7 @@ links:
   spotify: TODO
   apple: TODO
 intro: 'Struggling to find a mentor — or wondering how to become one — in a fast-moving
-  tech career? In this episode Rahul Jain, a senior solutions engineer and data/AI
+  tech career? In this episode, Rahul Jain, a senior solutions engineer and data/AI
   leader with 15+ years driving enterprise data transformations and a career arc from
   mining engineering to data engineering and leadership, walks through practical mentoring
   strategies for tech professionals. We define mentoring (purpose, scope, types),

_podcast/s02e01-writing.md

@@ -21,7 +21,7 @@ links:
   spotify: TODO
   apple: TODO
 intro: How can technical writing accelerate your data science career in just one week?
-  In this episode Eugene Yan — an Applied Scientist at Amazon who previously led data
+  In this episode, Eugene Yan — an Applied Scientist at Amazon who previously led data
   science teams at Lazada and uCare.ai and writes about ML in production and career
   growth — walks through a practical, repeatable 7-day workflow for technical writing
   tailored to data scientists. <br><br> We cover Eugene’s career transition and first

_podcast/s02e02-developer-advocacy.md

@@ -969,7 +969,7 @@ transcript:
 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
+  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

_podcast/s02e03-open-source.md

@@ -28,7 +28,7 @@ links:
   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
+  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,

_podcast/s02e04-mlops.md

@@ -1121,7 +1121,7 @@ description: Master MLOps with Kubeflow pipelines and automated retraining—det
   drift, accelerate deployment, and boost production model reliability for faster
   iteration.
 intro: How do you build reliable, production-ready ML pipelines that detect model
-  drift, monitor fairness, and trigger automated retraining? In this episode Theofilos
+  drift, monitor fairness, and trigger automated retraining? In this episode, Theofilos
   Papapanagiotou — a systems engineer with 20 years’ experience (from Unix engineering
   to ML engineering) now helping companies run ML workloads and a Kubeflow enthusiast
   — walks through practical MLOps strategies and tooling. <br><br> We define MLOps

_podcast/s02e05-feature-stores.md

@@ -28,7 +28,7 @@ links:
   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 —
+  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),

_podcast/s02e06-decision-optimization.md

@@ -19,7 +19,7 @@ description: 'Learn prescriptive analytics & robust optimization for supply chai
   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
+  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

_podcast/s02e07-abc-data-science.md

@@ -1255,7 +1255,7 @@ description: 'Master the Data Science ABC Framework: Analyst, Builder, Consultan
   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
+  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

_podcast/s02e08-personal-branding.md

@@ -19,7 +19,7 @@ description: 'Build a personal brand: publish on LinkedIn & Medium, grow your au
   and monetize with online courses—publishing best practices, course design, growth
   tips.'
 intro: How do you build a personal brand that actually attracts an audience and turns
-  into revenue? In this episode Admond Lee Kin Lim — data scientist, writer, speaker,
+  into revenue? In this episode, Admond Lee Kin Lim — data scientist, writer, speaker,
   and Data Science Instructor at Hackwagon Academy — breaks down a practical path
   from first posts to monetizing with online courses. Drawing on his experience at
   Micron and as an independent consultant and communicator featured in KDnuggets and