EPISODE · Aug 21, 2025 · 50 MIN
Episode 42: The Missing Link Between AI Engineering and Data Readiness
from TeqTalk with Jas · host Jas Kaur
You can also apply to be the guest on TeqTalk: https://www.teqfocus.com/teqtalks/ You can connect with: Jas Kaur, CTO, Teqfocus: https://www.linkedin.com/in/jas-kaur-5396b237/ Anjan Kumar Ayyadapu, Senior Data Solutions Architect, Cloudera: https://www.linkedin.com/in/anjanreddy8686/ Harshit Kohli, Sr. Technical Account Manager, Amazon Web Services: https://www.linkedin.com/in/harshit-kohli-99801543/ ----------- Data without AI has untapped potential. AI without strong data pipelines is unreliable. In this episode of #TeqTalk, we dive into the real-world gap between data engineering and AI engineering and how leading enterprises are closing it. From data pipelines and feature stores to real-time AI, vector embeddings, and semantic platforms, this conversation unpacks what it takes to make AI not just possible, but scalable, responsible, and business-ready. You’ll Learn: - Why bridging data and AI engineering is the #1 challenge for enterprises - How feature stores, data contracts, and MLOps are powering next-gen AI systems - The infrastructure bottlenecks holding back large-scale AI adoption - Real-world use cases: fraud detection, healthcare outcomes, and retail personalization - Why responsible AI and governance matter as much as innovation Timestamps 00:00 – Intro: Why data & AI engineering need alignment 01:42 – The evolution from DataOps to MLOps to AIOps 03:18 – Feature stores & real-time pipelines explained 05:02 – Why more data ≠ better AI (quality > quantity) 07:15 – Governance, contracts & responsible AI adoption 09:44 – How enterprises prevent AI hallucinations & bias 12:06 – Industry use cases: finance, healthcare, fraud detection 14:30 – Scaling AI systems: building for longevity, not POCs 16:20 – The future of AI + data: cultural & organizational shifts Whether you’re a CIO, CTO, data engineer, or AI practitioner, this episode gives you a front-row seat to the future of enterprise AI. ----------- About podcast: TeqTalk is a leading technology podcast hosted by Jas Kaur, focused on the future of AI, data, and digital innovation especially in the healthcare, pharma, and tech space. Each episode features exclusive conversations with CXOs, CIOs, data architects, and industry experts from across the US and Canada. From AI-powered transformation and natural language data access to real-time insights and data integration, TeqTalk explores how modern businesses can bridge the gap between technology and strategy. Whether you're a tech leader, marketer, or innovation enthusiast, TeqTalk delivers practical knowledge, thought leadership, and cutting-edge trends shaping the future of enterprise solutions. Subscribe to stay ahead in the world of artificial intelligence, cloud data platforms, healthcare tech, and business innovation. #gotomarket #ai #genai #teqtalk #Teqfocus #EastBayCXO #salesforce #snowflake #aws #partner #agenticai #datacloud #CIO #technicaldept #healthcare #homehealth #pharma
What this episode covers
You can also apply to be the guest on TeqTalk: https://www.teqfocus.com/teqtalks/ You can connect with: Jas Kaur, CTO, Teqfocus: https://www.linkedin.com/in/jas-kaur-5396b237/ Anjan Kumar Ayyadapu, Senior Data Solutions Architect, Cloudera: https://www.linkedin.com/in/anjanreddy8686/ Harshit Kohli, Sr. Technical Account Manager, Amazon Web Services: https://www.linkedin.com/in/harshit-kohli-99801543/ ----------- Data without AI has untapped potential. AI without strong data pipelines is unreliable. In this episode of #TeqTalk, we dive into the real-world gap between data engineering and AI engineering and how leading enterprises are closing it. From data pipelines and feature stores to real-time AI, vector embeddings, and semantic platforms, this conversation unpacks what it takes to make AI not just possible, but scalable, responsible, and business-ready. You’ll Learn: - Why bridging data and AI engineering is the #1 challenge for enterprises - How feature stores, data contracts, and MLOps are powering next-gen AI systems - The infrastructure bottlenecks holding back large-scale AI adoption - Real-world use cases: fraud detection, healthcare outcomes, and retail personalization - Why responsible AI and governance matter as much as innovation Timestamps 00:00 – Intro: Why data & AI engineering need alignment 01:42 – The evolution from DataOps to MLOps to AIOps 03:18 – Feature stores & real-time pipelines explained 05:02 – Why more data ≠ better AI (quality > quantity) 07:15 – Governance, contracts & responsible AI adoption 09:44 – How enterprises prevent AI hallucinations & bias 12:06 – Industry use cases: finance, healthcare, fraud detection 14:30 – Scaling AI systems: building for longevity, not POCs 16:20 – The future of AI + data: cultural & organizational shifts Whether you’re a CIO, CTO, data engineer, or AI practitioner, this episode gives you a front-row seat to the future of enterprise AI. ----------- About podcast: TeqTalk is a leading technology podcast hosted by Jas Kaur, focused on the future of AI, data, and digital innovation especially in the healthcare, pharma, and tech space. Each episode features exclusive conversations with CXOs, CIOs, data architects, and industry experts from across the US and Canada. From AI-powered transformation and natural language data access to real-time insights and data integration, TeqTalk explores how modern businesses can bridge the gap between technology and strategy. Whether you're a tech leader, marketer, or innovation enthusiast, TeqTalk delivers practical knowledge, thought leadership, and cutting-edge trends shaping the future of enterprise solutions. Subscribe to stay ahead in the world of artificial intelligence, cloud data platforms, healthcare tech, and business innovation. #gotomarket #ai #genai #teqtalk #Teqfocus #EastBayCXO #salesforce #snowflake #aws #partner #agenticai #datacloud #CIO #technicaldept #healthcare #homehealth #pharma
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Episode 42: The Missing Link Between AI Engineering and Data Readiness
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