The AI and ML Conversations with Iavor Botev

Iavor Botev
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Conversations with AI and ML experts on building real-world systems, navigating career growth, and exploring the future of generative AI. From messy data pipelines to scaling infrastructure, from proof of concept to production, we dive into the technical lessons, human stories, and big-picture impacts shaping the future of AI.

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  1. 3 NGÀY TRƯỚC

    Diogo Diogo: Pragmatic Data Science, Marketing Measurement, Privacy | AI and ML Conversations #3

    In episode 3 of "AI and ML Conversations," I sit down with Diogo, a senior data scientist at Usercentrics and a PhD researcher in data science, to unpack pragmatic data science, marketing measurement, and using LLMs with strong privacy guardrails.​Diogo traces his path from management and marketing into industry roles across Europe, balancing a remote career in Norway with research on measuring cultural value - drawing sharp parallels to brand equity, data scarcity, and business value.​We cover what it takes to be effective with quick proofs of concept, financial value proxies, and privacy-first use of LLMs for customer data enrichment.The conversation also dives into remote vs office culture across countries, startup realities where roles blur across data and engineering, and lightweight rituals like bi‑weekly project reviews that keep stakeholders aligned and accountable.​Timestamps​00:00 - Introduction​00:40 - Guest intro: Diogo, background, Usercentrics​01:13 - Why a PhD and timing trade‑offs​05:02 - Cultural economics: measuring cultural value vs brand equity​07:41 - Data scarcity and useful variables: ticketing API, weather/holidays, telco footfall, surveys​09:19 - Economic impact: spillovers to housing and tourism; online reviews sentiment​11:59 - Moving from Portugal to Norway; EOR setup and distributed teams​13:15 - Remote vs office: flexibility, productivity, and policy pitfalls​16:55 - Portugal’s remote reality, expats, and housing pressure​19:04 - Ship value fast: POCs, value rules, pragmatic LTV signals​23:49 - Communicating with non‑technical stakeholders and focusing on business metrics​27:18 - Startup roles: DS, DE, MLE, AI eng; wearing multiple hats​30:34 - Meetings and ceremonies: beyond daily standups to bi‑weekly project cadences​34:57 - Toolbox: VS Code, schemas, and data discoverability pains​36:59 - The measurement trifecta: attribution, geo‑incrementality, and Marketing Mix Modelling (MMM​)39:35 - Adding external signals (e.g., Apple keynotes) to MMM​40:29 - LLMs for customer data enrichment and segmentation​42:26 - Hosting models on Vertex AI/Azure and privacy considerations​43:09 - Career advice: build close stakeholder relationships and iterate visibly​44:56 - Closing​

    45 phút

  2. 23 THG 9

    Sebastián Poliak: Independent App Development, ML Engineering, RecSys | AI and ML Conversations #2

    🎙️ In episode 2 of "AI and ML Conversations," I sit down with Sebastián Poliak, an experienced machine learning engineer who's transitioned into an independent app developer. 🚀 Sebastián opens up about his path from applied research to building AI-powered mobile apps like Stridly and Babli (https://publicspeakingcoach.app/).With notable roles including Senior ML Engineer at Bloomreach and Machine Learning Researcher at Seznam, he brings a wealth of expertise. We explore the changing world of machine learning engineers, the influence of generative AI, and why focusing on high-quality products with great user experiences matters. Join us for practical tips on career shifts, integrating AI into app development, and making the most of data-driven strategies - the links are in the comments. Timestamps: 00:00 – Introduction 01:22 – Sebastián’s background & early interest in AI 03:42 – Career as a machine learning engineer 08:02 – Key projects: search, NLP & recommender systems 11:33 – The impact of transformers & GenAI 14:19 – From ML engineer to indie app developer 24:03 – Building and launching first apps 26:50 – Marketing strategies 30:13 – Data-driven product design & user experience 37:55 – Using AI in development & backend setup 40:19 – Costs, fine-tuning, and evaluation challenges 48:22 – Focus on onboarding, growth & staying solo 52:32 – Influencer marketing & growth hacks 55:15 – Overview of Sebastián’s apps 57:54 – Life advice: freedom, happiness, and building quality products 01:00:23 – Closing

    1 giờ

  3. 6 THG 9

    Dimitar Nentchev: Soccer, Economics, Data, and Product | AI and ML Conversations #1

    In this conversation, Dimitar Nentchev shares his journey from aspiring professional soccer player to a successful career in data science and analytics. He discusses the challenges he faced during his transition, the importance of aligning data projects with business needs, and the impact of AI and LLMs on productivity. Dimitar emphasizes the value of iterative development and the necessity of quality data in machine learning projects, while also reflecting on his experiences in both startups and large organizations. The conversation also delves into personal experiences with mental health, work-life balance, and the significance of taking risks in one's career.

    1 giờ 16 phút
  • 3 tập

Giới Thiệu

Conversations with AI and ML experts on building real-world systems, navigating career growth, and exploring the future of generative AI. From messy data pipelines to scaling infrastructure, from proof of concept to production, we dive into the technical lessons, human stories, and big-picture impacts shaping the future of AI.

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