DataTalks.Club

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Jul 2026

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DataTalks.Club - the place to talk about data!

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August 21, 2026Episode 11 hr 0 min

Engineering AI-Powered Data Products - Radovan Bacovic

In this talk, Radovan Bacovic, Principal Data Engineer and Snowflake/dbt Ambassador, shares his two-decade journey, from traditional database administration to leading modern data platform engineering. We explore the essential building blocks of scalable data architectures, the trade-offs of no-code solutions, and how to effectively integrate AI into your data pipelines while maintaining strict security and governance.You’ll learn about:- The evolution of the full-stack data engineer over the past two decades.- Building scalable, business-driven data platforms without premature optimization.- Efficient data modeling and transformation using DuckDB and dbt.- Leveraging AI to accelerate pipeline development and launch data products.- Implementing enterprise data governance and robust pipeline security.- Balancing no-code integration tools with strict DataOps methodologies.LINKS:https://gitlab.com/radovan.bacovic/wsc26_dataops/-/blob/main/README.md?ref_type=headsTIMECODES:0:00 Data Engineering Career Trajectory and Industry Experience6:37 Full Stack Data Engineer Role Evolution and Responsibilities14:45 Scalable Data Platform Architecture and Business Alignment21:59 DuckDB Integration and Premature Database Optimization Avoidance30:04 Data Pipeline Transformation and Data Modeling with dbt35:31 Modern Data Platform Ecosystem and Infrastructure Orchestration42:13 AI Powered Data Products and LLM Pipeline Automation49:42 Enterprise Data Governance and Pipeline Security Best Practices55:06 No Code Data Integration Tools and DataOps MethodologyThis talk is ideal for data engineers, analytics engineers, and data platform leads looking to modernize their tech stacks and future-proof their infrastructure. It provides highly actionable insights for anyone aiming to build resilient, AI-ready data platforms that prioritize real business value and strict security constraints.Connect with Radovan- Twitter - https://twitter.com/Al_Grigor - Linkedin - https://www.linkedin.com/in/agrigorev/ Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

July 24, 2026Episode 91 hr 1 min

Engineering Your Own AI Assistant - Paul Iusztin

In this talk, Paul Iusztin, Creator of Decoding AI and author of the LLM Engineer's Handbook, shares his deep expertise in personal automation from managing a digital life with lightweight data pipelines to architecting autonomous agents for deep research. We explore the mechanics of building personal AI assistants and the critical role of using a "second brain" as a context layer over heavy, over-engineered RAG infrastructure.You’ll learn about:- Organizing your digital life using the PARA method and lightweight data pipelines.- Capturing and retrieving resources effortlessly with Obsidian, Readwise, and custom deep research algorithms.- Leveraging your "second brain" setup as the ultimate context layer for personal AI assistants.- Generating ad-hoc wikis from markdown brain dumps to streamline content creation and research.- Optimizing AI-generated content by deliberately lowering LLM reasoning capabilities for better styling.- Adapting multi-agent workflows and personal wikis to accelerate software engineering and coding tasks.TIMECODES:00:00 Digital life organization using the PARA method and lightweight data pipelines05:21 Seamless resource capture with Obsidian and Readwise09:26 Resource retrieval optimization using a deep research algorithm12:44 High-quality internet curation versus heavy RAG pipelines16:31 Second brain setup as a context layer for personal AI assistants21:40 AI workflow simplification with Anthropic APIs and CLI tools25:26 Ad-hoc wiki generation from markdown brain dumps for content creation29:18 Codebase ingestion and web scraping proxy tool workarounds34:43 Resource reranking and context window management for large texts39:06 Content styling optimization by lowering LLM reasoning capabilities46:18 Multi-agent workflows and personal wikis for software engineering tasks52:32 Personal wiki scaling for enterprise knowledge bases and book writingThis talk is perfect for individual developers, AI engineers, and knowledge workers looking to escape "PoC purgatory" and build practical, low-maintenance personal AI assistants. It offers highly actionable insights for anyone wanting to integrate agentic workflows into their daily productivity systems without over-engineering their tech stack.Connect with Paul- Linkedin - https://www.linkedin.com/in/pauliusztin/- Website - https://www.pauliusztin.ai/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

July 17, 2026Episode 81 hr 0 min

Thriving in the AI Era with Human Skills - Maryam Ramezani-Bartsch

In this talk, Maryam Ramezani-Bartsch, Data and AI Leader with over 20 years of experience at companies like adidas and Zalando, shares her extensive career journey from building foundational ML systems at adidas to coaching data experts through the modern AI landscape. We explore the critical intersection of technical strategy and the essential human skills needed to thrive in the AI era.LINKS:- https://maryamramezani.com/designyourdatacareerYou will learn about:- The surprising similarities and critical differences between the current Generative AI boom and the previous Big Data era.- Why the traditional boundaries between data roles are disappearing and the specific T shaped profile companies are actually hiring for today.- The hidden danger of perfectionism in corporate tech, and what a healthy margin of failure actually looks like in practice.- How to stop leaving your career trajectory to chance by applying product Design Thinking to your own life.- A practical framework for navigating industry uncertainty and tech career anxiety without burning out.- Battle tested strategies for regaining your footing and standing out in a highly competitive job market after a layoff.- The specific non technical human skills that will become your ultimate career moat against automation.TIMECODES:00:00 Human Skills in the AI Era07:07 Building ML Systems at Adidas12:49 Generative AI vs Big Data Era18:39 T-Shaped Data Engineering Roles24:04 Overcoming Perfectionism in Tech30:34 Design Thinking for Data Careers38:59 Managing Tech Career Anxiety45:07 Aligning Passion with Tech Skills50:18 Job Search Strategies After Layoffs56:03 Essential Soft Skills for JuniorsThis talk is essential for data professionals, software engineers, and tech leaders looking to future proof their careers in an increasingly automated world. Whether you are a junior developer navigating a tough job market, an engineer bouncing back from layoffs, or a senior professional looking to strategically design your next pivot, this session provides the tools to build a highly resilient career.Connect with Maryam- Linkedin - https://www.linkedin.com/in/maryam-ramezani-bartsch/- Website - https://maryamramezani.com/- Substack - https://maryamramezani.substack.com/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

July 10, 2026Episode 71 hr 2 min

Building a Career in AI From Real Estate to AI Engineering - Gustaf Gyllensporre

In this talk, Gustaf Gyllensporre, Senior AI Engineer and PropTech Founder, shares his unconventional career journey from selling Miami real estate to shipping production AI systems. We explore the tactical steps for breaking into the tech space as a self taught developer and how to successfully bypass traditional industry gatekeepers.Links:- @PropTechFounder - https://youtu.be/leXRiJ5TuQo?si=ymK03qKVEC7hAt9N- https://x.com/gostak_ddYou will learn about:- The strategic approach to crafting an AI engineering resume that actually gets noticed by hiring managers.- Why building another generic RAG chatbot might be hurting your portfolio and the specific high impact projects you should build instead.- The massive difference between interviewing for AI roles at dynamic startups versus traditional big tech companies.- How to leverage open source contributions to prove your technical mastery without a computer science degree.- The surprisingly simple networking tactics and developer ambassador programs that can unlock exclusive job opportunities.- Actionable ways to improve the critical social and communication skills that most developers completely ignore.TIMECODES:00:00 AI Engineering Field Guide05:01 Self Taught AI Engineer Pivot09:39 CPython Open Source Contributions13:49 Tech YouTube Channel Growth18:26 AI Engineer Resume Optimization22:44 AI Engineering Portfolio Projects29:23 Startup vs Big Tech Interviews33:47 Open Source AI Project Ideas38:55 Building Deep Research AI Agents42:52 Landing Your First AI Job46:48 Tech Networking Strategies51:16 Technical Project Demo Videos54:53 Self Taught Developer Mistakes58:53 Soft Skills for Software EngineersConnect with Gustaf- Linkedin - https://www.linkedin.com/in/gustaf-g/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

July 3, 2026Episode 757 min

How to Build AI that actually Ships in Production - Aleksandr Kim

In this talk, Aleksandr Kim, Senior Data Scientist at Intuit, shares his expertise in building AI-powered features in production from fine-tuning BERT models in cyber security to engineering scalable data verification platforms. We explore the reality of moving beyond messy research code to build observable, cost-effective AI agents and automated pipelines.You’ll learn about:- Translating traditional machine learning metrics into actionable business outcomes- Validating large language model behavior through robust evaluation and alignment techniques- Pivoting from a generic chatbot project to high-value Slack automation workflows- Structuring outputs and guided reasoning layers to eliminate trivial AI summaries- Defining the overlapping skills between AI engineers, data scientists, and full-stack software engineers- Implementing multi-LLM routing logic and token caching to minimize enterprise API expenses- Identifying critical data infrastructure bottlenecks to determine when to pivot or drop an AI pilotTIMECODES:00:00 AI Engineering Production and Scalability06:12 Intuit Ecosystem and QuickBooks Products12:17 Aligning ML Metrics with Business Outcomes18:52 AI Engineers Conducting Customer Interviews25:13 Structured Output and Guided Reasoning31:13 Defining AI Engineering vs Software Engineering37:20 Cost Optimization and Multi LLM Routing43:26 UI Trends and Token Management in Industry49:33 Future Career Trends in AI Engineering55:46 Data Infrastructure Bottlenecks and ML FailuresThis session is designed for mid-to-senior level Data Scientists, Machine Learning Engineers, and Software Engineers who want to develop a highly practical, production-first approach to generative AI. It is especially useful for technology leads focused on reducing token overhead and building self-correcting agentic systems.Connect with Aleksandr- Website - https://alexkimds.github.io/- Linkedin - https://www.linkedin.com/in/aleksandrkim/

June 26, 2026Episode 61 hr 2 min

AI Adoption in Enterprise Beyond Writing Code - Ivan Bilan

In this talk, Ivan, Senior Engineering Manager at Personio, shares his deep expertise in the data and software space from his early days building traditional NLP systems and massive ETL pipelines to his current leadership role in Identity and Access Management (IAM). We explore the rapid evolution of Generative AI, the reality of managing AI agents in production, and the emerging field of context engineering to optimize developer workflows.You’ll learn about:- The buy vs. build dilemma for AI infrastructure and local LLMs.- How AI agents are shifting workloads and evolving code reviews.- Why AI is currently better at fixing tech debt than building from scratch.- Measuring the ROI of AI integration using DORA metrics and cycle times.- Strategies to manage vendor lock-in and minimize AI provider dependency.- Using "context engineering" and specification-driven development to maximize LLM quality.- Why hiring junior engineers is still essential and how AI accelerates their onboarding.TIMECODES:00:00 Career Journey in Data Science and NLP07:37 Industry Adoption of Generative AI and Agents11:45 Buy vs Build Dilemma for AI Infrastructure15:46 AI Capability Limits in Fixing Tech Debt19:32 Developer Workloads and AI Code Contributions24:49 Experimentation with Open Source AI Agent Architectures30:06 Measuring ROI and Business Value of AI Integration35:10 Tracking AI Impact Using DORA Metrics39:51 Impact of AI Code Generation on CI/CD System Reliability43:00 Best Practices for Team AI Tool Adoption48:20 Managing Vendor Lock-In Risks with AI Providers51:27 Importance of Hiring Junior Software Engineers56:28 Accelerated Junior Developer Onboarding with AI Assistants01:00:12 Specification-Driven Development and Context EngineeringThis talk is perfect for software engineers, engineering managers, and technical leaders looking to practically integrate AI tools into their teams without sacrificing code quality or system reliability. It is especially valuable for tech professionals navigating the complexities of AI adoption, CI/CD pipeline management, and organizational scaling in the GenAI era.Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/ Connect with Ivan:- LinkedIn - https://www.linkedin.com/in/ivan-bilan/ - Twitter - https://x.com/demiourgosua - Github - https://github.com/ivan-bilan - Website - https://github.com/ivan-bilan

June 19, 2026Episode 554 min

Applied AI 2026 Berlin Conference Interview

The conference highlighted a critical shift in the technology and engineering ecosystem, moving away from passive implementations toward autonomous AI systems, collaborative communities, and robust engineering guardrails. Discussions centered on the practical architecture required to scale AI safely, the evolution of modern developer tools, and the importance of cross-border technical collaboration. Ultimately, the insights underscored that the future of technology relies on blending rigorous infrastructure with human-centric ecosystem growth. Florian Hönicke an expert in engineering infrastructure, explored the operational shifting of cloud services and the challenges of secure temporary access provisioning. He detailed strategies for managing transient credentials for large groups and autonomous agents using automated serverless functions without exposing long-lived access keys. His central thesis argues that true engineering rigor requires deterministic, self-expiring security layers at the container level. Stella Buhalis, a technical community and developer relations leader, addressed the human dynamics fueling open-source ecosystems and community-driven adoption. She emphasized that long-term project viability stems from structured developer onboarding and lower cognitive barriers rather than pure marketing outreach. Her key insight is that building trusted technical communities acts as the ultimate feedback loop for improving developer experience and software reliability. Błażej Nowakowski, a backend systems architect, focused on database migration paradigms and the optimization of high-dimensional vector search at the network edge. He analyzed real-world infrastructure friction points, specifically isolating SQLite database lock conflicts and remote data sync latencies on serverless architectures. He noted that decoupling persistent remote backends from the core runtime is crucial for maintaining low-latency, multi-cloud application performance. Alena Astrakhantseva , a talent strategy and engineering education specialist, outlined the rapid evolution of technical training as the industry shifts from traditional development to autonomous AI flows. She analyzed how continuous testing, real-time monitoring, and structured evaluation frameworks must become core competencies for new developers. Her notable perspective highlights that the next wave of technical talent must be hired for systemic engineering rigor over simple syntax mastery. Zhen Ming Ng (Babypro) , an open-source library maintainer and developer, demonstrated automation workflows for package deployment and baseline library compliance. He focused on minimizing framework overhead by substituting heavy, resource-intensive dependencies with lightweight tokenizers and compact client drivers. His core perspective is that library design must prioritize minimalism to remain functional across edge-native runtime environments. Connect with speakers: Florian Hönicke Cloud Infrastructure & DevOps Engineer Specialist https://www.linkedin.com/in/florian-h%C3%B6nicke-b902b6aa Stella Buhalis Developer Relations & Technical Community Lead https://www.linkedin.com/in/stella-buhalis Błażej Nowakowski Backend Systems Architect & Database Engineer https://www.linkedin.com/in/b%C5%82a%C5%BCej-nowakowski-096716168/ Alena Astrakhantseva Technical Talent Strategist & Engineering Educator https://www.linkedin.com/in/alenaastra/ Zhen Ming Ng (Babypro) Open Source Software Maintainer & Core Developer https://www.linkedin.com/in/ming91/

June 5, 2026Episode 41 hr 3 min

From GenAI Pilots to Production - Nikita Kozodoi

In this talk, Nikita, Senior Applied Data Scientist at the AWS Generative AI Innovation Center, shares his expertise in bringing enterprise artificial intelligence out of the sandbox—from his early days optimizing traditional machine learning models like gradient boosting to deploying advanced production-grade GenAI pipelines. We explore what it really takes to move generative AI systems from pilot prototypes to production environments.Links:- AWS Generative AI Innovation Center: https://aws.amazon.com/ai/generative-ai/innovation-center/You’ll learn about:- Deploying multi-layered defenses independent of backend LLMs.- Evaluating parameter-efficient methods like LoRA and QLoRA for small models.- Balancing long-term domain expertise with real-time documentation retrieval.- Utilizing multi-agent orchestration for search and anomaly explanation.- Setting up robust LLM-as-a-judge frameworks verified by human metrics.- Leveraging Amazon Bedrock components for memory and runtime scalability.TIMECODES:05:52 Shifting from traditional ML to generative AI07:49 Hybrid pipelines blending classical ML and LLMs11:25 Production guardrails and multi-layered system defense16:15 Prompt bypasses, input attacks, and AI red teaming20:49 Newsletter localization and translation with Zalando27:24 Evaluation frameworks and human-in-the-loop metrics33:07 Aligning LLM-as-a-judge with few-shot prompts34:49 Fine-tuning small language models versus prompting41:18 Complementary mechanics of RAG and fine-tuning43:00 Agentic web search tools for anomaly explanation47:01 Automated text generation from real-time sports sensors49:58 AWS project scoping and proof of concept timelines54:58 Interview requirements and career skills for AWS roles57:59 Enterprise architecture patterns and system observability01:00:42 Reusable infrastructure blocks on Amazon BedrockThis session is designed for machine learning engineers, data scientists, and technical product managers looking to architect reliable, production-ready GenAI workflows. It is highly valuable for teams aiming to bridge the gap between experimental AI prototypes and secure enterprise software.Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/ Connect with Nikita- Linkedin - https://www.linkedin.com/in/kozodoi/- Github - https://github.com/kozodoi- Website and blog - https://www.kozodoi.me/

May 29, 2026Episode 31 hr 7 min

From Notebook to Production: Building End-to-End AI Systems - Mariano Semelman

In this talk, Mariano, Lead Data Scientist and ML Engineer at OLX, shares his journey building high-impact AI media solutions. We explore the transition from traditional e-commerce models to Generative AI and Agentic tools, focusing on how to take AI products from a notebook to full-scale production.You’ll learn about: How to master the full product cycle from requirement gathering to deployment. Using video-to-ad technology to automate car listings and seller experiences. Essential modern tools like FastAPI, Arize, and why UV is a game-changer. When to use LLMs versus specialized vision models like CLIP and YOLO. Why production pipelines are moving from Jupyter notebooks to CLI tools. How agentic coding and AI assistants are 10x-ing development speed. TIMECODES: 0:00 Community Introduction and Slack Engagement 4:16 Career Journey: From Argentina to Barcelona 7:16 Product-Driven AI vs. Traditional Reporting 9:41 AI Media Solutions for E-Commerce Sellers 10:55 Video-to-Ad: The Future of Marketplaces 13:45 Automated Content Creation for Sellers 17:10 Defining End-to-End Ownership in Data Science 21:12 The Longevity of the CRISP-DM Framework 25:33 Impact of Agentic Coding and GitHub Copilot 31:42 Why LLMs Aren't Always the Best Solution 37:39 Translating Business Needs to ML Requirements 41:18 Managing Explicit and Implicit Feedback Loops 48:26 Architecture Deep Dive: Image Description Logic 55:28 The Declining Role of Notebooks in Production 1:02:53 The Modern Tech Stack: Fast API, UV, and Arize Connect with Mariano: Linkedin - https://www.linkedin.com/in/msemelman/ Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

May 22, 2026Episode 11 hr 6 min

Data Makers Fest 2026 Conference Interviews

At Data Makers Fest, a recurring theme was the tension between GenAI hype and production reality. Speakers stressed that classical ML, MLOps, evaluation, data quality, and governance remain essential—especially in regulated sectors like fintech and healthcare. Another strong theme was inclusivity: building AI that serves smaller languages, diverse communities, and practitioners beyond the English-centric ecosystem. Ryan Chaves. Head of ML at a Dutch fintech, Ryan focused on the gap between AI demos and production systems. He argued that classical ML remains critical for fraud detection and risk scoring, while GenAI works best as an accelerator on top of existing systems. He also emphasized storytelling, stakeholder communication, and mentorship as core engineering skills. Alp Öktem. Computational linguist and researcher Alp explored the imbalance between AI progress in English and low-resource languages. Through Mozilla Data Collective, he highlighted how open datasets, speech corpora, and synthetic data can expand AI access to underrepresented communities. His broader warning: fluent AI can still fail culturally, linguistically, and ethically. Agnieszka Kamińska. Working in pharmaceutical ML engineering, Agnieszka discussed extracting scientific knowledge from research documents into knowledge graphs. Her focus was reliability: LLMs help with entity extraction and relationship discovery, but trustworthy systems still require ontologies, validation layers, and production-minded engineering. She advocated a pragmatic middle ground between AI hype and skepticism. Nemanja Radojković. An MLOps engineer in finance, Nemanja reflected on how GenAI is changing software engineering itself. He argued that coding assistants improve productivity but risk weakening engineers’ understanding if overused. His central point: governance, reproducibility, and platform engineering will become even more important as organizations deploy AI agents at scale. Filipa Castro. Leading AI initiatives at Euronext, Filipa described how GenAI is integrated into regulated financial workflows. Her team uses LLMs to automate document-heavy operational processes while preserving human validation. Her broader message: successful enterprise AI depends less on flashy models and more on infrastructure foundations like CI/CD, monitoring, governance, and operational rigor. Beatriz Silva. As a student volunteer pursuing a master’s in data science, Beatriz represented the conference’s educational and community dimension. For her, the event was about access—networking with companies, exploring thesis opportunities, and connecting academic learning with industry practice. Her perspective highlighted how conferences like Data Makers Fest help shape the next generation of AI practitioners. Connect with speakers: Ryan Chaves. Head of Machine Learning at a Dutch fintech focused on fraud detection, risk systems, and production ML. LinkedIn Alp Öktem. Computational linguist and researcher focused on low-resource languages, inclusive AI, and open language datasets. LinkedIn Agnieszka Kamińska. Machine Learning Engineer working on scientific knowledge extraction, knowledge graphs, and AI systems in pharma. LinkedIn Nemanja Radojković. Senior MLOps Engineer specializing in regulated financial systems, AI governance, and platform engineering. LinkedIn Filipa Castro. AI Lead at Euronext focused on enterprise GenAI systems, operational AI strategy, and financial services automation. LinkedIn Beatriz Silva. Data science master’s student and conference volunteer exploring opportunities in ML and computer vision. LinkedIn