From GenAI Pilots to Production

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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/

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 [https://www.linkedin.com/in/ryan-c-g-chaves/?utm_source=chatgpt.com] Alp Öktem. Computational linguist and researcher focused on low-resource languages, inclusive AI, and open language datasets. LinkedIn [https://www.linkedin.com/in/alp-oktem/?utm_source=chatgpt.com] Agnieszka Kamińska. Machine Learning Engineer working on scientific knowledge extraction, knowledge graphs, and AI systems in pharma. LinkedIn [https://www.linkedin.com/in/agnieszka-kaminska-ds/?utm_source=chatgpt.com] Nemanja Radojković. Senior MLOps Engineer specializing in regulated financial systems, AI governance, and platform engineering. LinkedIn [https://www.linkedin.com/in/radojkovic/?utm_source=chatgpt.com] Filipa Castro. AI Lead at Euronext focused on enterprise GenAI systems, operational AI strategy, and financial services automation. LinkedIn [https://www.linkedin.com/in/filipafcastro/?utm_source=chatgpt.com] Beatriz Silva. Data science master’s student and conference volunteer exploring opportunities in ML and computer vision. LinkedIn [https://www.linkedin.com/in/beatriz-iara-silva/?utm_source=chatgpt.com]

22. mai 20261 h 6 min

From GenAI Pilots to Production - Nikita Kozodoi

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