OKDP (Open Kubernetes Data Platform) est une plateforme data open source conçue pour répondre aux enjeux

de gouvernance, d’industrialisation et de souveraineté des données, en particulier dans des contextes sensibles

ou stratégiques.

Issue d’une collaboration entre la DGFIP et Orange et portée par l’association TOSIT, OKDP propose

une architecture modulaire, interopérable et cloud-native, pensée pour la production et sans vendor lock-in,

afin de garantir une pleine maîtrise des environnements techniques et des données.

Au programme :

• Pourquoi OKDP ? Objectifs et cas d’usage ciblés

• Architecture et composants clés

• Statut open source : gouvernance, contributions, rôle du TOSIT

• Roadmap et perspectives

• Démonstration & session Q&R

Rejoignez-nous pour découvrir comment OKDP permet de déployer des projets data souverains, robustes et mutualisables à grande échelle.

In this session, we’ll explore the real-world journey of implementing a scalable, secure, and resilient data streaming platform—from the ground up. Bridging DevOps and DataOps practices, we’ll cover how our team designed the architecture, selected the right tools (like Kafka and Kubernetes), automated deployments, and enforced data governance across environments. You'll learn how we tackled challenges like schema evolution, CI/CD for data pipelines, monitoring at scale, and team collaboration. Whether you're just starting or scaling your data platform, this talk offers practical takeaways and battle-tested lessons from the trenches of building streaming infrastructure in production.

Vos tableaux de bord vous donnent-ils vraiment le pouvoir d’agir… ou seulement de constater ? Et si l’action publique devenait le laboratoire de l’innovation data/IA que les entreprises attendent ? 

La Région des Pays de la Loire a décidé de rompre avec les approches classiques pour inventer un pilotage augmenté, mêlant souveraineté numérique, IA et appropriation terrain. Découvrez une approche souveraine et radicalement nouvelle d’un pilotage à 360° par la Data et l’IA, à partir d’une seule et unique plateforme Data unifiée Full Web, Full Kubernetes et Full IA.

Summary In this crossover episode of the AI Engineering Podcast, host Tobias Macey interviews Brijesh Tripathi, CEO of Flex AI, about revolutionizing AI engineering by removing DevOps burdens through "workload as a service". Brijesh shares his expertise from leading AI/HPC architecture at Intel and deploying supercomputers like Aurora, highlighting how access friction and idle infrastructure slow progress. Join them as they discuss Flex AI's innovative approach to simplifying heterogeneous compute, standardizing on consistent Kubernetes layers, and abstracting inference across various accelerators, allowing teams to iterate faster without wrestling with drivers, libraries, or cloud-by-cloud differences. Brijesh also shares insights into Flex AI's strategies for lifting utilization, protecting real-time workloads, and spanning the full lifecycle from fine-tuning to autoscaled inference, all while keeping complexity at bay.

Pre-amble I hope you enjoy this cross-over episode of the AI Engineering Podcast, another show that I run to act as your guide to the fast-moving world of building scalable and maintainable AI systems. As generative AI models have grown more powerful and are being applied to a broader range of use cases, the lines between data and AI engineering are becoming increasingly blurry. The responsibilities of data teams are being extended into the realm of context engineering, as well as designing and supporting new infrastructure elements that serve the needs of agentic applications. This episode is an example of the types of work that are not easily categorized into one or the other camp.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Brijesh Tripathi about FlexAI, a platform offering a service-oriented abstraction for AI workloadsInterview IntroductionHow did you get involved in machine learning?Can you describe what FlexAI is and the story behind it?What are some examples of the ways that infrastructure challenges contribute to friction in developing and operating AI applications?How do those challenges contribute to issues when scaling new applications/businesses that are founded on AI?There are numerous managed services and deployable operational elements for operationalizing AI systems. What are some of the main pitfalls that teams need to be aware of when determining how much of that infrastructure to own themselves?Orchestration is a key element of managing the data and model lifecycles of these applications. How does your approach of "workload as a service" help to mitigate some of the complexities in the overall maintenance of that workload?Can you describe the design and architecture of the FlexAI platform?How has the implementation evolved from when you first started working on it?For someone who is going to build on top of FlexAI, what are the primary interfaces and concepts that they need to be aware of?Can you describe the workflow of going from problem to deployment for an AI workload using FlexAI?One of the perennial challenges of making a well-integrated platform is that there are inevitably pre-existing workloads that don't map cleanly onto the assumptions of the vendor. What are the affordances and escape hatches that you have built in to allow partial/incremental adoption of your service?What are the elements of AI workloads and applications that you are explicitly not trying to solve for?What are the most interesting, innovative, or unexpected ways that you have seen FlexAI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on FlexAI?When is FlexAI the wrong choice?What do you have planned for the future of FlexAI?Contact Info LinkedInParting Question From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Links Flex AIAurora Super ComputerCoreWeaveKubernetesCUDAROCmTensor Processing Unit (TPU)PyTorchTritonTrainiumASIC == Application Specific Integrated CircuitSOC == System On a ChipLoveableFlexAI BlueprintsTenstorrentThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

At PyData Berlin, community members and industry voices highlighted how AI and data tooling are evolving across knowledge graphs, MLOps, small-model fine-tuning, explainability, and developer advocacy.

• Igor Kvachenok (Leuphana University / ProKube) combined knowledge graphs with LLMs for structured data extraction in the polymer industry, and noted how MLOps is shifting toward LLM-focused workflows.
• Selim Nowicki (Distill Labs) introduced a platform that uses knowledge distillation to fine-tune smaller models efficiently, making model specialization faster and more accessible.
• Gülsah Durmaz (Architect & Developer) shared her transition from architecture to coding, creating Python tools for design automation and volunteering with PyData through PyLadies.
• Yashasvi Misra (Pure Storage) spoke on explainable AI, stressing accountability and compliance, and shared her perspective as both a data engineer and active Python community organizer.
• Mehdi Ouazza (MotherDuck) reflected on developer advocacy through video, workshops, and branding, showing how creative communication boosts adoption of open-source tools like DuckDB.

Igor Kvachenok Master’s student in Data Science at Leuphana University of Lüneburg, writing a thesis on LLM-enhanced data extraction for the polymer industry. Builds RDF knowledge graphs from semi-structured documents and works at ProKube on MLOps platforms powered by Kubeflow and Kubernetes.

Connect: https://www.linkedin.com/in/igor-kvachenok/

Selim Nowicki Founder of Distill Labs, a startup making small-model fine-tuning simple and fast with knowledge distillation. Previously led data teams at Berlin startups like Delivery Hero, Trade Republic, and Tier Mobility. Sees parallels between today’s ML tooling and dbt’s impact on analytics.

Connect: https://www.linkedin.com/in/selim-nowicki/

Gülsah Durmaz Architect turned developer, creating Python-based tools for architectural design automation with Rhino and Grasshopper. Active in PyLadies and a volunteer at PyData Berlin, she values the community for networking and learning, and aims to bring ML into architecture workflows.

Connect: https://www.linkedin.com/in/gulsah-durmaz/

Yashasvi (Yashi) Misra Data Engineer at Pure Storage, community organizer with PyLadies India, PyCon India, and Women Techmakers. Advocates for inclusive spaces in tech and speaks on explainable AI, bridging her day-to-day in data engineering with her passion for ethical ML.

Connect: https://www.linkedin.com/in/misrayashasvi/

Mehdi Ouazza Developer Advocate at MotherDuck, formerly a data engineer, now focused on building community and education around DuckDB. Runs popular YouTube channels ("mehdio DataTV" and "MotherDuck") and delivered a hands-on workshop at PyData Berlin. Blends technical clarity with creative storytelling.

Connect: https://www.linkedin.com/in/mehd-io/

A practical workshop exploring threats, attack scenarios, and strategies for securing Helm charts using Cloudsmith's artifact management. Topics include verifying assets (public Helm charts, dependencies, and images), automating compliance with Trivy, and enforcing runtime OPA Gatekeeper policies to protect Kubernetes deployments. Learn to audit and manage Helm charts before distribution to prevent supply-chain attacks. Bonus: hands-on Instruqt lab analyzing insecure chart templates and demonstrating how to scan and validate Helm charts prior to production Kubernetes deployment.

This practical workshop explores common threats, attack scenarios, and proven strategies for securing Helm charts through Cloudsmith's artifact management, maintaining supply chain integrity and regulatory compliance. Topics include: verifying every asset (public Helm charts, dependencies, and images from popular OSS projects before deployment); automating compliance with Trivy and enforcing runtime OPA Gatekeeper security policies in real-time; preventing supply chain attacks by auditing and managing Helm charts before distributing through secure repositories; and acknowledging the manual overhead, as most charts are insecure-by-default and require further security checks by your team. Bonus: Hands-on Instruqt lab platform that analyzes actual insecure chart templates and demonstrates how to scan and detect vulnerabilities with open-source tools, implement security standards, and properly validate Helm charts prior to production Kubernetes deployment.

At Berlin Buzzwords, industry voices highlighted how search is evolving with AI and LLMs.

• Kacper Łukawski (Qdrant) stressed hybrid search (semantic + keyword) as core for RAG systems and promoted efficient embedding models for smaller-scale use.
• Manish Gill (ClickHouse) discussed auto-scaling OLAP databases on Kubernetes, combining infrastructure and database knowledge.
• André Charton (Kleinanzeigen) reflected on scaling search for millions of classifieds, moving from Solr/Elasticsearch toward vector search, while returning to a hands-on technical role.
• Filip Makraduli (Superlinked) introduced a vector-first framework that fuses multiple encoders into one representation for nuanced e-commerce and recommendation search.
• Brian Goldin (Voyager Search) emphasized spatial context in retrieval, combining geospatial data with AI enrichment to add the “where” to search.
• Atita Arora (Voyager Search) highlighted geospatial AI models, the renewed importance of retrieval in RAG, and the cautious but promising rise of AI agents.

Together, their perspectives show a common thread: search is regaining center stage in AI—scaling, hybridization, multimodality, and domain-specific enrichment are shaping the next generation of retrieval systems.

Kacper Łukawski Senior Developer Advocate at Qdrant, he educates users on vector and hybrid search. He highlighted Qdrant’s support for dense and sparse vectors, the role of search with LLMs, and his interest in cost-effective models like static embeddings for smaller companies and edge apps. Connect: https://www.linkedin.com/in/kacperlukawski/

Manish Gill
Engineering Manager at ClickHouse, he spoke about running ClickHouse on Kubernetes, tackling auto-scaling and stateful sets. His team focuses on making ClickHouse scale automatically in the cloud. He credited its speed to careful engineering and reflected on the shift from IC to manager.
Connect: https://www.linkedin.com/in/manishgill/

André Charton
Head of Search at Kleinanzeigen, he discussed shaping the company’s search tech—moving from Solr to Elasticsearch and now vector search with Vespa. Kleinanzeigen handles 60M items, 1M new listings daily, and 50k requests/sec. André explained his career shift back to hands-on engineering.
Connect: https://www.linkedin.com/in/andrecharton/

Filip Makraduli
Founding ML DevRel engineer at Superlinked, an open-source framework for AI search and recommendations. Its vector-first approach fuses multiple encoders (text, images, structured fields) into composite vectors for single-shot retrieval. His Berlin Buzzwords demo showed e-commerce search with natural-language queries and filters.
Connect: https://www.linkedin.com/in/filipmakraduli/

Brian Goldin
Founder and CEO of Voyager Search, which began with geospatial search and expanded into documents and metadata enrichment. Voyager indexes spatial data and enriches pipelines with NLP, OCR, and AI models to detect entities like oil spills or windmills. He stressed adding spatial context (“the where”) as critical for search and highlighted Voyager’s 12 years of enterprise experience.
Connect: https://www.linkedin.com/in/brian-goldin-04170a1/

Atita Arora
Director of AI at Voyager Search, with nearly 20 years in retrieval systems, now focused on geospatial AI for Earth observation data. At Berlin Buzzwords she hosted sessions, attended talks on Lucene, GPUs, and Solr, and emphasized retrieval quality in RAG systems. She is cautiously optimistic about AI agents and values the event as both learning hub and professional reunion.
Connect: https://www.linkedin.com/in/atitaarora/

This talk explores how to implement Agentic AI Golden Paths within Internal Developer Platforms using Kagent, a Kubernetes-native framework for building, deploying, and managing AI agents at enterprise scale. I'll demonstrate how Kagent treats AI agents as first-class Kubernetes resources with proper lifecycle management and observability, while leveraging Pulumi IDP templates and components to standardize infrastructure provisioning workflows.

A session on leveraging vCluster to create lightweight virtual Kubernetes clusters for secure multi-tenant deployments in IoT environments, combining KubeEdge for real-time AI processing. Topics include secure multi-tenant architecture, edge AI and data operations, and performance and cost optimization.

Join Rohit Ghumare as he demonstrates how to leverage vCluster to create lightweight virtual Kubernetes clusters that maximize security in multi-tenancy and minimize overhead in IoT environments. The talk also covers combining KubeEdge with vCluster for real-time AI processing, secure multi-tenant architectures, edge AI/data operations, and performance/cost optimization.

Kubeflow is a platform for building and deploying portable and scalable machine learning (ML) workflows using containers on Kubernetes-based systems.

We will code together a simple Kubeflow pipeline, show how to test it locally. As a bonus, we will explore one solution to avoid dependency hell using the modern dependency management tool uv.

If we want to run data science workloads (e.g. using Tensorflow, PyTorch, and others) in containers (for local development or production on Kubernetes), we need to build container images. Doing that with a Dockerfile is fairly straightforward, but is it the best method? In this talk, we'll take a well-known speech-to-text model (Whisper) and show various ways to run it in containers, comparing the outcomes in terms of image size and build time.

Building and deploying scalable, reproducible machine learning pipelines can be challenging, especially when working with orchestration tools like Slurm or Kubernetes. In this talk, we demonstrate how to create an end-to-end ML pipeline for anomaly detection in International Space Station (ISS) telemetry data using only Python code.

We show how Kubeflow Pipelines, MLFlow, and other open-source tools enable the seamless orchestration of critical steps: distributed preprocessing with Dask, hyperparameter optimization with Katib, distributed training with PyTorch Operator, experiment tracking and monitoring with MLFlow, and scalable model serving with KServe. All these steps are integrated into a holistic Kubeflow pipeline.

By leveraging Kubeflow's Python SDK, we simplify the complexities of Kubernetes configurations while achieving scalable, maintainable, and reproducible pipelines. This session provides practical insights, real-world challenges, and best practices, demonstrating how Python-first workflows empower data scientists to focus on machine learning development rather than infrastructure.

You're brilliant at debugging code. But can you debug a conversation?

Research shows 78% of engineers believe others perceive less than 70% of their technical brilliance. That perception gap costs you £50,000 per year in salary, kills your best architectural decisions, and keeps your platforms unnecessarily complex.

In this highly interactive talk, Steve Wade (The Pragmatic CNCF Guy) reveals how the same systematic thinking that makes you a great engineer can transform you into an exceptional communicator. No fluffy soft skills—just proven techniques that work.

You'll learn: - The "killer question" that transforms any professional interaction - Three gestures that instantly boost your executive presence - How to explain complex architecture in ways that get stakeholders leaning in, not zoning out - Why your monotone delivery is sabotaging your brilliant ideas (and how to fix it)

Through live demonstrations, partner exercises, and real-world case studies, you'll practice techniques that helped one engineer get her previously-rejected platform rebuild approved and promoted to VP within six months.

Perfect for engineers who are tired of watching less-technical colleagues get promoted faster, and leaders who want their teams' technical brilliance to finally get the recognition it deserves.

Come ready to participate. Leave ready to be unstoppable.

What does AI transformation really look like inside a 180-year-old company? In this episode of Data Unchained, we are joined by Younes Hairej, founder and CEO of Aokumo Inc, a trailblazing company helping enterprises in Japan and beyond bridge the gap between business intent and AI execution. From deploying autonomous AI agents that eliminate the need for dashboards and YAML, to revitalizing siloed, analog systems in manufacturing, Younes shares what it takes to modernize legacy infrastructure without starting over. Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US

ArtificialIntelligence #EnterpriseAI #AITransformation #Kubernetes #DevOps #GenAI #DigitalTransformation #OpenSourceAI #DataInfrastructure #BusinessInnovation #AIInJapan #LegacyModernization #MetadataStrategy #AIOrchestration #CloudNative #AIAutomation #DataGovernance #MLOps #IntelligentAgents #TechLeadership

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Send us a text Welcome to the cozy corner of the tech world! Datatopics is your go-to spot for relaxed discussions around tech, news, data, and society. In this episode of Data Topics, we sit down with Nick Schouten — data engineer at dataroots — for a full recap of KubeCon Europe 2025 and a deep dive into the current and future state of Kubernetes. We talk through what’s actually happening in the Kubernetes ecosystem — from platform engineering trends to AI infra challenges — and why some teams are doubling down while others are stepping away. Here’s what we cover: What Kubernetes actually is, and how to explain it beyond the buzzwordWhen Kubernetes is the right choice (e.g., hybrid environments, GPU-heavy workloads) — and when it’s overkillHow teams are trying to host LLMs and AI models on Kubernetes, and the blockers they’re hitting (GPUs, complexity, cost)GitOps innovations spotted at KubeCon — like tools that convert UI clicks into Git commits for infrastructure-as-codeWhy observability is still one of Kubernetes’ biggest weaknesses, and how a wave of new startups are trying to solve itThe push to improve developer experience for ML and data teams (no more YAML overload)The debate around abstraction vs control — and how some teams are turning away from Kubernetes entirely in favor of simpler toolsWhat “vibe coding” means in an LLM-driven world, and how voice-to-code workflows are changing how we write infrastructureWhether the future of Kubernetes is more “visible and accessible,” or further under the hoodIf you're a data engineer, MLOps practitioner, platform lead, or simply trying to stay ahead of the curve in infrastructure and AI — this episode is packed with relevant insights from someone who's hands-on with both the tools and the teaching.

Deliver flexible, scalable, and high-performance data storage that's perfect for AI and other modern applications with MongoDB 8.0 and MongoDB Atlas multi-cloud data platform. In MongoDB 8.0 in Action, Third Edition you'll find comprehensive coverage of the latest version of MongoDB 8.0 and the MongoDB Atlas multi-cloud data platform. Learn to utilize MongoDB’s flexible schema design for data modeling, scale applications effectively using advanced sharding features, integrate full-text and vector-based semantic search, and more. This totally revised new edition delivers engaging hands-on tutorials and examples that put MongoDB into action! In MongoDB 8.0 in Action, Third Edition you'll: Master new features in MongoDB 8.0 Create your first, free Atlas cluster using the Atlas CLI Design scalable NoSQL databases with effective data modeling techniques Master Vector Search for building GenAI-driven applications Utilize advanced search capabilities in MongoDB Atlas, including full-text search Build Event-Driven Applications with Atlas Stream Processing Deploy and manage MongoDB Atlas clusters both locally and in the cloud using the Atlas CLI Leverage the Atlas SQL interface for familiar SQL querying Use MongoDB Atlas Online Archive for efficient data management Establish robust security practices including encryption Master backup and restore strategies Optimize database performance and identify slow queries MongoDB 8.0 in Action, Third Edition offers a clear, easy-to-understand introduction to everything in MongoDB 8.0 and MongoDB Atlas—including new advanced features such as embedded config servers in sharded clusters, or moving an unsharded collection to a different shard. The book also covers Atlas stream processing, full text search, and vector search capabilities for generative AI applications. Each chapter is packed with tips, tricks, and practical examples you can quickly apply to your projects, whether you're brand new to MongoDB or looking to get up to speed with the latest version. About the Technology MongoDB is the database of choice for storing structured, semi-structured, and unstructured data like business documents and other text and image files. MongoDB 8.0 introduces a range of exciting new features—from sharding improvements that simplify the management of distributed data, to performance enhancements that stay resilient under heavy workloads. Plus, MongoDB Atlas brings vector search and full-text search features that support AI-powered applications. About the Book MongoDB 8.0 in Action, Third Edition you’ll learn how to take advantage of all the new features of MongoDB 8.0, including the powerful MongoDB Atlas multi-cloud data platform. You’ll start with the basics of setting up and managing a document database. Then, you’ll learn how to use MongoDB for AI-driven applications, implement advanced stream processing, and optimize performance with improved indexing and query handling. Hands-on projects like creating a RAG-based chatbot and building an aggregation pipeline mean you’ll really put MongoDB into action! What's Inside The new features in MongoDB 8.0 Get familiar with MongoDB’s Atlas cloud platform Utilizing sharding enhancements Using vector-based search technologies Full-text search capabilities for efficient text indexing and querying About the Reader For developers and DBAs of all levels. No prior experience with MongoDB required. About the Author Arek Borucki is a MongoDB Champion, certified MongoDB and MongoDB Atlas administrator with expertise in distributed systems, NoSQL databases, and Kubernetes. Quotes An excellent resource with real-world examples and best practices to design, optimize, and scale modern applications. - Advait Patel, Broadcom Essential MongoDB resource. Covers new features such as full-text search, vector search, AI, and RAG applications. - Juan Roy, Credit Suisse Reflects author’s practical experience and clear teaching style. It’s packed with real-world examples and up-to-date insights. - Rajesh Nair, MongoDB Champion & community leader This book will definitely make you a MongoDB star! - Vinicios Wentz, JP Morgan & Chase Co.

The talk focuses on the practical implementation of GitOps in a hybrid infrastructure setup, designing Helm charts and provisioning infrastructure with Terraform. Target audience: DevOps engineers or platform engineers building internal developer platforms, especially those working with Kubernetes.

KP Division of Research uses Airflow as a central technology for integrating diverse technologies in an agile setting. We wish to present a set of use-cases for AI/ML workloads, including imaging analysis (tissue segmentation, mammography), NLP (early identification of psychosis), LLM processing (identification of vessel diameter from radiological impressions), and other large data processing tasks. We create these “short-lived” project workflows to accomplish specific aims, and then may never run the job again, so leveraging generalized patterns are crucial to quickly implementing these jobs. Our Advanced Computational Infrastructure is comprised of multiple Kubernetes clusters, and we use Airflow to democratize the use of our batch level resources in those clusters. We use Airflow form-based parameters to deploy pods running R and Python scripts where generalized parameters are injected into scripts that follow internal programming patterns. Finally, we also leverage Airflow to create headless services inside Kubernetes for large computational workloads (Spark & H2O) that subsequent pods consume ephemerally.