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Approaches to maintaining data quality amidst AI-driven analytics.
Discussion on the analyst’s role in an AI-driven world.
Featured speaker at GenerationAI 2025
Featured speaker at GenerationAI 2025
Featured speaker at GenerationAI 2025
In this episode, we travel back to one of the great origin stories in gene regulation: the discovery of lin-4, the first-ever microRNA. In Caenorhabditis elegans, scientists found that tiny non-coding RNAs could silence gene expression by pairing with target mRNAs — launching the entire field of microRNA biology. We explore: How lin-4 regulates developmental timing by repressing LIN-14 protein The discovery of small RNAs (22 and 61 nucleotides) as gene regulators The first evidence for RNA-RNA antisense interactions controlling translation Why this work reshaped our understanding of gene expression across species How a worm taught us that not all genes code for proteins 📖 Based on the research article: "The C. elegans Heterochronic Gene lin-4 Encodes Small RNAs with Antisense Complementarity to lin-14" Rosalind C. Lee, Rhonda L. Feinbaum & Victor Ambros. Published in Cell (1993). 🔗 https://doi.org/10.1016/0092-8674(93)90529-Y 🎧 Subscribe to the WOrM Podcast for more whole-organism breakthroughs that reshaped biology!
This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.
📩 More info: 🔗 www.veerenchauhan.com 📧 [email protected]
Ibtissem Hattab (GDE Cloud) : Cette présentation offrira un aperçu général des annonces d'IA les plus percutantes faites lors des deux événements majeurs de Google en mai. Elle synthétisera les thèmes clés et les technologies présentées, des outils destinés aux développeurs aux nouvelles plateformes publicitaires. Ce titre est idéal pour un public large intéressé par un résumé complet mais concis de la dernière stratégie d'IA de Google et de ses applications pratiques.
Rémi partagera sa vision et ses réflexions sur comment l’IA peut réellement améliorer l’expérience utilisateur.
Get up to speed with rapid-fire insights from our expert panelists on the integration of knowledge graphs and AI—setting the context for the discussion to follow.
Jason Bryll is a healthcare analytics expert and hiring manager with nearly two decades of experience. In this episode, Jason explains what healthcare analytics entails, why it's essential, and the role of AI in the field. More importantly, you'll learn how to stand out to hiring managers-- even in today's market! Wanna dive further into healthcare analytics? Here's your next podcast: 👉 https://datacareerpodcast.com/episode/160-she-became-a-data-analyst-after-a-20-year-career-in-physical-therapy-melody-santos 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 Introduction to Healthcare Analytics 00:27 Jason's Career Journey 02:23 What Is Healthcare Analytics? 06:37 Parable Associates 10:56 Understanding Revenue Cycle and Accounts Receivable 15:00 Complexities in Healthcare Data Management 19:47 The Importance of Domain Knowledge 27:12 The Importance of Building a Strong Portfolio 31:43 Recommended Data Tools and Platforms 34:10 Advice To Become A Healthcare Analyst 🔗 CONNECT WITH JASON BRYLL 🎥 YouTube Channel: https://www.youtube.com/@UCGh1LOrX0mWuoWZk5J10zkw 🤝 LinkedIn: https://www.linkedin.com/in/jason-bryll/ 📸 Instagram: https://www.instagram.com/parable_associates/ 💻 Website: https://parableassociates.com/ Check out Jason's Healthcare Analyst courses here: 👉 https://www.parableacademy.com/link/d7GlNy?url=https%3A%2F%2Fwww.parableacademy.com%2Fcourse%3Fcourseid%3Drcm-analyst 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!
To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more
If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.
👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa
In today’s dynamic data environments, tables and schemas are constantly evolving and keeping semantic layers up to date has become a critical operational challenge. Manual updates don’t scale, and delays can quickly lead to broken dashboards, failed pipelines, and lost trust. We’ll show how to harness Apache Airflow 3 and its new event-driven scheduling capabilities to automate the entire lifecycle: detecting table and schema changes in real time, parsing and interpreting those changes, and shifting left the updating of semantic models across dbt, Looker, or custom metadata layers. AI agents will add intelligence and automation that rationalize schema diffs, assess impact of changes, and propose targeted updates to semantic layers reducing manual work and minimizing the risk of errors. We’ll dive into strategies for efficient change detection, safe incremental updates, and orchestrating workflows where humans collaborate with AI agents to validate and deploy changes. By the end of the session, you’ll understand how to build resilient, self-healing semantic layers that minimize downtime, reduce manual intervention, and scale effortlessly across fast-changing data environments.
What if your Airflow tasks could understand natural language AND adapt to schema changes automatically, while maintaining the deterministic, observable workflows we rely on? This talk introduces practical patterns for AI-native orchestration that preserve Airflow’s strengths while adding intelligence where it matters most. Through a real-world example, we’ll demonstrate AI-powered tasks that detect schema drift across multi-cloud systems and perform context-aware data quality checks that go beyond simple validation—understanding business rules, detecting anomalies, and generating validation queries from prompts like “check data quality across regions.” All within static DAG structures you can test and debug normally. We’ll show how AI becomes a first-class citizen by combining Airflow’s features, assets for schema context, Human-in-the-Loop for approvals, and AssetWatchers for automated triggers, with engines such as Apache DataFusion for high-performance query execution and support for cross-cloud data processing with unified access to multiple storage formats. These patterns apply directly to schema validation and similar cases where natural language can simplify complex operations. This isn’t about bolting AI onto Airflow. It’s about evolving how we build workflows, from brittle rules to intelligent adaptation, while keeping everything testable, auditable, and production-ready.
In this keynote, Peeyush Rai and Vikram Koka will be walking through how Airflow is being used as part of a Agentic AI platform servicing insurance companies, which runs on all the major public clouds, leveraging models from Open AI, Google (Gemini), AWS (Claude and Bedrock). This talk walks through the details of the actual end user business workflow including gathering relevant financial data to make a decision, as well as the tricky challenge of handling AI hallucinations, with new Airflow capabilities such as “Human in the loop”. This talk offers something for both business and technical audiences. Business users will get a clear view of what it takes to bring an AI application into production and how to align their operations and business teams with an AI enabled workflow. Meanwhile, technical users will walk away with practical insights on how to orchestrate complex business processes enabling a seamless collaboration between Airflow, AI Agents and Human in the loop.
The workflow orchestration team at Zoox aims to build a solution for orchestrating heterogeneous workflows encompassing data, ML, and QA pipelines. We have encountered two primary challenges: first, the steep learning curve for new Airflow users and the need for a user-friendly yet scalable development process; second, integrating and migrating existing pipelines with established solutions. This presentation will detail our approach, as a small team at Zoox, to address these challenges. The discussion will first present an exciting introduction to Zoox and what we do. Then we will walk down the memory lane of the past and current of Airflow use at Zoox. Furthermore, we will share our strategies for simplifying the Airflow DAG creation process and enhancing user experience. Lastly, we will share a few of our thoughts for how to grow the team and grow Airflow’s presence at Zoox in the future.
Join us to explore the DAG Upgrade Agent. Developed with Google Agent Development Kit and powered by Gemini, the DAG Upgrade Agent uses a rules-based framework to analyze DAG code, identify compatibility issues between core airflow and provider package versions, and generates precise upgrade recommendations and automated code conversions. Perfect for upcoming Airflow 3.0 migrations.
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.
This session will detail Allegro’s, a leading e-commerce company in Poland, journey with Apache Airflow. It will chart our evolution from a custom, on-premises Airflow-as-a-Service solution through a significant expansion to over 300 Cloud Composer instances in Google Cloud, culminating in Airflow becoming the core of our data processing. We orchestrate over 64,000 regular tasks spanning over 6,000 active DAGs on more than 200 Airflow instances. From feeding business-supporting dashboards, to managing main data marts, and handling ML pipelines, and more. We will share our practical experiences, lessons learned, and the strategies employed to manage and scale this critical infrastructure. Furthermore, we will introduce our innovative economy-of-share approach for providing ready-to-use Airflow environments, significantly enhancing both user productivity and cost efficiency.
Red Hat’s unified data and AI platform relies on Apache Airflow for orchestration, alongside Snowflake, Fivetran, and Atlan. The platform prioritizes building a dependable data foundation, recognizing that effective AI depends on quality data. Airflow was selected for its predictability, extensive connectivity, reliability, and scalability. The platform now supports business analytics, transitioning from ETL to ELT processes. This has resulted in a remarkable improvement in how we make data available for business decisions. The platform’s capabilities are being extended to power Digital Workers (AI agents) using large language models, encompassing model training, fine-tuning, and inference. Two Digital Workers are currently deployed, with more in development. This presentation will detail the rationale and background of this evolution, followed by an explanation of the architectural decisions made and the challenges encountered and resolved throughout the process of transforming into an AI-enabled data platform to power Red Hat’s business.