Revue des éléments à considérer et outils Azure pour mieux encadrer votre IA et éviter les dérapages en production.

GitHub Copilot pour Azure, trois étapes essentielles: améliorer l’efficacité du codage, faciliter le déploiement et maîtriser le dépannage.

Découvrez comment GitHub Copilot peut simplifier et accélérer le déploiement cloud sur Azure. Cette session vous guidera à travers trois étapes essentielles : améliorer l’efficacité du codage, faciliter le déploiement des applications et maîtriser le dépannage avancé. Apprenez à exploiter la scalabilité d’Azure avec Copilot comme assistant IA, garantissant des applications performantes et fiables. Que vous soyez développeur ou professionnel IT, repartez avec les compétences nécessaires pour créer, déployer et gérer vos solutions cloud en toute confiance.

Automatisation avancée mettant en oeuvre Power Automate et AI Builder pour optimiser la gestion des demandes de support.

Atelier pas à pas pour créer votre premier agent avec le service Azure AI Agent, avec l'aide d'un tuteur sur place. Atelier dure environ 75 minutes; nécessite une souscription Azure et un compte GitHub.

Créer pas à pas votre premier agent avec le service Azure AI Agent avec l'aide d'un tuteur sur place. L'atelier dure environ 75 minutes, nécessite une souscription Azure, un compte GitHub, et se base sur https://aka.ms/aitour/wrk552 (instructions en anglais).

L'infrastructure intelligente est un concept en évolution rapide qui exploite les technologies avancées pour optimiser la performance, la fiabilité et la sécurité des systèmes informatiques. En intégrant la maintenance prédictive, l'assistance intelligente et d'autres approches innovantes, les organisations peuvent améliorer la qualité de service de leur infrastructure, bénéficiant ainsi aux applications qui en dépendent. Dans cette session, nous étudierons des principes tels que AIOps, Agents autonomes, et autres, pour améliorer la fiabilité, la performance et la sécurité de notre infrastructure Cloud. Nous apprendrons également des fournisseurs de services Cloud (Azure, AWS) comment ils mettent en œuvre l'infrastructure intelligente dans le Cloud public, et comment nous pouvons tirer parti de leur expérience et de leurs technologies pour le bénéfice de nos applications.

L'infrastructure intelligente est un concept en évolution rapide qui exploite les technologies avancées pour optimiser la performance, la fiabilité et la sécurité des systèmes informatiques. En intégrant la maintenance prédictive, l'assistance intelligente et d'autres approches innovantes, les organisations peuvent améliorer la qualité de service de leur infrastructure Cloud.

Learn how to build a GenAI application in Python using Azure and the OpenAI API.

In this podcast episode, we talked with Eddy Zulkifly about From Supply Chain Management to Digital Warehousing and FinOps

About the Speaker: Eddy Zulkifly is a Staff Data Engineer at Kinaxis, building robust data platforms across Google Cloud, Azure, and AWS. With a decade of experience in data, he actively shares his expertise as a Mentor on ADPList and Teaching Assistant at Uplimit. Previously, he was a Senior Data Engineer at Home Depot, specializing in e-commerce and supply chain analytics. Currently pursuing a Master’s in Analytics at the Georgia Institute of Technology, Eddy is also passionate about open-source data projects and enjoys watching/exploring the analytics behind the Fantasy Premier League.

In this episode, we dive into the world of data engineering and FinOps with Eddy Zulkifly, Staff Data Engineer at Kinaxis. Eddy shares his unconventional career journey—from optimizing physical warehouses with Excel to building digital data platforms in the cloud.

🕒 TIMECODES 0:00 Eddy’s career journey: From supply chain to data engineering 8:18 Tools & learning: Excel, Docker, and transitioning to data engineering 21:57 Physical vs. digital warehousing: Analogies and key differences 31:40 Introduction to FinOps: Cloud cost optimization and vendor negotiations 40:18 Resources for FinOps: Certifications and the FinOps Foundation 45:12 Standardizing cloud cost reporting across AWS/GCP/Azure 50:04 Eddy’s master’s degree and closing thoughts

🔗 CONNECT WITH EDDY Twitter - https://x.com/eddarief Linkedin - https://www.linkedin.com/in/eddyzulkifly/ Github: https://github.com/eyzyly/eyzyly ADPList: https://adplist.org/mentors/eddy-zulkifly

🔗 CONNECT WITH DataTalksClub 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 LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

The role of data and AI engineers is more critical than ever. With organizations collecting massive amounts of data, the challenge lies in building efficient data infrastructures that can support AI systems and deliver actionable insights. But what does it take to become a successful data or AI engineer? How do you navigate the complex landscape of data tools and technologies? And what are the key skills and strategies needed to excel in this field?  Deepak Goyal is a globally recognized authority in Cloud Data Engineering and AI. As the Founder & CEO of Azurelib Academy, he has built a trusted platform for advanced cloud education, empowering over 100,000 professionals and influencing data strategies across Fortune 500 companies. With over 17 years of leadership experience, Deepak has been at the forefront of designing and implementing scalable, real-world data solutions using cutting-edge technologies like Microsoft Azure, Databricks, and Generative AI. In the episode, Richie and Deepak explore the fundamentals of data engineering, the critical skills needed, the intersection with AI roles, career paths, and essential soft skills. They also discuss the hiring process, interview tips, and the importance of continuous learning in a rapidly evolving field, and much more. Links Mentioned in the Show: AzureLibAzureLib Academy Connect with DeepakGet Certified! Azure FundamentalsRelated Episode: Effective Data Engineering with Liya Aizenberg, Director of Data Engineering at AwaySign up to attend RADAR: Skills Edition  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Enjeux du Numérique Responsable et de l’IA, offre de Devoteam pour adresser les défis environnementaux des DSI, approche Microsoft Cloud for Sustainability et autres outils pour opérer les projets cloud et IA de façon durable.

The rapid expansion of data centers is reshaping the industry, requiring new approaches to design, safety, and leadership. 

We’re excited to have Doug Mouton, former Senior Eng Lead, Datacenter Design Engineering and Construction at Meta, as a guest on this latest episode of the “Data Center Revolution” podcast. Doug joins us with key insights into leadership, adaptability, and the evolution of hyperscale data-center construction. He also shares his journey from military service to leading large-scale infrastructure projects in the data center industry, highlighting key transferable skills along the way. 

Key Takeaways:

(07:54) Military mindset builds strong leaders. (14:25) Veterans thrive in high-pressure environments. (25:32) Katrina exposed disaster preparedness gaps. (35:16) Microsoft shifted to cost-effective data center designs. (43:56) Data centers face growing energy challenges. (54:26) Safety-first culture boosts efficiency and morale. (01:21:43) Data centers must transition to hybrid cooling solutions. (01:42:09) AI needs ethical guardrails.

Resources Mentioned:

Fidelis New Energy | Website - https://www.fidelisinfra.com

Microsoft Azure - https://azure.microsoft.com/en-us/

Meta - https://about.meta.com/

Jacobs - https://www.jacobs.com/

National Guard - https://nationalguard.com/

Jones Lang LaSalle - https://www.us.jll.com/

Thank you for listening to “Data Center Revolution.” Don’t forget to leave us a review and subscribe so you don’t miss an episode.   To learn more about Overwatch, visit us at https://linktr.ee/overwatchmissioncritical 

DataCenterIndustry #NuclearEnergy #FutureOfDataCenters #AI

Discover how DXC and Microsoft's FastTrack team delivered a scalable passport and citizenship case management solution for New Zealand's DIA using the Power Platform and Azure. Learn about the journey, lessons learned, and the epic go-live success from the lead architects.

Learn to use Managed Identities in Dataverse plugins to safely authenticate with Azure endpoints, without secrets. From a new Dataverse and Azure environment to a modern secure integration, you will be able to update your code to the latest security standards.

Descubra los conceptos básicos de la IA generativa, incluidos los modelos y las funcionalidades principales. Aprenda a utilizar estos modelos dentro del ecosistema de Azure y a aprovechar varios servicios para crear sus propias aplicaciones de IA generativa.

Explore Snowflake’s core concepts and unique features that differentiates it from industry competitors, such as, Azure Synapse and Google BigQuery. This book provides recipes for architecting and developing modern data pipelines on the Snowflake data platform by employing progressive techniques, agile practices, and repeatable strategies. You’ll walk through step-by-step instructions on ready-to-use recipes covering a wide range of the latest development topics. Then build scalable development pipelines and solve specific scenarios common to all modern data platforms, such as, data masking, object tagging, data monetization, and security best practices. Throughout the book you’ll work with code samples for Amazon Web Services, Microsoft Azure, and Google Cloud Platform. There’s also a chapter devoted to solving machine learning problems with Snowflake. Authors Dillon Dayton and John Eipe are both Snowflake SnowPro Core certified, specializing in data and digital services, and understand the challenges of finding the right solution to complex problems. The recipes in this book are based on real world use cases and examples designed to help you provide quality, performant, and secured data to solve business initiatives. What You’ll Learn Handle structured and un- structured data in Snowflake. Apply best practices and different options for data transformation. Understand data application development. Implement data sharing, data governance and security. Who This book Is For Data engineers, scientists and analysts moving into Snowflake, looking to build data apps. This book expects basic knowledge in Cloud (AWS or Azure or GCP), SQL and Python

Overview of Small Language Models (SLMs) in solving business problems and improving device capabilities. Compares SLM with Large Language Models (LLMs) and explains why SLM may be better suited for an organization's needs, with emphasis on customization via fine-tuning. Covers deploying models on edge devices for real-time processing and decision-making, plus a roadmap from cloud development to cross-platform deployment, with Azure for seamless integration and IP ownership. Concludes with how Microsoft's Student Ambassador Program can empower students to bridge academia and industry and foster innovation.

Overview of how Small Language Models (SLMs) can solve business problems and boost device intelligence, with a comparison to Large Language Models (LLMs). Focus on fine-tuning and customization, edge deployments for real-time processing, and a cloud-to-edge deployment roadmap using Azure to maintain IP ownership and integration. Includes perspectives on leveraging SLMs to revolutionize devices and mentions Microsoft’s Student Ambassador Program energizing students to advance intelligent tech.