84% of FM leaders want more AI. Only 10% have deployed it. #Calsoft built an Azure ML pipeline for a large HVAC company — structured telemetry, governed model lifecycle, automated deployment across sites. na2.hubs.ly/H060ZDS0 #MLOps #AzureML #HVAC #PredictiveMaintenance

🤖 Build an AI customer support agent for FREE! 🚀 ProjectPro Academy's Udemy course (2 hrs) covers OpenAI & AzureML. Boost productivity! All levels welcome. Explore on AIxplore! #AICustomerSupport #Azure #OpenAI #FreeCourse aixplore.in/course_details?s…

Stop manually deploying ML models. We have created a concise roadmap detailing how to build a production-ready CI/CD pipeline for real-time model deployment using Azure ML and modern MLOps principles. #Vinsys #MLOps #AzureML #MachineLearning #AI #DataScience #CloudComputing

Migrating legacy code to a new system is the kind of thing that nobody volunteers for. We’ve watched this play out for years at ZenML. Someone wants to try us out and they’re already running Airflow or Dagster or Kubeflow or Vertex or SageMaker. They like what they see in our docs but hit the wall of ‘converting actual pipeline code is going to take weeks.’ We noticed that most of the time, that’s where the curiosity dies. So we built something to fix that. Today we’re shipping migration skills for 11 of the most common ML and data pipeline platforms (see the image attached). For the obvious question: why not just paste your pipeline code into your coding agent of choice and ask it to convert? You can and people actually already do, but these skills have a few things baked in that a cold prompt doesn’t: ✋ Hand-curated (ok, mostly hand-curated) concept maps for each platform. The skill tells you what maps 1:1, what’s approximate, and what genuinely needs a rethink. 📔 Knowledge of ZenML best practices and the common migration pitfalls we’ve watched people hit over the years. 🔍 A conservative approach: when the skill isn’t sure, it flags this uncertainty instead of making something up. Each platform is built different and the skills treat them that way. Dagster’s asset-first world needs different thinking than Airflow’s scheduler-first model. Prefect’s dynamic runtime doesn’t translate the same way as Kedro’s Data Catalog. For the cloud platforms (AzureML, SageMaker, Vertex AI), there's also a "keep the backend" path via which you can switch to authoring your pipelines in ZenML while keeping your existing cloud execution. These skills work with Claude Code, Cursor, Codex, or honestly any coding agent that supports custom instructions or skills. A few things I want to be upfront about: ❌ The point of this feature isn’t to convince anyone that their current tool is bad. Airflow, Argo, AzureML, Dagster, Databricks, and all the other ML and data pipeline tools are serious projects built by serious teams and we’ve learned a lot from each. The point is just: if you’re curious about ZenML, the barrier to entry the cost of trying us out should be as low as possible. 🧠 When a skill hits something your current tool does and ZenML handles differently, it doesn’t paper over it. It tells you and explains how ZenML thinks about the problem pointing you at the native primitive and lets you decide what to do next. (Selfishly, we benefit a lot from this. We’ve spent way too many POCs hand-converting customer code and this is the version of that work we can hand to everyone, for free!) We’ve tested these skills ourselves, but 11 platforms is a lot of ground to cover. If you give one a spin, I’d love to hear from you - what worked, what broke, what’s missing, or what can be improved. Check it out here - github.com/zenml-io/skills is the repo to take them for a spin! Big thanks to the team for shipping these.

We just shipped migration skills that help you try out @ZenML_io from 11 different ML/data platforms. Airflow, Argo, AzureML, Dagster, Databricks, Flyte, Kedro, Metaflow, Prefect, SageMaker, Vertex AI. You could just paste your pipeline code into your coding agent and ask it to convert. But these skills have something extra baked in: → Hand-curated (ok ok, mostly!) concept maps for each platform showing what maps 1:1, what's approximate, and what needs genuine redesign → Knowledge of ZenML best practices and common migration pitfalls → A conservative approach: the skill flags what it's unsure about rather than making something up Each platform has its own migration story. Dagster's asset-first world needs different thinking than Airflow's scheduler-first model. Prefect's dynamic runtime doesn't translate the same way as Kedro's Data Catalog. For cloud platforms (AzureML, SageMaker, Vertex AI), the skills support "keep the backend" paths too: switch to ZenML authoring while keeping your existing cloud execution. They work with @AnthropicAI Claude Code, @cursor_ai, @OpenAI Codex, or any coding agent that supports custom instructions / agent skills. Open-source and free to install. We've tested them, but 11 platforms is a lot of ground to cover. If you give one a try and have feedback on what worked, what was off, or what's missing, we'd love to hear it. Issues and PRs very welcome. #MLOps #AgenticCoding #MachineLearning #OpenSource

自分のテキトー機械学習だと、MAPE 60→40がが精一杯か これ以上の精度ならAzureML引っ張り出してこないといけないけど、数字遊びになりそうだから一旦撤退 #SUNABACO #DX16th #PBL

🚀 Azure ML Pipelines - Step by Step An ML pipeline is just a workflow, automated. Data → Prep → Train → Evaluate → Deploy → Monitor No chaos. No manual runs. Just structure. Once you think in pipelines, ML becomes scalable and repeatable. #AzureML #AI #rakshitabelwal

🚀 NiCE Pune hiring AI Engineers! BE/BTech/ME/MTech for GenAI Ops, Azure AI/ML, LLM pipelines, RAG. Python/.NET Docker/K8s. #NiCEJobs #PuneTech #GenAI #AzureML #MLOps #LLM #RAG #AIEngineering #Jobformore Apply: jobformore.com/nice-off-camp…

After 5 attempts, I built & published my first Azure ML pipeline predicting house prices! 🚀 Tried Linear Regression, Decision Forest & Boosted Trees. Linear Regression performed best (R²=0.926, RMSE=5.35). #AzureML #DataScience #MachineLearning #AI #Portfolio

این سایت خیلی جالبه. باهاش می تونید چک کنید که هر Role در Microsoft Azure دقیقن چه دسترسی هایی رو اعطا می کنه و تاریخ آخرین به روزرسانی ها هم اعلام می کنند. برای مثال در این لینک Role ای که به نام AzureML Data Scientist می شناسیم، به طور کامل تشریح شده : azadvertizer.net/azrolesadve…

Ready to move from theory to practice? Microsoft’s latest guide shows how to apply AI in real-world data science workflows—from model building to deployment. Includes hands-on events, tools like #AzureML, and tips for bridging the learning gap. 🔗 msft.it/6019soeIv

Shipping AI is only half the job. 🚀 With Azure ML, @nixtlainc hosts models so teams can upload data and get forecasts almost instantly, backed by Microsoft for Startups. Watch the full video: msft.it/6018sqgXw #MicrosoftForStartups #AzureML #TimeSeriesForecasting

🚀 #FabConEurope Session: ML and AI Capabilities in Microsoft Fabric 📢 Speaker: Christian Henrik Reich Learn more👉 ow.ly/XkA150WgTjo #Microsoft #FabCon #MicrosoftFabric #MachineLearning #AutoML #MLOps #GenAI #AzureAIFoundry #AzureML #AIinFabric #AIPlatform #AIInnovation

Can you manage authentication and authorization for #AzureML? With so many companies jumping on the #AI train, are you ready to operate this? Introduction to Azure Machine Learning authentication and authorization learn.microsoft.com/en-us/tr… #AzOps #EntraID #RBAC #Azure

🚀 #FabConEurope Session: ML and AI Capabilities in Microsoft Fabric 📢 Speaker: Christian Henrik Reich Learn more👉 ow.ly/XkA150WgTjo #Microsoft #FabCon #MicrosoftFabric #MachineLearning #AutoML #MLOps #GenAI #AzureAIFoundry #AzureML #AIinFabric #AIPlatform #AIInnovation

I work in this area. Stuxnet was very specific, but the vibe is right. C2-less malware exists in private, but LLMs are already being used in operational settings. Where there is compute, there is C2. Host something at AzureML, Databricks, Huggingface, you name it. - huggingface.co/Kiddyz/testll… - arxiv.org/abs/2405.15652 - github.com/cylance/MarkovObf… - csoonline.com/article/402513…

Predicting the Future Using Azure Machine Learning Eric D. Boyd of responsiveX previews his #VSLive! session at Microsoft HQ in August where he explains how #AzureML empowers teams to build, deploy & manage ML models with ease and confidence. visualstudiomagazine.com/art…

AzureMLが使えれば、データがるだけで 統計の知識が極小でもAI予測モデルが作れる！ 医療研究を圧倒的に簡単になる！

とりあえず救急医学会総会に AzureMLで新規入院患者予測AIモデル作った話を、だしてみる カチカチ頭の人達につたわるか!? #SUNABACO

「Azure Machine Learningではじめる機械学習/LLM活用入門」をご寄贈いただきました。AOAIドーナツ本の著者陣も何人か入っていて、私もレビュー参加しております。AzureMLは旧来のMLからLLMまでモデル学習からデプロイまでの過程をとてもスムーズにカバーします。(AWSでいうとこのSageMakerポジション) mlflowとネイティブ統合されていて、mlflowで学習コード書いてたらデプロイから何からすごくスムーズに統合できてしまう、Azureの中でもかなり出来の良い玄人にも好まれるサービスです。 本書はその使い方を1からすげえ詳しく書いてくれています。なんならmlflowもすげえ詳しく書かれてるので、mlflow入門としてもとても良い。(意外と市場に無いような…) そんで分厚い。これも嬉しい。 gihyo.jp/book/2025/978-4-297…