Bernhard Merkle

Explore the power of #Kubernetes with #Couchbase! Gregor Bauer's #stackconf talk unveils how integrating both technologies revolutionizes your data solutions. 💡 Gain valuable insights and best practices for agile and efficient applications. Don't miss the chance to boost your #K8s expertise! Couchbase https://lnkd.in/deknQjV8

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🔥 Learn how to go from zero to production-grade ML orchestration on #Azure: https://lnkd.in/gvKKzfBP Thanks to Sachi Desai and Erin Schaffer for your input and feedback; it enabled us to add integrations with Azure Container Insights, AKS node auto-upgrade, and other improvements for Flyte on Azure users!

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Kubernetes + Postgres = CloudnativePG <3

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We are already at KCD Munich warming up for our talk at 1:20 pm today at the top stage! 🎤 Don't miss the chance to listen to Janis Kemper and Aniruddha Basak talk about managing Kubernetes with the Cluster API and Cluster Stacks. You can also grab a cool #syself sticker and chat with us about any questions regarding #kubernetes #kcdmunich #clusterstacks #clusterapi #devops

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Forget costly mistakes in #OpenStack storage with expert insights from Christian Huebner! Join us on May 28th to delve into strategic use-case scenarios and optimize your architecture for peak efficiency. Discover how effectively harnessing open source solutions can meet your specific requirements. Register now! https://bit.ly/4dNGNeW

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From release 24.11 onwards, PyBaMM includes an opt-in telemetry collection system to help us better understand how PyBaMM is being used. 💾 What is collected: Basic usage information like PyBaMM version, Python version, and which functions are run. ❓ Why: To understand how PyBaMM is used and prioritize development efforts. 🚶 Permanent opt-out: You will be prompted whether you want to opt in the first time you run PyBaMM after (re)installing. 👤 Privacy: No personal information (name, email, etc) or sensitive information (parameter values, simulation results, etc) is ever collected. The data is used and accessed only by the PyBaMM team and is not shared with any third parties. For more information, see:

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#AzureMachineLearning is comprised of several key components that all come together to facilitate the machine learning process and Alexander Walters explains the processes in our blog article here -> https://lnkd.in/gpDxWyaa

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ICYMI - at last week's #MSBuild, Thorsten Hans presented "Hyper-efficient serverless on AKS with Fermyon Platform for Kubernetes"! Follow along to learn how you could run your own high-density serverless platform on top of Microsoft Azure #Kubernetes Service: https://bit.ly/4e2DnoG

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New blog post: Access 150k+ Datasets from Hugging Face with DuckDB This blog post, co-authored by the Hugging Face and DuckDB teams, describes how you can use the hf:// prefix in DuckDB to access datasets in Hugging Face repositories. Read more at https://lnkd.in/dYf2PPKk

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Spark doesn't always interpret JSON how we'd like. For example, if each key/value pair in a JSON object is conceptually one item, Spark won't give you a row per item by default. This article, by Ian Griffiths, shows how to nudge Spark in the right direction using #MicrosoftFabric. https://lnkd.in/ebwzPca4

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The latest update for #Last9 includes "Datadog vs Dynatrace: A Comprehensive Comparison" and "#Grafana and #Docker: A Simple Way to Monitor Everything". https://lnkd.in/eV2ztwiV

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DuckDB Labs has added first-party support for #DeltaLake in DuckDB using the new delta-kernel-rs project. In this blog post, get a short overview of Delta Lake, Delta Kernel and the new DuckDB Delta extension. 🦆🦀 Learn more: https://lnkd.in/emZ6KXJn #opensource #oss #lfaidata #duckdb

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Building Neural Search Pipelines with #Azure and #Qdrant: A Step-by-Step Guide. In this article, we will transition from theory to practice, focusing on the implementation details. This hands-on guide will walk you through the process of setting up the data streaming pipeline, vectorizing the data, and integrating Qdrant for efficient search capabilities. By following along the article, you will be able to implement a fully functional “neural search pipeline” capable of handling large volumes of data efficiently. The integration of Azure services and Qdrant provides a powerful solution for building advanced search applications, making it easier to extract valuable insights from your data. https://lnkd.in/gdDmBxeR

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Smol VLMs ftw! Microsoft just dropped Florence - SOTA 200M & 800M parameter vision foundation model! 🔥 > Best part MIT Licensed! > 200M checkpoint beats Flamingo 80B (400x bigger model) by a huge margin > Performs captioning, object detection and segmentation, OCR, phrase grounding and more > Leverages FLD-5B dataset - 5.4 billion annotations across 126 million images > Multi-task learning > Finetuned model checkpoints beat the likes of PaLI, PaLI-X Thanks, and kudos to Microsoft for choosing open-source! 🤗 https://lnkd.in/en3xKke5

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Apache Arrow is an #opensource columnar memory format for both flat and hierarchical data. In the #moderndatalake, open data formats like Apache Arrow live in the storage layer in modern object storage, becoming the objects in #objectstorage. In their most recent release, Apache Arrow announced plans to separate from Apache DataFusion, the query execution framework that has been attached to Arrow since February 2019, and to graduate both projects to top level within The Apache Software Foundation. This move represents Arrow’s maturity and widespread adoption in the data community. Databases and datalakes SME Brenna Buuck discusses the key benefits of Apache Arrow, its notable projects and open source standards. https://hubs.li/Q02vsTBr0

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