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#dataprocessing

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LinkedIn, jak to wielka platforma, przetwarza dużo danych. Musi to robić szybko i sprawnie, stąd tutaj zwykła baza danych nie wystarczy i infrastruktura jest "trochę" większa. Warto wiedzieć, jak to robią duże serwisy.

#DataProcessing #NoSQL #SQL #Oracle #BazaDanych #ArchitekturaOprogramowania

blog.bytebytego.com/p/how-link

ByteByteGo Newsletter · How LinkedIn Scaled User Restriction System to 5 Million Queries Per SecondBy ByteByteGo

❇️ Unlocking #AI Potential with #Kubernetes & Key Benefits 🌐

Kubernetes is revolutionizing AI deployment with its scalable and efficient container orchestration capabilities.

Here are the key perks:
🔹Scalability: Automatically scales AI workloads to handle surges in demand, ensuring seamless performance.
🔹Efficient Resource Management: Optimizes CPU, GPU, and memory allocation based on workload needs, reducing costs.
🔹Multi-Tenancy: Allows multiple teams to share resources without interference, enhancing collaboration.
🔹High Availability: Ensures AI applications remain operational even during hardware failures, thanks to self-healing capabilities.
🔹Seamless Deployment: Enables consistent deployment across different environments, from on-premises to cloud.

klizos.com/kubernetes-ai-deplo

#Kubernetes #AI #MachineLearning #Scalability #Efficiency #MultiTenancy #HighAvailability #CloudNative #DevOps #CI/CD #ContainerOrchestration #ITInfrastructure #Innovation #TechTrends #CloudApplications #DataProcessing

❇️#Kubernetes in action: real-world #AI deployment success stories 🌐
Kubernetes is transforming AI deployment across industries with its robust container orchestration capabilities.

Some use cases:
🔸Netflix: Uses Kubernetes to simplify and streamline deployments, ensuring resilience and scalability for its vast array of microservices.
🔸HSBC: Manages applications across private and public clouds with Kubernetes, optimizing resource costs and meeting regulatory requirements.
🔸Airbnb: Orchestrates resources for complex machine learning models, ensuring efficient processing of large datasets.
🔸YouTube: Utilizes Kubernetes for scalable video delivery, handling spikes in demand without disrupting service quality.

klizos.com/kubernetes-ai-deplo

#Kubernetes #AI #MachineLearning #Netflix #GitLab #HSBC #Airbnb #YouTube #CloudNative #DevOps #Scalability #HighAvailability #CI/CD #ContainerOrchestration #ITInfrastructure #Innovation #TechTrends #CloudApplications #DataProcessing #RealTimeAnalytics #Automation

Kubernetes is revolutionizing how organizations manage complex applications, offering scalability, flexibility, and robustness.
Here are some more key use cases:
✅ Large-Scale Apps - Kubernetes helps manage large-scale apps during peak traffic, ensuring a smooth user experience.
✅ AI/ML with Kubernetes - NVIDIA uses Kubernetes to deploy AI models at scale, optimizing resource utilization.
✅ DevOps Efficiency - Spotify speeds up software delivery with Kubernetes, reducing time to market.
...

#Kubernetes #CloudNative #AI #MachineLearning #DevOps #Microservices #EdgeComputing #CloudNetworking #HybridCloud #Scalability #Flexibility #Reliability #CI/CD #ContainerOrchestration #ITInfrastructure #Innovation #TechTrends #CloudApplications #DataProcessing #RealTimeAnalytics #Automation #DigitalTransformation

acecloud.ai/resources/blog/kub

Going to other clubs: 😵😒🍹
Going to GBIF clubs: 😎💚🧬#DataUseClub

Registrations are open for our next Data Use Club practical session - Data standards & processing!

This session aims to give users the tools to understand the context of the data available on GBIF and help them decide if the data is fit for their analyses.

📍 Wed 12 Feb 2025 15:00-16:30 CET. This event will be recorded.

🔗 gbif.link/datauseclub-practica

🧐 In threat intelligence, you often have to deal with a bunch of different data sources, but these data can come in different forms and need to be processed before they can be analyzed!

Well, that’s exactly what you can do with Docling, an open-source library that allows you to process different kinds of data (PDF, DOCX, PPTX, XLSX, images, and more). Bonus: you can also use it for chunking your data, for example for a RAG.

One of the easiest ways to use it is as follows, where I pass a PDF report and convert it into JSON and Markdown, and now it is much easier to process further and extract additional details without polluting my pipeline with garbage data from the file structure.

And it supports OCR 🤓

You can find my code below 👇

➡️ github.com/DS4SD/docling/tree/

➡️ Code: gist.github.com/fr0gger/251cf8