Yandex Cloud's Data Pipeline for ML Models: Efficient and Scalable

🚀 Discovering the Data Processing Pipeline in Yandex Cloud for ML Models In the world of machine learning, an efficient data pipeline is key to success. Yandex Cloud has developed an innovative solution that processes terabytes of data daily, optimizing the training of AI models. This approach not only accelerates development but also ensures scalability and reliability in cloud environments. 🔧 System Architecture The core of the pipeline is based on a distributed architecture that integrates components like Apache Kafka for real-time data ingestion, Spark for batch processing, and Kubernetes for orchestration. This allows handling heterogeneous data flows, from user logs to images, with a focus on fault tolerance. • 📊 Ingestion and Storage: Data is captured via streams and stored in S3-compatible storage, ensuring durability. • ⚙️ Transformation: Using DataFlow, ETL jobs are applied to clean and enrich data, reducing preparation time by 40%. • 🧠 ML Training: Integration with TensorFlow and PyTorch, where the pipeline directly feeds GPU clusters for rapid iterations. 💡 Challenges Overcome One of the main challenges was handling massive volumes without latency. Yandex implemented dynamic auto-scaling and monitoring with Prometheus, resolving bottlenecks during load peaks. Additionally, they incorporated security with end-to-end encryption and compliance with regulations like GDPR. This innovation demonstrates how modern clouds can empower AI at an enterprise scale, inspiring teams to adopt similar practices. For more information, visit: https://enigmasecurity.cl #MachineLearning #DataPipeline #YandexCloud #BigData #AI #CloudComputing #TechInnovation If you're passionate about cybersecurity and tech, consider donating to Enigma Security for more content: https://lnkd.in/er_qUAQh Connect with me on LinkedIn to discuss trends in AI and security! https://lnkd.in/eFb3bY4C 📅 Wed, 01 Oct 2025 07:00:52 GMT 🔗Subscribe to the Membership: https://lnkd.in/eh_rNRyt

Why Amazon Nova Needs Data Pipelines to Deliver AI at Scale Success with Nova (or any AI system) depends on mastering your data pipelines: ⚡ Latency: Every millisecond matters — slow ingestion kills real-time AI. 🔐 Security & Compliance: Pipeline governance protects your PII and encrypted data. 💰 Cost: Inefficient architecture leads to runaway data transfer expenses. Great models are powerful. Great data pipelines make them unstoppable. https://lnkd.in/dD9rgCQy #AmazonNova #AWS #DataPipelines #AI #GenerativeAI #CloudComputing #DataEngineering #MachineLearning

Why Amazon Nova Needs Data Pipelines to Deliver AI at Scale Success with Nova (or any AI system) depends on mastering your data pipelines: ⚡ Latency: Every millisecond matters — slow ingestion kills real-time AI. 🔐 Security & Compliance: Pipeline governance protects your PII and encrypted data. 💰 Cost: Inefficient architecture leads to runaway data transfer expenses. Great models are powerful. Great data pipelines make them unstoppable. https://lnkd.in/dD9rgCQy #AmazonNova #AWS #DataPipelines #AI #GenerativeAI #CloudComputing #DataEngineering #MachineLearning

1,000 times faster analytics. Built-in AI agents. RAG without pipelines. MariaDB's new platform sounds impossible until you see the benchmarks. MariaDB just launched Enterprise Platform 2026. It changes everything about database development. The platform unifies three critical workloads: • Transactional processing • Real-time analytics • AI vector operations No more complex data pipelines. No separate vector databases. The standout feature? "RAG-in-a-Box." This automatically handles embedding, storing, and retrieving vector data. Your AI gets instant context from operational data without moving anything. Built-in AI copilots convert natural language into database actions. Developers can ask questions in plain English. The system does the rest. For unpredictable AI workloads, MariaDB Cloud offers serverless scaling. Pay only for what you use. Resources adjust automatically when AI agents spike. Performance jumped 250% compared to previous versions. The collaboration with Exasol brings analytics that process multi-terabyte workloads at unprecedented speeds. Real-time insights from operational data become reality. This matters because AI applications need different infrastructure. Traditional always-on setups struggle with activity spikes. MariaDB's approach solves that problem. Trusted by 75% of Fortune 500 companies, MariaDB now positions itself for the next wave of intelligent applications. The platform is available immediately to all users. What challenges do you face when building AI applications with existing database infrastructure? #MariaDB #AI #DatabaseTechnology 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gQRmr9-n

1,000 times faster analytics. Built-in AI agents. RAG without pipelines. MariaDB's new platform sounds impossible until you see the benchmarks. MariaDB just launched Enterprise Platform 2026. It changes everything about database development. The platform unifies three critical workloads: • Transactional processing • Real-time analytics • AI vector operations No more complex data pipelines. No separate vector databases. The standout feature? "RAG-in-a-Box." This automatically handles embedding, storing, and retrieving vector data. Your AI gets instant context from operational data without moving anything. Built-in AI copilots convert natural language into database actions. Developers can ask questions in plain English. The system does the rest. For unpredictable AI workloads, MariaDB Cloud offers serverless scaling. Pay only for what you use. Resources adjust automatically when AI agents spike. Performance jumped 250% compared to previous versions. The collaboration with Exasol brings analytics that process multi-terabyte workloads at unprecedented speeds. Real-time insights from operational data become reality. This matters because AI applications need different infrastructure. Traditional always-on setups struggle with activity spikes. MariaDB's approach solves that problem. Trusted by 75% of Fortune 500 companies, MariaDB now positions itself for the next wave of intelligent applications. The platform is available immediately to all users. What challenges do you face when building AI applications with existing database infrastructure? #MariaDB #AI #DatabaseTechnology 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gA8-jp4b

🧠 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 𝐨𝐧 𝐀𝐖𝐒 — 𝐓𝐡𝐞 𝐁𝐫𝐚𝐢𝐧 𝐁𝐞𝐡𝐢𝐧𝐝 𝐑𝐀𝐆 💡 “The real magic of Generative AI isn’t the LLM — it’s how well you feed it your data.” RAG (Retrieval-Augmented Generation) systems are transforming how enterprises use GenAI — connecting private, structured, and unstructured data for smarter, context-aware answers. And at the heart of this lies the Vector Database — the true “memory” of AI systems. Here’s how AWS makes enterprise-grade vector search effortless 👇 --- 🔍 1️⃣ Amazon Kendra — Semantic Search for Documents AI-powered document search that understands context, not just keywords. Perfect for internal knowledge bases and support chatbots. 📦 2️⃣ Amazon OpenSearch — Vector + Keyword Hybrid Search Blend full-text search with embeddings for hybrid RAG setups. Supports real-time updates and integrates natively with Bedrock. 🧱 3️⃣ Bedrock Knowledge Bases — Serverless RAG Made Simple Automatically manages embeddings, retrieval, and grounding for Bedrock models. No infrastructure, no pipeline headaches — just connect your S3 data. ⚙️ 4️⃣ Integration Example S3 → Knowledge Base (vector store) → Bedrock → Lambda API That’s your end-to-end enterprise RAG workflow — fully managed and scalable. --- 🚀 Having built pipelines where raw machine and operational data needed AI context, I’ve seen firsthand how vector search bridges the gap between “stored data” and “actionable intelligence.” Vector databases aren’t just storage — they’re the bridge between memory and reasoning in modern AI systems. #AWS #GenerativeAI #Bedrock #VectorDatabase #RAG #Kendra #OpenSearch #KnowledgeBase #Serverless #AIonAWS #MachineLearning #EnterpriseAI #CloudComputing #DataEngineering #AIArchitecture #LLM #RetrievalAugmentedGeneration

Most AI applications die from unpredictable workloads. Traditional databases can't handle the spikes. MariaDB Cloud's serverless approach scales with your AI agents automatically. MariaDB just launched Enterprise Platform 2026. It's a game-changer for AI development. The platform unifies everything: • Transactional data • Analytics • AI workloads All in one database. No more complex pipelines. The standout feature? RAG-in-a-Box. It handles embedding, storing, and retrieving vector data automatically. You don't need separate vector databases anymore. Performance is impressive too. The new MariaDB Exa engine processes multi-terabyte workloads 1,000x faster than traditional OLTP engines. But here's what excites me most. The serverless model adjusts resources when AI agents process tasks. It handles those unpredictable activity spikes that break traditional setups. This addresses a real pain point. AI workloads are inherently unpredictable. MariaDB also added AI copilots for developers. They convert natural language queries into database actions. That's a huge productivity boost. The numbers speak for themselves. Enterprise Server 11.8 shows 250% better performance compared to version 10.6. This feels like the future of database development. One platform for everything. What's your biggest challenge with AI application workloads? Are you dealing with scaling issues? #AI #Database #MariaDB 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gt8-FFdE

𝐖𝐡𝐲 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 𝐚𝐠𝐞𝐧𝐭𝐬 𝐧𝐞𝐞𝐝 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐭𝐡𝐚𝐧 𝐒𝐐𝐋 𝐚𝐠𝐞𝐧𝐭𝐬 🚀 As AI agents become central to product strategy, the data stack must support adaptable reasoning. MongoDB’s flexible documents enable agents to interpret unstructured data and evolving contexts in real time. MongoDB agents weave together chat logs, sensor streams, and user profiles without strict schemas. They surface relevant patterns, tailor prompts, and act on insights without costly schema migrations. Outcome: faster data onboarding, richer context, and more proactive automation. SQL agents excel at fixed transactions, but MongoDB agents thrive on probabilistic reasoning and cross-collection queries. They plan across evolving data, reduce latency by localizing reasoning, and scale with demand. This enables more resilient, context-aware automation across products. From governance to experimentation, MongoDB-based agents provide traceable decisions. Data provenance stays intact as agents reason with flexible schemas, enabling safer experimentation and faster iteration. I’d love to hear how you’re seeing MongoDB agents unlock new reasoning patterns in your organization. #ArtificialIntelligence #MachineLearning #GenerativeAI #AIAgents #MindzKonnected

Redis has acquired Featureform, a data orchestration framework that streamlines the delivery of structured data signals for AI applications. This acquisition addresses a key challenge in deploying production-grade AI: ensuring the right data reaches the right model at the right time. For more on how Redis is positioning itself as the "context engine" for AI, enhancing model deployment in enterprises. Read here: https://lnkd.in/eWrm-785 #AI #DataInfrastructure #DataEngineering #AgenticAI