AI For Real-Time Data Processing

If you’re an AI engineer trying to optimize your LLMs for inference, here’s a quick guide for you 👇 Efficient inference isn’t just about faster hardware, it’s a multi-layered design problem. From how you compress prompts to how your memory is managed across GPUs, everything impacts latency, throughput, and cost. Here’s a structured taxonomy of inference-time optimizations for LLMs: 1. Data-Level Optimization Reduce redundant tokens and unnecessary output computation. → Input Compression:  - Prompt Pruning, remove irrelevant history or system tokens  - Prompt Summarization, use model-generated summaries as input  - Soft Prompt Compression, encode static context using embeddings  - RAG, replace long prompts with retrieved documents plus compact queries → Output Organization:  - Pre-structure output to reduce decoding time and minimize sampling steps 2. Model-Level Optimization (a) Efficient Structure Design → Efficient FFN Design, use gated or sparsely-activated FFNs (e.g., SwiGLU) → Efficient Attention, FlashAttention, linear attention, or sliding window for long context → Transformer Alternates, e.g., Mamba, Reformer for memory-efficient decoding → Multi/Group-Query Attention, share keys/values across heads to reduce KV cache size → Low-Complexity Attention, replace full softmax with approximations (e.g., Linformer) (b) Model Compression → Quantization:  - Post-Training, no retraining needed  - Quantization-Aware Training, better accuracy, especially <8-bit → Sparsification:  - Weight Pruning, Sparse Attention → Structure Optimization:  - Neural Architecture Search, Structure Factorization → Knowledge Distillation:  - White-box, student learns internal states  - Black-box, student mimics output logits → Dynamic Inference, adaptive early exits or skipping blocks based on input complexity 3. System-Level Optimization (a) Inference Engine → Graph & Operator Optimization, use ONNX, TensorRT, BetterTransformer for op fusion → Speculative Decoding, use a smaller model to draft tokens, validate with full model → Memory Management, KV cache reuse, paging strategies (e.g., PagedAttention in vLLM) (b) Serving System → Batching, group requests with similar lengths for throughput gains → Scheduling, token-level preemption (e.g., TGI, vLLM schedulers) → Distributed Systems, use tensor, pipeline, or model parallelism to scale across GPUs My Two Cents 🫰 → Always benchmark end-to-end latency, not just token decode speed → For production, 8-bit or 4-bit quantized models with MQA and PagedAttention give the best price/performance → If using long context (>64k), consider sliding attention plus RAG, not full dense memory → Use speculative decoding and batching for chat applications with high concurrency → LLM inference is a systems problem. Optimizing it requires thinking holistically, from tokens to tensors to threads. Image inspo: A Survey on Efficient Inference for Large Language Models ---- Follow me (Aishwarya Srinivasan) for more AI insights!

Let’s face it—traditional knowledge bases feel like relics in a world that changes by the second. I’ve been searching for something more dynamic, and I think I’ve finally found it. Graphiti: an open-source framework that redefines AI memory through real-time, bi-temporal knowledge graphs. Developed by Zep AI (YC W24), Graphiti is engineered to handle the complexities of dynamic data environments, making it a game-changer for AI agents. Key takeaways: 1) Real-time incremental updates: Graphiti processes new data episodes instantly, eliminating the need for batch recomputations. This ensures that your AI agents always have access to the most current information. 2) Bi-temporal data model: It meticulously tracks both the occurrence and ingestion times of events, allowing for precise point-in-time queries. This dual-timeline approach enables a nuanced understanding of how knowledge evolves over time. 3) Hybrid retrieval system: By combining semantic embeddings, keyword search (BM25), and graph traversal, Graphiti delivers low-latency, context-rich responses without relying solely on large language model summarizations. 4) Custom entity definitions: With support for developer-defined entities via Pydantic models, Graphiti offers the flexibility to tailor the knowledge graph to specific domains and applications. 5) Scalability: Designed for enterprise-level demands, Graphiti efficiently manages large datasets through parallel processing, ensuring performance doesn't degrade as data scales. Integration with Zep Memory !!!! Graphiti powers the core of Zep’s memory layer for LLM-powered assistants and agents. This integration allows for the seamless fusion of personal knowledge with dynamic data from various business systems, such as CRMs and billing platforms. The result is AI agents capable of long-term recall and state-based reasoning. Graphiti vs. GraphRAG_______________________________________________ While Microsoft's GraphRAG focuses on static document summarization, Graphiti excels in dynamic data management. It supports continuous, incremental updates and offers a more adaptable and temporally aware approach to knowledge representation. This makes Graphiti particularly suited for applications requiring real-time context and historical accuracy. #AI #KnowledgeGraphs #Graphiti #RealTimeData #Innovation #TechCommunity #OpenSource #AIDevelopment #DataScience #MachineLearning #Ontology #ZapAI #Microsoft #AdaptiveAI

Business intelligence has always been about evaluating the past. Now, AI analytics are giving us a look into the future. For years, reporting was static and retrospective. It helped leaders understand what happened last month or last quarter, but offered little support for acting in the moment or anticipating what might come next. AI is changing that. By analyzing live data streams, surfacing patterns in real-time, and taking meaningful action, AI gives leaders a clearer lens on the present and a sharper view of the future. I’ve seen the impact across industries: • Healthcare: Identifying top call drivers and adjusting self-service flows immediately to reduce patient wait times. • Logistics: Spotting delays in agent response times and redistributing resources before service levels slip. • Retail: Tracking sentiment by product line and adapting campaigns to reflect what customers are actually saying. The benefits extend well beyond efficiency. With AI analytics, teams become more responsive, customer experiences improve, and decisions are made with greater clarity. How do you see real-time analytics reshaping the way your teams work? #BusinessIntelligence #AIAnalytics #DataAnalysis #CustomerExperience

If all you're monitoring is your agent's outputs, you're fighting a losing battle. Beyond even embedding drift, output sensitivity issues, and the petabytes of structured data that can go bad in production, AI systems like agents bring unstructured data into the mix as well — and introduce all sorts of new risks in the process. When documents, web pages, or knowledge base content form the inputs of your system, poor data can quickly cause AI systems to hallucinate, miss key information, or generate inconsistent responses. And that means you need a comprehensive approach to monitoring to resolve it. Issue to consider: - Accuracy: Content is factually correct, and any extracted entities or references are validated. - Completeness: The data provides comprehensive coverage of the topics, entities, and scenarios the AI is expected to handle, where gaps in coverage can lead to “I don’t know” responses or hallucinations. - Consistency: File formats, metadata, and semantic meaning are uniform, reducing the chance of confusion downstream. - Timeliness: Content is fresh and appropriately timestamped to avoid outdated or misleading information. - Validity: Content follows expected structural and linguistic rules; corrupted or malformed data is excluded. - Uniqueness: Redundant or near-duplicate documents are removed to improve retrieval efficiency and avoid answer repetition. - Relevance: Content is directly applicable to the AI use case, filtering out noise that could confuse retrieval-augmented generation (RAG) models. While a lot of these dimensions mirror data quality for structured datasets, semantic consistency (ensuring concepts and terms are used uniformly) and content relevance are uniquely important for unstructured knowledge bases where clear schemas and business rules don't often exist. Of course, knowing when an output is wrong is only 10% of the challenge. The other 90% is knowing why and how it resolve it fast. 1. Detect 2. Triage. 3. Resolve. 4. Measure. Anything less and you aren't AI-ready. #AIreliability #agents

BREAKING: AI agents can now access the live web, unblocked, structured, and at scale. I’m here at AI4 and just witnessed Bright Data launch The Web MCP, a free infrastructure layer that finally solves one of the biggest roadblocks for agentic AI: reliable, real-time web access. Until now, most AI agents struggled when faced with CAPTCHAs, geo-restrictions, and dynamic sites. The Web MCP changes that by giving agents the ability to: • Pull fresh data instantly • Bypass bot defenses and geo-fencing • Automate browser actions on complex sites • Return structured, ready-to-use JSON results It integrates with all major LLMs, supports frameworks like LangChain, LlamaIndex, and CrewAI, and works out-of-the-box for both locally hosted and cloud-based models. Bright Data is making this available with a free tier of 5,000 monthly requests, opening up possibilities for real-time use cases like travel booking, competitor monitoring, healthcare research aggregation, and social sentiment tracking. This launch could be a turning point for building AI agents that truly interact with the live web—without the friction we’ve seen until now. Learn more here: https://lnkd.in/gDDmWA7C #data #ai #publicdata #brightdata #theravitshow

This concept is the reason you can track your Uber ride in real time, detect credit card fraud within milliseconds, and get instant stock price updates.  At the heart of these modern distributed systems is stream processing—a framework built to handle continuous flows of data and process it as it arrives.     Stream processing is a method for analyzing and acting on real-time data streams. Instead of waiting for data to be stored in batches, it processes data as soon as it’s generated making distributed systems faster, more adaptive, and responsive.  Think of it as running analytics on data in motion rather than data at rest.  ► How Does It Work?  Imagine you’re building a system to detect unusual traffic spikes for a ride-sharing app:  1. Ingest Data: Events like user logins, driver locations, and ride requests continuously flow in.   2. Process Events: Real-time rules (e.g., surge pricing triggers) analyze incoming data.   3. React: Notifications or updates are sent instantly—before the data ever lands in storage.  Example Tools:   - Kafka Streams for distributed data pipelines.   - Apache Flink for stateful computations like aggregations or pattern detection.   - Google Cloud Dataflow for real-time streaming analytics on the cloud.  ► Key Applications of Stream Processing  - Fraud Detection: Credit card transactions flagged in milliseconds based on suspicious patterns.   - IoT Monitoring: Sensor data processed continuously for alerts on machinery failures.   - Real-Time Recommendations: E-commerce suggestions based on live customer actions.   - Financial Analytics: Algorithmic trading decisions based on real-time market conditions.   - Log Monitoring: IT systems detecting anomalies and failures as logs stream in.  ► Stream vs. Batch Processing: Why Choose Stream?   - Batch Processing: Processes data in chunks—useful for reporting and historical analysis.   - Stream Processing: Processes data continuously—critical for real-time actions and time-sensitive decisions.  Example:   - Batch: Generating monthly sales reports.   - Stream: Detecting fraud within seconds during an online payment.  ► The Tradeoffs of Real-Time Processing   - Consistency vs. Availability: Real-time systems often prioritize availability and low latency over strict consistency (CAP theorem).  - State Management Challenges: Systems like Flink offer tools for stateful processing, ensuring accurate results despite failures or delays.  - Scaling Complexity: Distributed systems must handle varying loads without sacrificing speed, requiring robust partitioning strategies.  As systems become more interconnected and data-driven, you can no longer afford to wait for insights. Stream processing powers everything from self-driving cars to predictive maintenance turning raw data into action in milliseconds.  It’s all about making smarter decisions in real-time.

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁, 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗼𝘀𝗮𝗯𝗹𝗲 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 As AI systems evolve, managing how agents, tools, and services interact is becoming the foundation of next generation architectures. This is where Model Context Protocol (MCP) changes the game. MCP defines how AI agents connect, retrieve data, and coordinate tasks across systems. The secret to unlocking its full potential lies in choosing the right implementation pattern for your use case. Here are the eight most impactful MCP implementation patterns: 𝟭. 𝗗𝗶𝗿𝗲𝗰𝘁 𝗔𝗣𝗜 𝗪𝗿𝗮𝗽𝗽𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Agents interact directly with APIs through MCP servers to streamline simple tool integrations. Ideal for quick command execution and lightweight orchestration. 𝟮. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝗲𝘀𝘀 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 AI agents access analytical data from OLAP systems via MCP, enabling real-time reporting, predictive modeling, and decision automation. 𝟯. 𝗠𝗖𝗣-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 A primary agent delegates complex operations to a specialist agent using MCP, ensuring optimized reasoning and contextual precision. 𝟰. 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗨𝘀𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Agents fetch dynamic configuration values through MCP managed services, ensuring seamless alignment across environments. 𝟱. 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗠𝗖𝗣 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 MCP servers are structured in layers for large scale ecosystems. Domain level MCPs manage specialized contexts such as payments, wallets, or customer profiles. 𝟲. 𝗟𝗼𝗰𝗮𝗹 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗰𝗰𝗲𝘀𝘀 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 AI agents access and process local files through MCP managed tools, supporting secure document handling and private workflows. 𝟳. 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 MCP servers aggregate multiple tools into a single orchestration layer, allowing agents to execute multi step workflows efficiently. 𝟴. 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Agents respond to streaming data in real time, integrating with asynchronous workflows for high-performance event processing and continuous insights. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 As enterprise AI systems move toward multi-agent orchestration and retrieval augmented intelligence, MCP patterns provide the framework to scale effectively. Choosing the right implementation strategy ensures better performance, modularity, and long term adaptability. Follow Umair Ahmad for more insights #AI #MCP #SystemDesign #EnterpriseArchitecture #LLMOps #IntelligentAgents 

🚀 Big AI updates from Current Bengaluru today! Apache Flink is getting some major upgrades in Confluent Cloud that make real-time AI way easier: 🔹 Run AI models directly in Flink –Bring your model and start making predictions in real time. No need to host externally. 🔹 Search across vector databases – Easily pull in data from places like Pinecone, Weaviate, and Elasticsearch as well as your real-time streams. 🔹 Built-in AI functions – Flink now has built-in tools for forecasting and anomaly detection, so you can spot trends and outliers as the data flows in. Additionally, Tableflow for Iceberg is now GA, and Delta Lake is in early access, making it easier to connect real-time data streams to your AI workflows without managing ETL pipelines. 💡 Why this matters – AI needs fresh, fast data. These updates make it way easier to run models, retrieve data, and build real-time AI apps without stitching together a dozen different tools. Exciting times for AI + streaming! #Current2025 #Confluent #ApacheFlink #AI #RealTimeData #StreamingAI

The biggest lie in tech right now? "Our infrastructure can handle AI workloads." I've spent 3 months deep-diving into 18 companies claiming to be "AI-native." The reality? Brutal. Vector databases running on MySQL. Kubernetes clusters melting under inference loads. Load balancers choking on streaming responses. Here's what I'm seeing across these "AI-ready" companies: One spent $400K on GPU clusters that sit idle 60% of the time because their data pipeline can't feed them fast enough. Another discovered their enterprise API rate limits were designed for humans clicking buttons, not AI models firing 1000 requests per second. A third company's recommendation engine brought down their entire platform when Black Friday traffic hit and their PostgreSQL database couldn't handle 10M embedding lookups simultaneously. The pattern is always the same: impressive demos, catastrophic reality. This isn't just technical debt. It's a fundamental misunderstanding of what AI workloads actually demand. Traditional infrastructure assumes predictable, human-paced interactions. AI doesn't work that way. Models make millions of decisions per second. They need data instantly. They scale in bursts, not gradual curves. They fail in ways monitoring tools have never seen. When your "AI-ready" infrastructure meets real AI workloads, the results are predictable: → Inference requests timing out during user sessions → Training jobs crashing when they hit memory limits designed for batch processing → Feature stores that can't serve embeddings fast enough for real-time recommendations → Security systems that flag every AI decision as anomalous behavior The companies getting this right aren't retrofitting legacy systems with AI lipstick. They're rebuilding everything: → Event-driven architectures that handle AI's asynchronous nature → Vector-native databases that don't translate embeddings through relational layers → Observability systems that can trace AI decision paths, not just system metrics → Auto-scaling that understands model inference patterns, not web traffic patterns But here's the real challenge: it's not just about infrastructure. It's about building teams that think AI-first. Engineers who understand that latency kills AI user experience. DevOps teams that can debug model drift, not just server outages. Product managers who design for AI's probabilistic nature, not deterministic features. Most CTOs are trying to train their existing teams on AI tools. The breakthrough companies are hiring people who already think in AI patterns and building teams around AI-native workflows from day one. This July, I'm hosting a private CTO roundtable in Bengaluru on building AI-first teams for product CTOs. 15 seats. Real playbooks. If you're tired of infrastructure promises that don't survive production — this room is for you. DM me.

As enterprises accelerate their deployment of GenAI agents and applications, data leaders must ensure their data pipelines are ready to meet the demands of real-time AI. When your chatbot needs to provide personalized responses or your recommendation engine needs to adapt to current user behavior, traditional batch processing simply isn't enough. We’re seeing three critical requirements emerge for AI-ready data infrastructure. We call them the 3 Rs: 1️⃣ Real-time: The era of batch processing is ending. When a customer interacts with your AI agent, it needs immediate access to their current context. Knowing what products they browsed six hours ago isn't good enough. AI applications need to understand and respond to customer behavior as it happens. 2️⃣ Reliable: Pipeline reliability has taken on new urgency. While a delayed BI dashboard update might have been inconvenient, AI application downtime directly impacts revenue and customer experience. When your website chatbot can't access customer data, it's not just an engineering problem. It's a business crisis. 3️⃣ Regulatory compliance: AI applications have raised the stakes for data compliance. Your chatbot might be capable of delivering highly personalized recommendations, but what if the customer has opted out of tracking? Privacy regulations aren't just about data collection anymore—they're about how AI systems use that data in real-time. Leading companies are already adapting their data infrastructure to meet these requirements. They're moving beyond traditional ETL to streaming architectures, implementing robust monitoring and failover systems, and building compliance checks directly into their data pipelines. The question for data leaders isn't whether to make these changes, but how quickly they can implement them. As AI becomes central to customer experience, the competitive advantage will go to companies with AI-ready data infrastructure. What challenges are you facing in preparing your data pipelines for AI? Share your experiences in the comments 👇 #DataEngineering #ArtificialIntelligence #DataInfrastructure #Innovation #Tech #RudderStack