Challenges in Large-Scale Productions

Challenges faced in LLM Deployments in Enterprise Environments. As enterprises increasingly adopt large language models (LLMs) to transform workflows, the transition from prototypes to production environments reveals critical architectural challenges. One recurring issue? API rate limits. While small-scale systems handle dozens of users seamlessly, scaling to serve 50,000+ employees often triggers cascading 429 errors during peak usage. This isn’t just a technical hiccup, it’s a systemic challenge that requires rethinking architecture to ensure reliability and performance at scale. The solution lies in distributed architecture patterns: Intelligent load balancing across geographically dispersed API endpoints (e.g., US-East, EU-West, Asia-Pacific). Circuit breaker mechanisms to reroute traffic during regional throttling events. Real-time monitoring dashboards to track RPM utilization while adhering to data residency mandates. Beyond the technical complexities, there’s also a financial dimension. Token-based pricing models often force enterprises to maintain 3-5x capacity buffers to avoid service degradation during spikes, a costly yet necessary trade-off for reliability. Scaling LLMs is not just about adding capacity; it’s about building resilient systems that anticipate demand surges. AI gateways with predictive auto-scaling algorithms, leveraging historical traffic patterns, calendar events, and real-time queue depths, are key to staying ahead of the curve. Solving these issues requires not just technical expertise but also a shared commitment to innovation and operational excellence. For those working on similar challenges, I’d love to hear how you’re addressing scalability in your LLM deployments! Let’s keep the conversation going. #AI #ArtificialIntelligence #Innovation #Technology #FutureOfWork #DigitalTransformation #CloudComputing #EnterpriseArchitecture #Scalability #APIDevelopment

Why Processes That Work in the Lab Often 𝐅𝐚𝐢𝐥 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞 One of the most persistent challenges in chemical engineering is the transition from lab-scale processes to full commercial production. A system that performs flawlessly in a 5-liter reactor may behave unpredictably in a 5,000-liter vessel. This shift—from controlled experiments to industrial volumes—brings significant risks, including cost overruns, quality issues, and safety concerns. 𝐊𝐞𝐲 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐢𝐧 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐒𝐜𝐚𝐥𝐞-𝐔𝐩: 🔹Heat Transfer Limitations Larger reactors exhibit a lower surface area-to-volume ratio, which can hinder efficient heat removal and increase the risk of hotspots. 🔹Non-Uniform Mixing What mixes well at lab scale may lead to distinct mixing zones at scale, adversely affecting reaction rates and outcomes. 🔹Residence Time Distribution Flow behaviour changes with scale, impacting conversion rates and product consistency. 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡-𝐁𝐚𝐜𝐤𝐞𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐭𝐨 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐒𝐜𝐚𝐥𝐞-𝐔𝐩 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬: 🔹Advanced CFD Modelling Computational fluid dynamics simulations can predict flow, temperature, and mixing behaviour, allowing engineers to proactively address potential issues before they arise in production. 🔹Rethinking Scale-Up Rules Traditional guidelines (such as constant tip speed or power per volume) may not apply effectively for complex reactions and should be adapted on a case-by-case basis. 🔹Targeted Instrumentation Strategically placing sensors at CFD-identified critical points can significantly enhance real-time control and process stability. Chemical process scale-up is rarely a linear task. However, with the right combination of modeling, monitoring, and engineering judgment, it can become a manageable challenge. What obstacles have you encountered when scaling complex reactions? I’d love to hear your experiences! #ChemicalScaleUp #ProcessEngineering #PolymerManufacturing #Ingenero

Building the best AI model is only half the battle, it’s useless if it’s not usable… the real challenge is scaling it for production. Developing a cutting-edge model in the lab is exciting, but the true value of AI lies in deployment. Can your model handle the real-world pressures of scalability, latency, and reliability? 👉 How do you handle model drift when production data doesn’t match training data? Continuous monitoring with techniques like concept drift detection is crucial. 👉 Are you optimizing your inference time? Deploying large models efficiently requires leveraging techniques like quantization and model pruning to reduce size without sacrificing accuracy. 👉 Is your model robust to edge cases and unexpected inputs? Adversarial testing and uncertainty quantification ensure your AI performs reliably under a wide range of scenarios. Modeling isn’t just about accuracy, it’s about deployment, monitoring, and scaling. The difference between a good model and a great one is whether it delivers value consistently in production. What strategies are you using to ensure your models thrive in production? Let’s dig into the details👇 #AI #MachineLearning #ModelDeployment #Scalability #ModelDrift #ProductionAI #Optimization

Training large-scale models—particularly LLMs with hundreds of billions or even trillions of parameters—poses unique system-level challenges. Memory limits, communication bottlenecks, and uneven compute loads can quickly bring naïve training strategies to a halt. Relying on just one form of parallelism (e.g., data parallelism alone) simply doesn’t scale effectively. Instead, modern deep learning frameworks and teams combine multiple forms of parallelism to stretch hardware capabilities to their limits. Each strategy addresses a different bottleneck: ➜ Data parallelism boosts throughput by replicating the model across nodes. ➜ Tensor/model parallelism breaks up massive weight matrices. ➜ Pipeline parallelism improves utilization across deep architectures. ➜ Expert parallelism adds sparsity and dynamic routing for efficiency. ➜ ZeRO optimizes memory allocation down to optimizer states and gradients. ➜ Context parallelism (a newer strategy) allows for splitting long sequences—critical for LLMs handling multi-thousand-token contexts. This modular, composable approach is the backbone of training breakthroughs seen in models like GPT-4, PaLM, and beyond. Link to the article: https://lnkd.in/gZBF-N2w

What are the Key Challenges in Deploying Agents in Production 🔧 Technical & Integration Challenges Enterprise connectivity: Agents must interface securely and reliably with existing systems—CRMs, ERPs, databases, internal APIs—which is far from trivial. Workflow entanglement: Fitting an AI agent into live business processes often requires extensive process re-engineering rather than simple plug-and-play. Framework churn: The rapid pace of AI tooling leads to instability—teams end up chasing new frameworks instead of building stable foundations. 📈 Quality & Performance Challenges Chasing quality: Defining and assuring “quality” in generative contexts requires continuous, often heavy, monitoring and tuning. Unpredictability: Non‑deterministic behavior of AI agents makes them unreliable for mission-critical or compliance-sensitive tasks. 🛡️ Risk & Governance Challenges Security & privacy: Agents require access to sensitive data, raising risks around breaches or misuse—robust safeguards are essential. Compliance burden: As governments and regulators tighten AI rules, maintaining compliance becomes a moving target needing dedicated oversight. ⚙️ Operational & Strategic Challenges Agent Ops capability: Organizations must build new roles and skill sets—Agent Ops—for monitoring, debugging, and managing live agents. Cost control: High compute, development, and maintenance costs can erode ROI unless tightly managed. Open source vs proprietary: Choosing between open‑source flexibility and commercial reliability impacts cost, control, and long‑term viability. 🏁 Conclusion Launching an AI agent in production isn’t just a technical sprint—it’s a strategic marathon. Impact comes only when you: 1. Integrate deeply with robust architecture 2. Ensure quality and reliability through monitoring and tuning 3. Maintain governance amid evolving regulations 4. Build Agent Ops teams 5. Align costs and strategy clearly Only organizations that adopt this disciplined, end-to-end, “production-first” mindset—focusing on integration, governance, operations, and economics—can convert prototypes into high-impact, real-world AI assets. read my detailed blog on this here https://lnkd.in/gnQKRe8m

Lakehouse Challenges in Production ⛔️ The way we have presented the Lakehouse architecture sounds very simple. - Pick an open table format like Apache Iceberg, Hudi, Delta. - Hook it up to your favourite compute engines/catalogs. - Now your data is open. Your tools are interoperable (even with different vendors) It all looks great from one perspective! But the one thing we don’t talk about enough is what happens after. When these table formats land in production, when tables grow to TBs/PBs, pipelines run 24/7 & SLAs become real - that’s when the journey starts. Thats's when Engineers really need to be on the front-line. Truth is the real complexity isn’t in the "table format". Table formats are fundamentally just metadata. They track files, versions, commits, statistics. But metadata doesn’t manage itself. ⛔️ They don’t clean up your snapshots. ⛔️ They don’t compact your small files. ⛔️ They don’t automatically cluster or sort your data for efficient queries. It’s the table optimization services that determine whether your lakehouse can scale and stay healthy in production. The hard questions come later: ✅ How do we make sure snapshots are expired regularly without impacting ongoing writes? ✅ How do we balance compaction jobs with streaming ingestion? ✅ Can we cluster or sort data while ingestion is still running? ✅ What happens when a job fails mid-write - who cleans up the leftover files? These are the problems that surface when lakehouses move beyond POCs. As engineers and advocates, we need to shift some focus away from just the high-level architecture and start calling attention to non-trivial stuff that keep things running. It’s about understanding what it actually takes to run these systems reliably and making sure that part of the conversation gets the spotlight too. There are some directions to think about: - Knowing your workloads pattern & selective maintenance - Automatic table maintenance jobs (less engineer intervention) - Better knobs & metrics from vendor tools (We need better observability) - Design-time decisions (CoW vs MoR, file size tuning, etc.) #dataengineering #softwareengineering

11% of worldwide major projects face delay or cancellation, Mace report finds A new global analysis reveals that over one in 10 large-scale infrastructure projects face significant delays or risk cancellation, posing a substantial threat to economic growth worldwide. The world of major programme delivery is entering what construction giant Mace describes as an era of “unprecedented investment, unmatched scale and unique complexity”. The firm has undertaken a study, The Future of Major Programme Delivery, examining more than 5,000 megaprojects and giga-projects (valued at over $1bn and over $10bn respectively) and found that 11% of these endeavours are at risk of falling behind schedule or being scrapped altogether. The cumulative effect of such inefficiencies could cost the global economy upwards of $1.5 trillion (£1.1 trillion) in missed growth opportunities by 2030. Despite record levels of investment in infrastructure, the report highlights persistent challenges in delivering projects on time and within budget, while also realising promised economic and societal benefits. The analysis also reveals a sharp rise in the number of major programmes, with a 280% surge since 2010 amid growing demands driven by urbanisation and climate change. The United States leads with 1,663 projects announced over the past decade, followed by India (729), Saudi Arabia (577) and the United Kingdom (484). Collectively, the world’s top 10 ongoing mega and giga projects are valued at nearly $700bn (£513bn). Growth in project size and complexity, however, increases vulnerability to external shocks such as political upheaval, price volatility and global conflict, further complicating delivery. With over 11,000 mega-projects and 250 giga-projects currently underway worldwide, the report calls for a stronger focus on the tangible value these projects can bring, rather than solely on the complexities of their delivery. The coming decade’s infrastructure endeavours will be critical in shaping economic and social outcomes amid uncertain geopolitical and environmental landscapes. Mace Consult CEO Davendra Dabasia said: “When large-scale programmes are significantly delayed and go over budget, the focus on the positive impact they have is diluted. When major programmes exist to deliver beneficial outcomes for society, it’s a factor that needs to be addressed. “Many of the issues are systemic, often driven by national politics and policies, and reflect the challenging ecosystem that delivery takes place in. As major projects and programmes become larger and more complex, delivery models need to be agile to tackle challenges and capitalise on any new opportunities." “The solution must rest with more collaborative delivery approaches that prioritise the creation of integrated teams aligned to common goals that seek the same positive outcomes.” www.newcivilengineer.com

Data migration is often one of the most complex and underestimated aspects of large-scale cloud transformation programs—especially when transitioning from multiple legacy systems to a modern cloud-based ERP. Legacy landscapes, shaped by years of growth, mergers, and acquisitions, add layers of inconsistency and fragmentation. The challenges are compounded not only by technical intricacies such as data quality, mapping, and integration, but also by organizational constraints, including limited resources, competing priorities, and lack of specialized expertise.

I posted a while back about the tension between market pressure to deploy AI and practical challenges for enterprises of implementing production-grade solutions. At Data Day Texas a few weeks I got to see a great example of how impactful it can be when done right. Arthur Delaitre shared how he and the team at Mirakl have deployed a combo of frontier models and fine-tuned small models for optimal output quality and operational cost. Some background, Mirakl helps retailers list their products on various marketplaces. This is a massive data mapping problem. The retailer has their product catalogue data model which will not map 1-1 to the marketplaces’ models. Challenges include values which may be included in instructured text at source which need to be mapped to structured fields. This needs to be completed across hundreds of product categories and millions of individual products. Highlights:  -30x operational cost savings using fine-tuned smaller models in majority of production cases  -Frontier models used to power multi-step training of smaller models -Edge cases in production, e.g. less common languages, covered by frontier models Check out a few of his slides in the images. Exciting to see AI having a material impact in production! #ai #genai #finetuning #llm #frontiermodel #openai #llama #mistral

𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗼𝗳 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗶𝗻𝗴 𝗮 𝗡𝗲𝘄 𝗠𝗶𝗻𝗲 𝘃𝘀 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗮𝗻 𝗘𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Having led 𝗺𝗮𝗷𝗼𝗿 𝗺𝗶𝗻𝗶𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗿𝗼𝗺 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝘁𝗼 𝗳𝘂𝗹𝗹-𝘀𝗰𝗮𝗹𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻, I have seen firsthand that 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗮 𝗻𝗲𝘄 𝗺𝗶𝗻𝗲 𝗶𝘀 𝗳𝗮𝗿 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 than stepping into an existing operation. Both require strong leadership, operational expertise, and risk management. The sheer scale of challenges in greenfield development sets them apart. 𝟭️. 𝗙𝗿𝗼𝗺 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 𝘁𝗼 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: 𝗧𝗵𝗲 𝗛𝗶𝗴𝗵-𝗦𝘁𝗮𝗸𝗲𝘀 𝗣𝗵𝗮𝘀𝗲 Unlike an existing mine with established infrastructure, a new project starts with 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗯𝘂𝘁 𝗮 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗮𝗻𝗱 𝗮 𝘃𝗶𝘀𝗶𝗼𝗻. The journey from feasibility studies to production demands ✅ 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗮𝗽𝗽𝗿𝗼𝘃𝗮𝗹𝘀 that vary across jurisdictions ✅ 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁, including community and government ✅ 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲—roads, rail, power, and water—often from scratch Every decision at this stage determines the project’s long-term viability. 𝗔 𝗺𝗶𝘀𝘀𝘁𝗲𝗽 𝗵𝗲𝗿𝗲 𝗰𝗮𝗻 𝗰𝗼𝘀𝘁 𝗺𝗶𝗹𝗹𝗶𝗼𝗻𝘀—𝗼𝗿 𝗲𝘃𝗲𝗻 𝗱𝗲𝗿𝗮𝗶𝗹 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 project. 𝟮️. 𝗖𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 & 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗨𝗻𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲 𝗕𝗿𝗶𝗻𝗴𝗶𝗻𝗴 𝗮 𝗺𝗶𝗻𝗲 𝗼𝗻𝗹𝗶𝗻𝗲 𝗺𝗲𝗮𝗻𝘀 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗱𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗼𝗻𝘀, 𝗹𝗮𝗯𝗼𝗿 𝘀𝗵𝗼𝗿𝘁𝗮𝗴𝗲𝘀, 𝗮𝗻𝗱 𝘄𝗲𝗮𝘁𝗵𝗲𝗿 𝗱𝗲𝗹𝗮𝘆𝘀—all while keeping schedules and budgets in check. Having worked on large projects, I have seen how 𝗲𝘃𝗲𝗻 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁-𝗹𝗮𝗶𝗱 𝗽𝗹𝗮𝗻𝘀 𝗳𝗮𝗰𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 🔹 𝗗𝗲𝗹𝗮𝘆𝘀 𝗶𝗻 𝗮𝗽𝗽𝗿𝗼𝘃𝗮𝗹𝘀 can stall progress 🔹 𝗖𝗼𝘀𝘁 𝗼𝘃𝗲𝗿𝗿𝘂𝗻𝘀 due to inflation, logistics, design changes 🔹 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝗺𝗼𝗯𝗶𝗹𝗶s𝗮𝘁𝗶𝗼𝗻 in remote locations with limited skilled labour A leader’s ability to 𝗮𝗱𝗮𝗽𝘁, 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗲 𝗮𝗻𝗱 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝗺𝗼𝗺𝗲𝗻𝘁𝘂𝗺 determines whether the project succeeds or fails. 𝟯️. 𝗧𝗵𝗲 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝗮𝗻 𝗘𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 By contrast, stepping into a producing mine—while still demanding—𝗰𝗼𝗺𝗲𝘀 𝘄𝗶𝘁𝗵 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, 𝘄𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲, 𝗮𝗻𝗱 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. The focus shifts to 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻, 𝗰𝗼𝘀𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁, rather than the 𝗺𝗼𝗻𝘂𝗺𝗲𝗻𝘁𝗮𝗹 𝘁𝗮𝘀𝗸 𝗼𝗳 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘇𝗲𝗿𝗼. Having successfully led 𝗴𝗿𝗲𝗲𝗻𝗳𝗶𝗲𝗹𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝘁𝘂𝗿𝗻𝗮𝗿𝗼𝘂𝗻𝗱 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, I know that 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗺𝗶𝗻𝗲 𝗶𝘀 much more difficult and requires a different skillset - 𝗴𝗮𝗶𝗻ed 𝗳𝗿𝗼𝗺 𝗽𝗿𝗲𝘃𝗶𝗼𝘂𝘀 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲?