Scalability of Shipping Models

Real-Time ML Deployment is not just code - it is coordination. Here is how top teams ship models to production reliably and at scale: 𝟏. 𝐕𝐞𝐫𝐬𝐢𝐨𝐧𝐞𝐝 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬   Code-first pipelines trigger on merge. Every run is traceable. 𝟐. 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠   Features come from a Feature Store, validated, and logged via Experiment Tracking. 𝟑. 𝐌𝐨𝐝𝐞𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 & 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧   Models are trained, evaluated, and linked with metadata. 𝟒. 𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐲 + 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧   Validated models go to the Model Registry and are wrapped as REST/gRPC APIs. 𝟓. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞   Webhooks trigger deployments. Strategies like canary or blue-green ensure safe rollout. 𝟔. 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞   Apps call ML APIs that fetch real-time features and return predictions fast. Load balancing ensures scale. 𝟕. 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐌𝐋 𝐀𝐏𝐈𝐬   Expose specialized APIs per use case - ranking, recommendations, etc. 𝟖. 𝐇𝐲𝐛𝐫𝐢𝐝 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥   Static from the Data Warehouse. Dynamic from Kafka streams. 𝟗. 𝐑𝐞𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧   Scheduled or triggered based on drift or performance. 𝟏𝟎. 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠   Always-on monitoring. Auto-retraining if models degrade. 𝐊𝐞𝐲 𝐓𝐢𝐩: Low latency is a must. Cache smartly. Test thoroughly. Plan for dynamic feature complexity. Is your ML stack ready for real-time? Let’s talk. #MLOps #MachineLearning #RealTimeInference #ModelDeployment #DataEngineering #AIinProduction

Cursor is serving billions of code completions a day. 1 million queries per second. And the impressive part isn’t the model, it’s the plumbing. Autocomplete is easy to demo, hard to deliver. Cursor has to: Capture local context instantly Encrypt + transmit + infer + return a completion Do it all in under 1 second At peak scale Without storing user code That’s not “running a model.” That’s running a distributed system with strict latency guarantees, lossy input, and bursty, global traffic. I’ve seen this kind of pressure break production systems before. Not at the fancy edge. Not in the model layer. But in the middle…the message queues, the JSON serializers, the retry loops that quietly balloon from 100ms to 2s because one lambda took a nap. At 1M QPS, the thing that breaks is always the thing you forgot to think about. If you haven’t scaled a system like this, you might assume the bottleneck is inference. It’s not. It’s whatever you assumed would “just work.” This is the part that deserves more attention. Because shipping GenAI features isn’t hard. Keeping them up is where the engineering starts. Really enjoyed the breakdown from ByteByteGo https://lnkd.in/e94T6ySy #SystemDesign #GenAI #LatencyEngineering #DistributedSystems

🚀 𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗠𝗟/𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗟𝗲𝗮𝗿𝗻 𝗗𝗼𝗰𝗸𝗲𝗿 Deploying ML and AI models sounds simple… until you actually try it. What works perfectly on your laptop suddenly crashes in production. 𝗪𝗵𝘆? Because your server is speaking a different language. 📌 On your laptop → scikit-learn 1.4 📌 On the server → scikit-learn 1.2 📌 Your code → expects functions that only exist in 1.3+ Result? A nightmare of broken dependencies and late-night debugging. This is where Docker changes the game. 🐳 𝗧𝗵𝗶𝗻𝗸 𝗼𝗳 𝗗𝗼𝗰𝗸𝗲𝗿 𝗮𝘀 𝗮 "𝘁𝗶𝗺𝗲 𝗰𝗮𝗽𝘀𝘂𝗹𝗲" It freezes your entire environment (libraries, versions, OS dependencies) into a portable box. Wherever you run that box — AWS, GCP, a colleague’s laptop, or a production cluster — your model behaves exactly the same. No more “but it worked on my machine.” 🔑 𝗪𝗵𝘆 𝗠𝗟/𝗔𝗜 𝘁𝗲𝗮𝗺𝘀 𝗻𝗲𝘃𝗲𝗿 𝗻𝗲𝗴𝗹𝗲𝗰𝘁 𝗗𝗼𝗰𝗸𝗲𝗿 • 𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Every experiment, every model, same environment → same results. • 𝗣𝗼𝗿𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Build once, deploy anywhere. • 𝗦𝗽𝗲𝗲𝗱: Skip the “setup hell” and focus on shipping. • 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Containers isolate apps, reducing risk of leaks or conflicts. • 𝗦𝗰𝗮𝗹𝗲: Works seamlessly with Kubernetes for serving models at enterprise scale. 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Don’t stop at containerizing your model. Wrap the entire inference pipeline — preprocessing, model, postprocessing — so you can ship end-to-end reproducible systems. 👉 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗽𝗮𝗶𝗻 𝘄𝗵𝗲𝗻 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹𝘀 — 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗶𝘀𝘀𝘂𝗲𝘀, 𝘀𝗰𝗮𝗹𝗶𝗻𝗴, 𝗼𝗿 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴? Drop your thoughts below 👇 ♻️ Share if this resonates ➕ Follow Mouhssine AKKOUH for more real-world ML engineering tips #MLOps #AIEngineering #Docker #MachineLearning #AI #DataScience #MLDeployment #AIAgents