--- title: "How Lyft Runs Real-Time ML" newsletter: "MLOps Community" date: 2025-07-31 source: https://aaif.live/newsletters/mlopscommunity/2025-07-31-how-lyft-runs-real-time-ml --- # How Lyft Runs Real-Time ML *Plus, scaling LLM adoption, automating knowledge graph creation, Hidden Gems, and ML Confessions.* *MLOps Community — Agentic AI Foundation, 2025-07-31* I know you’ve come up with more inventive ones than this [https://go.mlops.community/intro31jul] during production. Hit reply and tell me what they were. ## Real-time Feature Generation at Lyft Part of the problem with real-time is no one agrees what it means – until it breaks. Then everyone agrees it feels like forever. At Lyft, real-time ML powers critical systems like surge pricing and demand forecasting. Rakesh explained how they moved from cron jobs to Apache Beam on Flink, cutting latency and enabling consistent one-minute feature updates. Geohash-based routing solved early hot shard issues and shaped their internal geospatial feature store: * Features are stored hierarchically (GH6 to GH4), so models can request region- or block-level data from a single source * A shared API supports different aggregation levels, improving reuse and simplifying scale across models There might be debate on what real-time means, but no debate this episode’s a good time. Video [https://go.mlops.community/prk31jul] || Spotify [https://go.mlops.community/srk31kul] || Apple [https://go.mlops.community/ark31jul] [https://go.mlops.community/ark31jul](https://go.mlops.community/ark31jul) [https://go.mlops.community/prk31jul](https://go.mlops.community/prk31jul) [https://go.mlops.community/srk31kul](https://go.mlops.community/srk31kul) ## PRESENTED BY DATABRICKS ## Don’t Miss Out: Databricks DevConnect Hits Chicago August 19! https://go.mlops.community/db31jul Get ready, data engineers! Databricks DevConnect is coming to Chicago on August 19, and you’re invited to join an evening packed with exciting ideas, hands-on learning, and incredible connections. This is your chance to: * Dive into the newest Databricks breakthroughs and AI-powered tools * Discover pro tips for integrating data and mastering governance * Meet Databricks engineers, product managers, developer advocates, and MVPs - plus passionate data pros just like you Connect with Databricks engineers, product managers, and peers in data at this high-energy meetup. If you’re looking to level up your skills and meet others driving the future of data, DevConnect Chicago is the place to be. Don’t let this opportunity pass you by - register now and save your spot [https://go.mlops.community/db31jul]! [register now and save your spot](https://go.mlops.community/db31jul) ## Hidden Gems ## Enterprise AI Adoption Challenges You’d think we’d have figured out tokens by now. Unfortunately, this episode doesn't help with that, but it does share an approach to driving LLM adoption across a 100-company portfolio. Toqan's team explained how they’re scaling by balancing advanced features for power users with intuitive entry points for everyone else. The real challenge isn’t technical. It’s getting people to trust and actually use the tools. One tactic that’s helped build confidence is steering new users toward tasks that reliably succeed: * Prompted onboarding flows guide people to summarize docs or translate text, rather than just “ask anything” * Live demos and walkthroughs show how real teams are using Toqan to get work done It's a great episode, so clicking below to listen isn't a token gesture. Video [https://go.mlops.community/ppvdb31jul] || Spotify [https://go.mlops.community/spvdb31jul] || Apple [https://go.mlops.community/apvdb31jul] [https://go.mlops.community/apvdb31jul](https://go.mlops.community/apvdb31jul) [https://go.mlops.community/ppvdb31jul](https://go.mlops.community/ppvdb31jul) [https://go.mlops.community/spvdb31jul](https://go.mlops.community/spvdb31jul) ## Job of the Week [https://go.mlops.community/jobrow31jul](https://go.mlops.community/jobrow31jul) ## Automating Knowledge Graph Creation with Gemini and ApertureDB - Part 1 Cmd+F helped you find a needle in your haystack, but AI’s now helping you pull out all the needles, screws, and paperclips, label each one properly, and store them neatly in drawers. This tutorial covers how to extract and store structured entities from long documents using Gemini 2.5 Flash, LangChain, and ApertureDB. After defining entity classes and properties with Gemini, specific instances are pulled from a 42-page PDF in parallel, deduplicated, and inserted into ApertureDB. Extraction is tightly structured using templated prompts and Pydantic schemas, including: * Class schema generation: Gemini maps general concepts like “Computing System” to relevant properties such as “definition” and “use cases”. * Efficient scaling: LangChain processes document chunks concurrently, then merges and cleans results before insertion. You won't need Cmd+F to find the blog, just click below to read. Read it here [https://go.mlops.community/blog31jul] [Read it here](https://go.mlops.community/blog31jul) [https://go.mlops.community/MLConfess](https://go.mlops.community/MLConfess) Real-time ML is the new microservices: makes sense at scale, but most teams cargo cult their way into a mess they can’t debug. Batch mindset or streaming mindset? Working on something tricky or planning ahead? Here’s how we can help - just hit reply: * Custom workshops tailored to your company’s needs * Hiring? I know some quality folks looking for a new adventure * Want to connect with someone tackling similar problems? I can introduce you Thanks for reading, catch you next time! --- Source: https://aaif.live/newsletters/mlopscommunity/2025-07-31-how-lyft-runs-real-time-ml