# Machine Learning > 70 posts about Machine Learning, summarised, each linking to the original. ## Articles ### [Catwalk: Serving Machine Learning Models at Scale](https://yomu.fyi/post/catwalk-serving-machine-learning-models-at-scale.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Nutdanai Phansooksai - Published: Jul 2, 2019 As machine learning adoption expanded at Grab, individual teams created fragmented model serving solutions that duplicated engineering effort and required data scientists to handle underlying infrastructure. To resolve these inefficiencies, Grab developed Catwalk, a self-service machine learning model serving platform. The system runs TensorFlow Serving containers across a managed Kubernetes cluster integrated with Grab's observability stack. Data scientists deploy or update models simply by saving files using the tf.saved\_model API to dedicated Amazon S3 buckets, while Kubernetes automates orchestration, ingress routing, and pod autoscaling. Catwalk abstracts server management away from data scientists, shortens deployment timelines, and provides high availability during model version rollouts. ### [Guiding You Door-to-Door via Our Superapp!](https://yomu.fyi/post/guiding-you-door-to-door-via-our-superapp.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Neeraj Mishra - Published: Apr 12, 2019 Grab addressed passenger navigation challenges at large Southeast Asian venues such as airports and shopping centers. Satellite signals weaken through concrete and steel, creating GPS inaccuracies that caused the rendezvous distance between passengers and drivers at large venues to exceed twice the average. While introducing Entrances previously mapped over 120,000 green dots to lower rendezvous distances, passengers still struggled to locate specific pickup spots indoors. The company launched Venues, an in-app feature delivering turn-by-turn text and photo directions to designated pickup points. To support this system, operations teams surveyed sites with cameras and scanners to capture landmarks, after which in-house teams masked faces and vehicle license plates. ### [How We Harnessed the Wisdom of Crowds to Improve Restaurant Location Accuracy](https://yomu.fyi/post/how-we-harnessed-the-wisdom-of-crowds-to-improve-restaurant-location-a.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Pravin Kakar - Published: Apr 2, 2019 Grab discovered that abnormally short driver wait times often indicated restaurants registered at incorrect coordinates due to moves or onboarding errors. To fix this, Grab used driver-partner GPS pings, timestamps, and order status updates to infer true food collection locations. The system cleans the data by filtering low-quality GPS pings and isolating the longest temporal streak a driver spends within a predefined radius of the venue. Clusters of inferred pick-up points are then ranked by order volume, the proportion of off-target pick-ups, and median distance errors before routing to mapping operations for verification. This periodic correction workflow achieved a fivefold reduction in order cancellations caused by unfound merchant locations. ### [Recipe for Building a Widget: How We Helped to “Peak-Shift” Demand by Helping Passengers Understand Travel Trends](https://yomu.fyi/post/recipe-for-building-a-widget-how-we-helped-to-peak-shift-demand-by-hel.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Lara PuReum Yim - Published: Mar 7, 2019 Transport demand spikes during regular commuting hours often outpace driver availability, resulting in passenger wait times and fare surges. To mitigate these imbalances, Grab created the Travel Trends Widget for its mobile feed to redistribute ride requests toward off-peak windows. The widget uses machine learning forecasting to present historical supply-demand patterns alongside pricing trends for the upcoming two hours. To handle anticipated high query rates across millions of database entries, engineers periodically load precomputed trend data into an in-memory data structure rather than querying the database per request. The feature rolled out to feeds in Singapore and Jakarta within four weeks of initial development. ### [Understanding Supply & Demand in Ride-hailing Through the Lens of Data](https://yomu.fyi/post/understanding-supply-demand-in-ride-hailing-through-the-lens-of-data.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Aayush Garg - Published: Feb 20, 2019 Grab measures ride-hailing supply and demand across space and time to resolve geo-temporal allocation mismatches between moving drivers and ride-seeking passengers. The analytics pipeline defines supply as idle online drivers and demand as passengers checking fares within brief time slots, aggregating locations into geohashes. Each driver is mapped across neighbouring demand units and inversely weighted by straight-line distance, which yields the effective supply, supply-demand ratio, and supply-demand difference for each geographic polygon. Grab uses these aggregated metrics to identify marketplace imbalances, deploying driver heatmaps to shift excess supply and passenger travel trend widgets to defer time-insensitive ride requests. ### [Grab Senior Data Scientist Liuqin Yang Wins Beale-Orchard-Hays Prize](https://yomu.fyi/post/grab-senior-data-scientist-liuqin-yang-wins-beale-orchard-hays-prize.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Yang Liuqin - Published: Jul 20, 2018 Grab Senior Data Scientist Dr. Liuqin Yang, Professor Defeng Sun, and Professor Kim-Chuan Toh received the 2018 Beale-Orchard-Hays Prize for their research paper introducing SDPNAL+. The software employs a majorised semismooth Newton-CG augmented Lagrangian method to solve large-scale semidefinite programming problems with nonnegative constraints. While traditional methods struggled beyond matrix dimensions of 2,000 and 5,000 constraints, SDPNAL+ successfully scales to matrix dimensions of 9,261 and over 12 million constraints. In benchmark testing, the software solved a problem on a desktop PC in 1.5 hours that required 122 hours on a 56-core CPU and 128-GPU cluster using a traditional solver. Grab implements these optimisation techniques to accelerate its passenger-driver allocation algorithms by hundreds of times. ### [The Data and Science Behind GrabShare Part I: Verifying Potential and Developing the Algorithm](https://yomu.fyi/post/the-data-and-science-behind-grabshare-part-i-verifying-potential-and-d.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Tang Muchen - Published: Oct 20, 2017 Expanding from point-to-point dispatch services to dynamic carpooling requires matching independent passenger requests traveling in similar directions without causing unacceptable delays. Grab evaluated the feasibility of its GrabShare service by analyzing historical trip data with DBSCAN clustering on coordinates projected into a Universal Transverse Mercator system. This analysis demonstrated that 35% to 46% of rides across typical daytime windows fell into tight geographic clusters with near-identical pickup and drop-off coordinates. The resulting matching framework adapts the baseline dispatch flow by searching for in-transit drivers and enforcing real-time seat reservation constraints. Route assignment decisions subsequently evaluate detour times, trip angles, and expected arrival times to ensure driver utilization improves while total driving distance decreases. ### [How to Go from a Quick Idea to an Essential Feature in Four Steps](https://yomu.fyi/post/how-to-go-from-a-quick-idea-to-an-essential-feature-in-four-steps.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Da Huang - Published: May 16, 2017 Grab engineered an in-app messaging platform, GrabChat, to help drivers and passengers coordinate pickups across Southeast Asian markets characterized by weak 2G connectivity and high packet loss. The team developed an in-house TCP messaging architecture consisting of a TCP gateway named Gundam and a message dispatcher named Hermes connected to internal backend services over HTTPS. To protect backend server resources from resend loops during poor connection states, the communication protocol adopts a "server only push once" model that delegates retry handling to the client. Data science evaluations using a pre-trained cancellation prediction model confirmed that GrabChat adoption correlated with reduced booking cancellations. Following early usage feedback, the team further iterated on the feature by introducing pre-written message templates to reduce driver distraction on the road. ### [Battling with Tech Giants for the World's Best Talent](https://yomu.fyi/post/battling-with-tech-giants-for-the-world-s-best-talent.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Grab Engineering - Published: Jan 18, 2017 Grab maintains research and development centres across Singapore, Seattle, and Beijing to develop mobility and transaction platforms for Southeast Asia. Engineer Brandon Gao turned down an offer from a major Seattle-based technology corporation to remain at the company due to rapid growth and high engineering impact. During backend infrastructure changes starting in May 2015, Grab transitioned core services from Node.js and Ruby to Golang. A weekend Golang prototype created by Gao evolved into the primary data service linking backend servers to 580,000 drivers across the region. Additionally, his initial work on the User Trust team established a machine-learning risk and fraud detection system supporting large-scale mobile transaction volumes. ### [GrabPay Wins Best Fraud Prevention Innovation at the Florin Awards](https://yomu.fyi/post/grabpay-wins-best-fraud-prevention-innovation-at-the-florin-awards.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Foo Wui Ngiap - Published: Oct 12, 2016 Grab won the Best Fraud Prevention Innovation (Community Votes) Award at the 2016 Florin Awards for its GrabPay platform. To support secure cashless transactions across Southeast Asia, Grab deployed a dedicated risk and fraud detection engine when launching GrabPay earlier in the year. The system utilizes machine learning algorithms that continually evolve by analyzing driver, passenger, and travel pattern data across massive transaction volumes. Furthermore, Grab provides full financial protection by covering any unauthorized fraudulent transactions for both passengers and drivers. At the time of the announcement, the platform supported up to 1.5 million daily bookings and exceeded 23 million app downloads. [Newer posts](https://yomu.fyi/topic/machine-learning/page/3.md)