# Machine Learning > 67 posts about Machine Learning, summarised, each linking to the original. ## Articles ### [Chimera Sandbox: A scalable experimentation and development platform for Notebook services](https://yomu.fyi/post/chimera-sandbox-a-scalable-experimentation-and-development-platform-fo.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Arkar Min Aung - Published: Aug 27, 2024 Grab's Chimera team built Chimera Sandbox to address resource accessibility challenges and enable rapid machine learning experimentation across technical and non-technical teams. The platform runs on multi-cluster Kubernetes infrastructure capable of scaling to thousands of nodes per cluster during peak loads. Users can provision customizable Docker environments with varying CPU and GPU configurations, collaborate in real time, and query data using Spark-on-Kubernetes integrations and helper magic cells. The workspace integrates with Grab's AI Gateway for LLM access alongside tools like MLflow, Optuna, RStudio, and LabelStudio on shared storage, backed by user-level cost attribution. ### [How we improved translation experience with cost efficiency](https://yomu.fyi/post/how-we-improved-translation-experience-with-cost-efficiency.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Jie Zhang - Published: Aug 5, 2024 Grab faced translation quality and cost challenges in its booking chat system when tourism resumed after COVID restrictions eased. Device language settings proved unreliable for determining message language, prompting the team to use Lingua alongside booking context heuristics and third-party fallbacks for real-time detection. To replace expensive and inaccurate general translation APIs, Grab distilled a large open-source Hugging Face model into lightweight, language-specific models trained on ten million synthetic chat examples. A post-translation validation step verifies non-translatable entities such as numbers and emojis before falling back to external services if mutations occur. Caching layers were also introduced across translation paths to curb redundant on-the-fly execution. ### [Managing dynamic marketplace content at scale: Grab's approach to content moderation](https://yomu.fyi/post/managing-dynamic-marketplace-content-at-scale-grab-s-approach-to-conte.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Poonam Gambhire - Published: Feb 1, 2024 Grab manages content moderation across GrabFood and GrabMart, where merchants update over 100,000 item listings daily across eight countries. The platform must adhere to internal rules, local government regulations, and external platform guidelines across these markets. To handle scale and regional nuance, Grab uses an in-house automated system powered by algorithms and machine learning to scan daily listings for violations. Items flagged as requiring subjective judgment or cultural awareness are routed to human moderators for manual review. Additionally, Grab collaborates with Google to maintain compliance with Play Store policies. ### [Scaling marketing for merchants with targeted and intelligent promos](https://yomu.fyi/post/scaling-marketing-for-merchants-with-targeted-and-intelligent-promos.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Sharon Teng - Published: Oct 11, 2023 Grab previously relied on globally assigned, heuristic promotional campaigns where all users could redeem offers until limits were reached. This lack of targeting and customisation failed to optimize promo spending or meet specific merchant business objectives. To solve this, the engineering team built Bullseye, an automated AI-driven promotional assignment system that customises and assigns offers to consumers. The architecture integrates an operations user interface, backend services, Amazon S3 storage, and Spark jobs executing a suite of predictive modules including eater segmentation, campaign impact simulation, and customer response modeling. Since deploying Bullseye in 2021, the system has increased food campaign sales while decreasing promo spend across food and Mart campaigns. ### [Stepping up marketing for advertisers: Scalable lookalike audience](https://yomu.fyi/post/stepping-up-marketing-for-advertisers-scalable-lookalike-audience.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: William Wu - Published: Sep 22, 2023 Grab's legacy lookalike audience generation platform suffered from long creation SLAs of two working days, high costs, and low weekly update frequencies. To resolve these bottlenecks, the engineering team designed an embedding-based platform powered by an in-memory retrieval service and automated update pipelines. The system creates audience representations by averaging constituent passenger embeddings, determining user membership through real-time cosine score thresholds. To eliminate feature store latency while fitting all embeddings into memory, a hash-based compression method cuts passenger embedding storage needs by roughly 90%. Consequently, audience availability dropped to within 15 minutes of campaign creation, audience generation costs fell by 98%, and ad impressions and clicks doubled. ### [Unsupervised graph anomaly detection - Catching new fraudulent behaviours](https://yomu.fyi/post/unsupervised-graph-anomaly-detection-catching-new-fraudulent-behaviour.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Rizal Fathony - Published: Aug 2, 2023 Fraud detection systems relying on historical training data often struggle to detect new fraudulent modus operandi because emerging patterns lack labeled supervision. Grab developed GraphBEAN, an unsupervised autoencoder model designed to detect anomalous patterns across bipartite interaction graphs representing consumers and merchants. The architecture uses graph convolution layers to encode node and edge attributes, then reconstructs features and edge existence through separate feature and structure decoders. High reconstruction errors flag rare, anomalous behaviors at both the node and edge levels. An automated pipeline applies heuristic fraud tags to these scores and routes them to human analysts and automated mitigation systems. ### [PII masking for privacy-grade machine learning](https://yomu.fyi/post/pii-masking-for-privacy-grade-machine-learning.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Fabrice Harbulot - Published: Jun 1, 2023 Data engineers at Grab require real-world streaming data to tune machine learning models, but user personal data cannot be exposed in non-production environments. To solve this, the data streaming team implemented an automated masking pipeline between production and staging Kafka clusters. Developers explicitly tag Personally Identifiable Information (PII) types in Protocol Buffers schemas, which a CI script validates against a keyword list before packaging the schemas into Scala JARs. An in-house Apache Flink application running in production consumes the unmasked streams, applies dynamic and consistent transformations like keyed HMACs or seeded random values, and produces sanitised records to staging. Untagged new fields in schema updates are automatically dropped by the deserialiser until the masking application is redeployed, preventing accidental data leaks. ### [How KartaCam powers GrabMaps](https://yomu.fyi/post/how-kartacam-powers-grabmaps.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Shuangquan Hou - Published: Dec 1, 2022 Grab's Geo team required an efficient, low-cost way to collect fresh street-level imagery across Southeast Asia, where professional mapping equipment is prohibitively expensive and smartphone crowdsourcing yields inconsistent quality. To address this, Grab built KartaCam, a custom mapping device equipped with a 12MP sensor, dual-band GNSS, 4G LTE, and onboard edge AI. Edge machine learning models evaluate scene suitability, check image quality, filter for map-relevant objects, and blur personal data directly on the device prior to upload. Deploying a four-camera KartaCam 360 array delivers panoramic coverage and point-of-interest data comparable to commercial mapping rigs at roughly one-twentieth the hardware cost. ### [Graph for fraud detection](https://yomu.fyi/post/graph-for-fraud-detection.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Min Chen - Published: Nov 24, 2022 Rapid expansion across multiple business verticals introduced diverse, evolving fraud patterns at Grab, making traditional rule engines and decision trees inadequate due to their heavy reliance on extensive feature engineering and labeled data. To address cold starts and detect unknown fraud, the engineering team implemented a semi-supervised Relational Graph Convolutional Network (RGCN) across millions of connected ecosystem entities. The model propagates information along neighborhood nodes through graph convolutional layers to produce node embeddings and output fraud probabilities. Trained on graphs where only a small percentage of nodes are labeled, the RGCN achieved an AUROC close to 1 and distinctly separated fraudulent embeddings from genuine ones. The approach also offers high explainability by surfacing dense clusters of shared physical devices and addresses ongoing challenges in real-time prediction and noisy connections. ### [How facial recognition technology keeps you safe](https://yomu.fyi/post/how-facial-recognition-technology-keeps-you-safe.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Kai Feng Tee - Published: Jun 9, 2022 Grab utilizes facial recognition technology across its platform for driver authentication, passenger verification, and digital electronic Know Your Customer (e-KYC) processes. The core pipeline consists of image preprocessing through face detection and alignment, anti-spoofing checks, feature extraction into high-dimensional vector embeddings, and downstream verification or search. To counter spoof attacks like screen replays, synthetic moiré patterns are generated and cropped face patches are used during training and inference to focus on local structures rather than global semantic noise. Face verification challenges involving shallow ID datasets and masked faces are resolved using semi-Siamese training architectures and masked data augmentations. ### [Graph Networks - Striking fraud syndicates in the dark](https://yomu.fyi/post/graph-networks-striking-fraud-syndicates-in-the-dark.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Muqi Li - Published: Apr 28, 2022 Grab's Integrity team faced escalating threats from organized fraud syndicates operating device farms and GPS spoofing tools that mimic normal user behavior. To overcome the limitations of traditional rule-based systems, Grab built a Graph-based Prediction Platform that links entities across shared attributes to expose hidden network structures. The architecture includes a scalable graph database platform holding over one billion nodes, powering real-time graph visualization for analysts alongside a configurable network feature extraction system. In addition, the team deployed Graph Neural Networks to capture structural correlations across transaction networks. These systems collectively help detect coordinated promo abuse, payment fraud, and complex money laundering schemes. ### [How we reduced our CI YAML files from 1800 lines to 50 lines](https://yomu.fyi/post/how-we-reduced-our-ci-yaml-files-from-1800-lines-to-50-lines.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Jia Long Loh - Published: Apr 19, 2022 Grab's Cauldron Machine Learning Platform team managed continuous delivery across multiple pipelines by using nested GitLab CI configurations, but soon encountered platform limitations such as the 100-include ceiling and bloated 1,800-line YAML files. To address these constraints, the team implemented GitLab Dynamic Child Pipelines to programmatically create execution stages on the fly. They built a command-line utility in Rust that runs git diff against the base branch, extracts pipeline and stage metadata using configurable stop patterns, and applies a template to produce a dynamic CI definition. On the master branch, the tool fetches the diff artifact from the source branch through the GitLab API to execute identical generation logic. This architectural shift reduced the root CI file from 1,800 lines down to a constant 50 lines, allowing configuration size to remain stable regardless of repository growth. ### [Protecting Personal Data in Grab's Imagery](https://yomu.fyi/post/protecting-personal-data-in-grab-s-imagery.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Adrian Popovici - Published: Jul 26, 2021 Grab's KartaView platform collects geotagged street imagery across over 100 countries, requiring automated obfuscation of faces and licence plates to protect personal privacy. Because off-the-shelf solutions struggled with diverse global environments and equirectangular 360-degree camera formats, Grab built a custom machine learning pipeline. The system projects varied image formats into standardized planar views, applies a YOLOv4 object detection model to locate target regions, and transforms bounding coordinates back to the original imagery for blurring. Training the detector required iterative dataset updates to accommodate edge cases like face masks and mirror reflections, paired with offline view splitting and oversampling of scarce large bounding boxes. Assessments confirmed that obfuscating these regions had minimal negative impact on downstream map feature extraction services. ### [Reshaping Chat Support for Our Users](https://yomu.fyi/post/reshaping-chat-support-for-our-users.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Elisa Monacchi - Published: Jul 7, 2021 Grab transitioned from voice hotlines and third-party tools to an in-house native chat support system integrated into their CRM. The team validated the platform through an MVP and user shadowing to address session disconnections, agent context switching, and routing bottlenecks. To optimize support operations at scale, they introduced dynamic queue limits based on Little's law, machine learning autocomplete suggestions for agents, and duration timers with visual nudges. These enhancements reduced chat waiting times by 30%, unresponsive users by 7%, and overall chat handling duration by 22%. ### [How We Improved Agent Chat Efficiency with Machine Learning](https://yomu.fyi/post/how-we-improved-agent-chat-efficiency-with-machine-learning.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Suman Anand - Published: Apr 19, 2021 Agent typing time represented a large portion of Grab's chat support journey, and 85% of messages were still free typed because agents customized static templates to fit their personal style. To accelerate typing across multilingual markets without robotic templates, Grab built SmartChat, a machine learning feature that provides contextual sentence completion. The team opted for a lightweight seq2seq architecture using single-layered GRU encoder-decoders in TensorFlow instead of bulky attention models to keep model latency under 100ms. The user interface was implemented in React using a content-editable div with inline typeahead suggestions activated via keyboard shortcuts. ### [Using Grab’s Trust Counter Service to Detect Fraud Successfully](https://yomu.fyi/post/using-grab-s-trust-counter-service-to-detect-fraud-successfully.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Chao Wang - Published: Oct 21, 2019 Grab's Trust Platform team built the Counter service to detect fraud across business verticals like transportation, food, and payments. The platform replaces manual, multi-week engineering cycles with a self-service UI where data analysts can define and experiment with counters independently. Operating on an asynchronous ingestion and synchronous transaction model, the architecture evaluates incoming stream data, enriches it via internal services, and persists aggregated signals to ScyllaDB through Grab-Stats. A multi-bucket strategy partitions queries into fifteen-minute, hourly, and daily granularities to maintain low-latency query aggregations across wide time ranges under strict SLAs. ### [Making Grab’s Everyday App Super](https://yomu.fyi/post/making-grab-s-everyday-app-super.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Justin Bolilia - Published: Jul 3, 2019 Grab manages an expanding superapp ecosystem comprising ride-hailing, food delivery, payments, and partner content surfaced through the Grab Feed. As content volume grows, the platform risks overwhelming users with irrelevant information. To address this, Grab built a recommendation engine that ranks cards using signals across user profiles, content metadata, and contextual factors such as time and location. The system employs multiple recommendation strategies—including popularity metrics, user favorites, collaborative filtering, habitual patterns, and cross-platform deep embeddings—which are selected or aggregated. Recommendation quality is evaluated via offline metrics like Recall@K and NDCG alongside online engagement experiments. ### [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. [Newer posts](https://yomu.fyi/topic/machine-learning/page/2.md) · [Older posts](https://yomu.fyi/topic/machine-learning/page/4.md)