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• Scaling Real-Time Recommendations for Millions with Distributed Machine Learning

• ML-Powered Contact Accuracy Score: Unifying Email and Company Verification

• Dual Contrastive Embeddings for Balanced Two-Sided Marketplace Recommendations.

• Building a Scalable Video Moderation Pipeline with Deep Learning and Human Review

• XGBoost Ranking for Hybrid Recommendations: Combining Content & Collaborative Signals at Scale

• Universal & Zero-Shot Models for Unified Semantic Embeddings of Reviews, Photos & Businesses.

• Real-Time Harmful Text Detection in User Reviews Using LLM Classification

• LLM-Powered Real-Time Scam Detection for Livestream Marketplace Messaging

• Fixing E-commerce Search Queries with Language Model Expansion & Rectification

• Building an LLM-Powered AI Assistant for E-commerce Sales Agent Support

• Precise Ad Targeting: Uplift Decision Trees for Incremental Conversion Lift

• Ranking Visually Compatible Furniture Using Deep Embeddings and Triplet Loss.

• Bayesian Thompson Sampling for Product Ranking: Addressing Bias and Sparsity

• Explainable ML for Triaging E-commerce Fraud Orders

• Scalable Personalized Ad Optimization with Contextual Bandits & Importance Weighted Regression.

• Constrained Optimization for Personalized Promotion Assignment with Share of Voice Targets.

• Optimizing Cross-Channel Marketing Spend Using Reinforcement Learning and Uplift Modeling

• Transformer Self-Attention Models for Adaptive E-commerce Recommendations

• Optimizing Millions of E-commerce Ad Bids via a Hybrid ML/Rules Architecture.

• Hierarchical Color Clustering for Accurate E-commerce Product Image Tagging

• Virtual Assistant Intent Classification with Multi-Label Distilled Transformers

• Generating Niche E-commerce Pages Using BERT for Aspect Sentiment Analysis

• Optimizing Email Sends: Predictive Net Value Models and Automated Retraining.

• Detecting Account-Hopping Retail Fraud Using Graph Neural Networks

• Unifying E-commerce Notifications with Contextual Bandits for Optimal Engagement

• Self-Supervised Session Embeddings for Advanced E-commerce Fraud Detection

• Real-Time Business Shopper Identification on eCommerce Platforms using xgboost

• Multi-Phase LSTM & Bias Control for Predicting New E-commerce Winners

• Accurate E-commerce Delivery Prediction via CatBoost Quantile Regression

• Scalable Text Classification Using Semantic Embeddings and Faiss Similarity Search

• Fourier Transform-Based Defrost Cycle Prediction for Refrigeration System Anomaly Detection.

• Personalized Training Recommendations Using Two-Stage Deep Learning and Content Embeddings

• CNN-LSTM for Automated E-commerce Product Taxonomy Classification

• Spark-Powered Association Rules for Scalable Retail Co-Buy Recommendations

• Predicting Refrigeration Temperature Anomalies at Scale Using Prophet

• Accurate Multimodal Product Categorization via Semantic-Aware Label Smoothing.

• Segmenting Multiple Products in Voice Orders Using Transformer-CRF

• Joint Multi-Task Model for Retail Assistants with Automated Log Annotation Pipeline

• Unifying Customer Records: Rule-Based & ML Entity Resolution Techniques

• Style Embeddings for Coherent, Scalable 'Complete the Look' E-commerce Recommendations

• LLM & Vision Driven Product Attribute Extraction and Catalog Matching from PDFs

• Predictive Filtering & Generative AI Checks for Accurate Product Typing at Scale.

• Text Augmentation Strategies for Imbalanced Invoice Error Classification

• Real-Time Fashion Recommendations: Leveraging Vector Search and Metadata Filters

• RAG-Powered Natural Language QA for Long-Form Video Transcripts

• Building a Generative AI Help Desk Chatbot with Retrieval-Augmented Generation

• RAG Pipeline for Context-Sensitive AI Assistant in Software Platforms

• Detecting Recommendation Quality Shifts Using Data Distribution Monitoring

• Categorizing User Feedback at Scale with USE Embeddings and LightGBM

• Tunable Beta-Binomial Exploration for Optimizing Travel Accommodation Rankings

• Hub-and-Spoke GenAI: A Lean Strategy for Location Technology Integration

• AI Product Strategy Co-Pilot: LLMs, Vector Search, and Structured Idea Generation.

• Building a RAG System for Fast, Accurate Support Answers from Proprietary Knowledge

• GNN-Powered Product Bundling: Tackling Sparsity in E-commerce Recommendations.

• GNNs and Attention for Real-Time Sequential E-commerce Recommendations

• AI-Driven Ensemble Modeling Tackles Hidden Retail Stockouts for Accurate Inventory

• Hierarchical Cross-Domain Mapping for Cold-Start Grocery Recommendations with Location-Aware Ranking.

• Accurate Two-Wheeler Distance Prediction via ML and Synthesized Ground Truth

• Detecting Food Delivery Claim Fraud Using Weakly Supervised Label Generation

• Deep Learning Ranker for Multi-Factor Food Dish Search Relevance

• Multi-Objective Restaurant Ranking: Wide & Deep Learning with Monotonic Constraints

• Classifying Incorrect Address Locations using RoBERTa and Geohashing

• Measuring Generative AI's Impact on Developer Productivity and Code Quality

• Stage-Wise Neural Networks for Accurate Real-Time Food Delivery ETA Prediction.

• Accurate Food Delivery Time Breakdown Using MIMO Deep Learning and Entity Embeddings.

• LLM-Powered Neural Search for Conversational Discovery Across Massive Catalogs

• Real-Time ETA Prediction using Multi-Model Architecture for On-Demand Services

• Personalized Meal Combo Recommendations via Embeddings and Approximate Nearest Neighbors.

• LLM-Powered Natural Language to SQL with Domain-Specific Metadata

• Detecting Inaccurate Delivery Locations with Multimodal Machine Learning

• Two-Stage Semantic Models for Precise Multi-Intent Food Search Retrieval

• Semi-Supervised Deviation Networks for Food Delivery Fraud Detection

• Fine-Tuning GenAI & RAG: Enhancing Food Platform Catalogs, Search, and Support Automation.

• Self-Supervised Geocoding for Real-Time Correction of Inaccurate Delivery GPS Locations.

• LLM System for Generating and Evaluating Suspicious Financial Transaction Reports

• Multi-Branch Deep Neural Networks for Scalable, Accurate Payment Fraud Detection

• Clustering Linked Fraudulent Accounts at Scale Using GBDT Similarity Scoring.

• Scalable Machine Learning for Real-Time Card-Not-Present Fraud Detection.

• Scaling Sequence Recommendation Model Training with PyTorch Distributed Data Parallel.

• Decoding Client Style Notes with BERT for Enhanced Fashion Recommendations.

• Personalized Fashion Recommendations from Images Using Hybrid Embeddings and Computer Vision

• Predictive Fashion Inventory Optimization Using UMAP Embeddings and Simulation

• Unified Real-Time Recommendations Using Sequence Modeling and Temporally-Masked Encoders

• Interactive UI for Debugging and Experimenting with Personalized Search Microservice Pipelines

• LLM-Powered Centralized Platform for Scalable, High-Quality Content Annotation.

• Personalizing Recommendations for Repeat Listens: Heuristics vs. Neural Networks

• Automated Pipelines for Dynamic Streaming Recommendations and Custom Evaluation Metrics

• Robust Media Recommendations: Data Consistency, Drift Detection, and Automated Retraining

• Scalable Global Time-Series Forecasting: Unified Pipeline for Diverse Streaming Markets.

• Semantic Podcast Search using Dense Retrieval and Transformer Embeddings

• Uplift Modeling for Targeted In-App Messaging on Streaming Platforms.

• Scaling Podcast Preview Generation with Streaming ML Pipelines and GPU Acceleration

• Bipartite Graph Neural Networks for Consistent Playlist Song Recommendations.

• Automated Marketing Loop: XGBoost Ranking for Dynamic Streaming Platform Ads

• Scaling Personalized Video Recommendations: Two-Tower Embeddings & ANN Retrieval.

• Calibrated Machine Learning for Scalable Real-Time Ad Ranking Systems

• Building Secure Enterprise Summarization & Search with Private Cloud RAG

• Threshold-Based Email Classification Using Eventually Consistent Domain Aggregation

• Scalable Real-Time Spam Invite Detection Using Logistic Regression

• Faster Customer Support Answers Using Semantic Search and Language Models.

• Enterprise App Recommendations via Relevance Models with Tunable Diversity and Explainability

• Reducing Enterprise Chat Overload with Scalable, Private Neural Network Summarization.

• Predicting Churn with ML: Addressing Poor Playback from Outdated Browsers.

• Using Classification to Detect Outdated Browsers and Boost Video Streaming Quality.

• Optimized Unified Transformers for Real-Time Multilingual Gaming Chat

• Scaling Real-Time Voice Moderation with Transformers and Machine Labeling

• From Diagnostics to Patches: Finetuning Code Models for Automated Repair

• Standardizing Business Classification: A RAG Pipeline Approach with NAICS Codes

• Standardizing Industry Classification with Retrieval-Augmented Generation and LLMs

• Optimizing Marketplace Recommendations Using Two-Tower Neural Network Models.

• Scalable Hybrid Recommendation Systems for Real-Time Engagement and Personalization

• Optimizing Large-Scale Ad Conversions with MTL, Sequence Modeling, and Ensemble Serving

• Scalable Real-Time Recommendations via Embedding Retrieval and Deep Neural Ranking.

• Real-Time Comment Moderation and Ranking with Multi-Task Transformers

• Combating Coordinated Spam: Real-Time Anomaly Detection, Clustering, and Automated Rule Generation.

• Targeted Audience Expansion Using User Embeddings and Per-Advertiser Classifiers

• Boosting Ad Performance with ML: GBDT-Ranked Recommendations for Bids, Budgets, and Targeting.

• Real-Time Recommendation Ranking with Transformers Using User Action Sequences

• Real-Time Multi-Action Predictions using Multi-Task DNNs and Utility Blending

• Knowledge Distillation & Automated Pipelines for Scalable DNN Recommendation Rankers.

• Proactively Predicting Advertiser Churn Using Gradient Boosting Trees

• Building a Real-Time Notification System with Multi-Task ML and PID Control

• Fine-Tuning Latent Diffusion for Stylized E-commerce Background Inpainting

• Natural Language to SQL with Vector Embeddings and LLMs for Large Warehouses.

• Aligning Ad Prediction Models: Fixing Offline vs. Online Performance Discrepancies.

• Hybrid Grocery Recommender: Boosting Repeats & Exploring New Items with Sequence Models

• Accurate Grocery Drop Time Prediction with MLP to Boost Delivery Efficiency.

• Accurate Retail Demand Forecasting Using Transformers and Scalable MLOps

• Temporal Fusion Transformers for Robust Multi-Horizon Perishable Demand Forecasting

• Multilingual Customer Message Classification and Routing Powered by BERT

• Enhancing Multilingual Grocery Search with LLMs and Vector Embeddings

• Personalized Fitness with Real-Time ML Recommendations & Churn Prediction

• Two-Layer Ensemble Model for Progressive Daily Sales Opportunity Prioritization

• Deep Multi-Task Learning for Robust Detection of Evolving Fraud Types

• Real-Time Graph Embeddings for Detecting Interconnected Payment Fraud

• Predicting Payment Card Declines with Gradient Boosting to Improve Authorization Rates.

• Maximizing Cross-Selling Revenue: Deep Learning with a Custom Profit-Driven Loss Function.

• Scaling Real-Time Fraud Detection Models with CI/CD and Shadow Environments

• Extracting Standardized Job Roles from Ads Using Generative AI and Taxonomy.

• Detecting Modified Fraudulent Images Using Siamese Networks and Triplet Loss.

• Neural Embeddings for Item Recommendations: Solving Cold Start via Content & Interactions.

• CatBoost Quantile Regression for Marketplace Shipment Delivery Time Estimation.

• Gradient Boosting for Precise Delivery Service Time Estimation and Route Optimization.

• Geofencing-Powered Machine Learning for Accurate Delivery Service Time Prediction

• Scalable Grocery Demand Forecasting with Hybrid Heuristics and Deep Networks

• Predicting Call Reasons with Real-Time Contrastive Learning Embeddings from Click Events

• Optimizing Personalized Coupons Beyond Prediction Using Causal Inference

• Graph-Based Categories & Vision Embeddings for Automated Outfit Generation

• Boosting Email Engagement: Generative AI Subjects Validated by a Reward Model

• Personalizing Geo-Notifications with XGBoost Relevance and PID Volume Control

• Proactively Flagging Abusive Threads Using Transformers and Behavioral Signals

• On-Device Handwriting Recognition for Mobile Crosswords Using Deep CNNs.

• Automating Media Interest Tagging with Ensemble Classifier Chains

• Causal Machine Learning for Personalized Metered Paywalls: Optimizing Engagement and Conversion.

• Personalized Recipe Carousels Using Embeddings and Contextual Bandits

• Causal Inference with Synthetic Controls & DML for Platform Growth

• Identifying Outdated Browsers Causing Video Errors with Supervised Classification

• Scalable Transformer Foundation Model for Unified Personalized Recommendations

• ML for Smooth Streaming: Predicting Throughput, Adapting Bitrate, Caching, Detecting Anomalies.

• Improving Streaming Video Quality and Efficiency with Deep Learning Downscaling

• Optimizing Recommendations within Time Budgets using Reinforcement Learning.

• Detecting Streaming Fraud at Scale with Semi-Supervised Autoencoders

• Unified Multi-Task Learning for Diverse Streaming Platform Recommendation Use-Cases.

• CRNN for Overlapping Frame-Level Speech and Music Detection in Audio

• Personalized Sizzle Reels: Real-Time Video Stitching Based on Viewer Rankings

• Scalable In-Video Text Search Powered by Contrastive Learning Embeddings.

• Contextual Bandits with Predicted Delayed Rewards for Better Long-Term Recommender Satisfaction.

• Efficient Video Classification Using Active Learning and Vision-Language Models

• Matching Documents to Saved Queries with Elasticsearch Percolator

• Fixing Data Job Failures: Cost-Optimized Auto-Remediation using ML & Bayesian Optimization.

• On-Device Image Captioning using Compressed Models for Automatic Alt Text

• Using XGBoost to Intelligently Select Tests in Large Continuous Integration Systems

• Optimizing Multi-Product Campaign Assignment with Uplift Modeling

• Categorizing Short Text Savings Goals with Biterm Topic Modeling

• Enhanced RAG and Vector Search for Reliable Multi-Turn Documentation Chatbots

• Using LLMs to Automate Incident Root Cause Detection and Mitigation Suggestions.

• LightGBM for VM Origin Classification using Heuristics with Limited Labeled Data.

• Discovering Support Ticket Themes with BERT Embeddings and HDBSCAN Clustering.

• Predicting Customer Support Dissatisfaction Risk with Machine Learning Classification.

• Architecting Scalable LLM Copilots for Context-Aware Software Assistance

• Ranking Code Changes with Fine-Tuned LLMs for Faster Incident Diagnosis

• Multitask Neural Networks for Real-Time Personalized Social Feed Ranking

• Optimizing Push Notifications via Uplift Modeling to Maintain Engagement

• Unified 100-Language Speech & Text Translation with a Single Self-Supervised Transformer Model.

• Scalable Multi-Stage Recommendations Using Two-Tower Neural Networks for Massive Content Discovery

• Developing Specialized Code LLMs: Data, Context, Fine-Tuning, Benchmarking, and Safety.

• Scaling Image-to-Animation Diffusion: Model Optimization and Global Traffic Management

• Building Scalable Lookalike Audiences with No-Code ML for E-commerce

• Scoring & Orchestrating Multi-Strategy Push Notifications for Personalized E-commerce Engagement.

• Measuring Long-Term Notification Impact on E-commerce Retention via Causal Inference.

• Forecasting Sales vs. Demand with Global LSTM Time Series Models

• PCA Eigenvalue Clipping & Clustering for Explainable Fraud Feature Selection

• Scalable Item Dimension Prediction Using FastText Embeddings and Approximate Nearest Neighbors

• LLM-Driven RAG for Enterprise Q&A and Automated Technical Documentation.

• Retriever-Ranker Architecture with Vector Databases for Real-Time Service Matching.

• Predicting Reliable Rideshare ETAs Using Tree-Based Classification

• Optimizing Personalized Rideshare Offers for Maximum Incremental Rides Within Budget

• Robust Rideshare Fraud Detection: Implementing GBDTs/DNNs & Scalable Pipelines.

• Neural Network Behavior Fingerprinting for Detecting Rideshare Driver Scams

• Long-Term Rideshare Forecasting with Spline-Exponential Cohort Retention Curves.

• LTV Prediction Driving Automated Marketing Budget Allocation and Dynamic Bidding

• Hybrid Neural Recommendations: Adapting to New Products and Shifting User Preferences

• Classifying Intersection Controls using CNNs on Telemetry-Derived Speed Images

• Real-Time Ride Destination Prediction Using Multi-Head Attention

• Differentiable DAGs for Causal Forecasting and Optimizing High-Dimensional Marketplace Decisions

• Optimizing Ride-Sharing Metrics Using Causal DAGs and Experiments

• Optimizing Rideshare Prices with Real-Time Reinforcement Learning

• Gradient Boosting for Personalized Ranking of Multi-Modal Ride Options.

• Interpretable PDF Analysis: Graph Layout Detection and Attention-Based Classification/NER.

• Accurate PDF Paragraph Extraction Using Graphs and Random Forest

• Real-Time Outdated Browser Analysis & Update Prompt Prediction with Logistic Regression

• Real-Time Marketplace Listing Re-ranking with LLM Two-Tower Models

• Hybrid Topic Modeling & Neural Networks for Customer Feedback Theme Discovery & Monitoring.

• Designing a Real-Time ML System for Scalable Product Search Ranking

• Predicting Video Platform Churn from Outdated Browsers Using Classification Models

• Real-Time Fraud Detection at Scale Using Streaming Pipelines and ClickHouse Analytics

• Optimizing Real-Time Grocery Delivery Using Advanced VRP Heuristics and Machine Learning.

• Real-Time Grocery Availability Prediction: Scaling Gradient Boosting with Mean Encoding

• Estimating Lost Fulfillment Demand Using Conversion Probability Modeling

• Retail Autocomplete: Multi-Objective Ranking and Semantic Matching for Enhanced Product Search

• Optimizing Recommendations for Learning Users via Two-Phase Multi-Armed Bandits

• Enhancing Retail Search Relevance with Dual-Tower Transformer Embeddings

• Advanced LLM Prompting Techniques for Accurate and Scalable E-commerce Productivity Tools

• Unified Wide-and-Deep CTR Prediction for Multi-Surface E-commerce Ads

• LLM-Based Internal Assistant: Secure Retrieval, Scaling, and Cross-Team Integration

• ML-Driven Real-Time Grocery Availability with Hybrid Refresh and Dynamic Thresholds.

• Predicting Grocery Item Availability with Machine Learning and Dynamic Thresholds.

• Contextual Bandits for Dynamic Selection of Personalized Item Ranking Methods

• Hierarchical ML Predicts Real-Time Store Item Availability at Scale

• Automating Deli & Bakery Imagery using Text-to-Image AI

• Adaptive Hybrid Retrieval Using Query Entropy for Enhanced E-commerce Search Relevance

• Enhancing E-commerce Search Discovery with LLM-Generated Product Recommendations

• Unified Next-Product Retrieval for E-commerce Using BERT Sequence Models.

• Thompson Sampling for Adaptive Marketing Budget Allocation and Profit Maximization

• Optimizing E-commerce Authorization Buffers with Regression Discontinuity Design

• Real-Time Slack Event Summarization Using Large Language Models

• LLM-Powered Natural Language to Query Translation Using Schema Embeddings

• Evaluating LLM Query Assistant Success: Metrics, Retention, and Cost Optimization.

• Real-Time Personalized Recommendations at Scale Using Two-Tower Neural Networks.

• AI Pipeline for Large-Scale Digital Asset Tagging using CV, OCR, and ML

• Real-Time Manufacturing Step Identification Using Sensor Data and FastDTW

• Automating Product Metadata Extraction with Cloud-Scale OCR and Fuzzy Matching

• Optimizing AdGroup Marketing Spend with Thompson Sampling

• Tracking Multilingual Healthcare Queries with NLP and TF-IDF Analysis

• Accurate Quote Attribution Using Machine Learning and Coreference Resolution

• Using Machine Learning Entity Linking to Disambiguate Names in Leaked Data

• Scalable Half-Hourly Food Delivery Forecasting with Python, Dask, and Time Series

• Scalable Time-Series Forecasting for Multi-Region Food Order Demand

• Detecting Delicate Text Beyond Toxicity with Transformer Models

• Adversarial Training for Context-Aware Grammatical Error Correction

• Accelerating Grammatical Error Correction with Interpretable Sequence Tagging

• Predicting Email Sentence Attention Using Cost-Value Linguistic Models

• Hybrid Sequence Models & Rules for Scalable Automated Grammar Correction

• Operational Transformation for Real-Time Text Editor Suggestions

• Specialized Text Revision Using Instruction Fine-Tuned Language Models

• Reducing Gender Bias in Grammar Correction Using Data Augmentation.

• Adaptive On-Device Keyboard Dictionary Management Using Time Decay & Edit Distance

• RAG-Powered Automation for Recurring SQL Queries and On-Demand Summaries.

• Scalable Cross-Service Fraud Detection Using Relational Graph Networks (RGCNs).

• AI-Driven Promo Optimization Using ML Segmentation & Hyperparameter Tuning

• Efficient Lookalike Audiences via Compressed Embeddings & Real-Time In-Memory Matching

• Detecting Emerging Transaction Fraud with Unsupervised Bipartite Graph Autoencoders.

• LLM-Powered Automated Column Tagging for Sensitive Data Governance

• Enhancing Vector Search with LLM Re-ranking for Complex Text Queries

• Accelerating Data Discovery: LLMs, Semantic Search, and AI Chatbots for Data Lakes.

• Predicting Subscription Churn with Gradient Boosting on Behavioral Data.

• Tiered Recipe Recommendations: Using 2-Tower Deep Learning for Personalized Food Delivery

• Robust Recipe Recommendations for Cold Starts using Hybrid Embeddings

• LLM-Driven Automation for Generating Secure Enterprise Incident Summaries

• Distilled Hybrid Seq2Seq Models for Efficient On-Device Grammar Correction

• AI-Powered Photo Curation: Building Automated Yet Controlled User Memories

• Abstractive Summarization of Enterprise Chats Using Distilled Transformers

• Automated Enterprise Document Summarization Using Transformer-Based Knowledge Distillation

• Optimizing User Engagement with Scalable Machine Learning Recommendation Pipelines

• Neural Network Recommender: Feature Engineering & Embeddings for High-Scale Interactions

• Machine Learning & Phase-Split Modeling for Accurate Real-Time Food Delivery ETAs.

• Architecting a Scalable Forecasting System using XGBoost and Exogenous Features

• Scalable Food Search Relevance Optimization with nDCG and ML Re-ranking.

• Personalized Restaurant Search Using Pairwise Learning to Rank

• Cross-Selling Platform Services Using Matrix Factorization Recommendation Systems

• Reducing User Churn with Real-Time Prediction via Streaming ML Pipelines

• Predicting Reliable Food Delivery ETAs Using XGBoost Regression

• Architecting Scalable AI Customer Support with LLM Multi-Agent RAG Systems

• Recommending Posts Using Embeddings and Implicit Feedback Signals

• Hybrid Embeddings for Real-Time Forum Post Ranking Based on User Likes

• Evaluating LLMs for Code Assistance using Cosine Similarity and Iterative Validation

• Enhancing AI Code Assistants with Data Pipelines, ML Refinement, and Security Checks.

• Automated Code Vulnerability Fixing Using LLMs and Static Analysis Integration

• Hybrid Deep Learning Static Analysis for Scalable Code Vulnerability Detection

• Productionizing Interactive LLM Coding Assistants for Enhanced Developer Workflows

• Enhancing AI Code Assistant Context with Vector Embedding Retrieval for Large Codebases.

• Strategic Integration of AI Code Generation for Enhanced Software Development

• Generative AI for Secure Code Reviews and Separation of Duties Compliance

• High-Precision Text Classification Identifies Good First Issues in Public Projects

• Leveraging LLMs for Real-Time Code Completion via Prompting and Fine-Tuning.

• Scalable Real-Time Travel Ranking with Streaming Pipelines and Feature Stores

• Embedding-Based Pipeline for Large-Scale Travel Image Moderation, Deduplication, and Similarity.

• Agile ML for Budget Optimization Amidst Volatile Supply and Demand.

• Auto-Classifying Restaurant Cuisines with Customer Clicks and Menu Embeddings

• Scalable A/B Testing Pipeline for Food Delivery Menu Conversion Optimization

• Predictive Modeling for Optimal Customer & Rider Incentive Budget Allocation

• Budget Optimization for GMV: Forecasting Saturation Points with Generalized Additive Models.

• Precise Automated Bug Routing Using XGBoost Text Classification.

• AI Search for Billions of Designs with Multimodal Embeddings and Vector Search

• Continuous ML Session Analysis for Real-Time Banking Fraud Detection

• Boosting Marketplace Semantic Search with Two-Tower Embedding Models

• Real-Time Wholesale Product Ranking using Elasticsearch Retrieval and XGBoost Re-ranking

• Feature Store for Real-Time Ranking: Unifying Batch and Streaming Features

• Scaling Semantic Relevance Classification with Fine-Tuned LLMs in E-commerce Search.

• Two-Tower Embeddings for Concept-Based Hotel Recommendations Using User Reviews

• Personalizing Business Traveler Hotel Search with Random Forest Ranking

• Unsupervised Multi-Label Text Classification Using Word Embedding Distance

• Two Tower Retrieval with LogQ Correction for Large-Scale Property Recommendation.

• Optimizing Travel Price Alerts via Random Forest Engagement Prediction

• Optimizing Flight Price Notifications with Random Forest Predictions

• Balancing Relevance and Diversity in Recommendations via MMR Re-ranking

• Predicting Travel CLV at Scale using Gradient Boosting and MLOps Pipelines

• Overcoming Sparsity: Using Synthetic Data for Robust Flight Price Forecasting.

• Unified Lodging Ranking with Deep Learning: Balancing Relevance and Property Similarity.

• Two-Stage ML Ranking for Travel Lodging with Real-Time Pricing & Availability.

• Designing an AI Group Travel Planner with LLMs and Real-Time Personalization

• Boosting Developer Productivity with Generative AI: Fine-Tuning, RAG & Commercial Tools

• Multi-Level Relevance Ranking with Gradient Boosting for Better Marketplace Ads

• Two-Tower Models & ANN for Low-Latency Personalized E-commerce Recommendations.

• LLMs and Personalization Models for Generating Adaptive Language Lessons

• AI-Personalized Language Learning: Adaptive Paths using Spaced Repetition & Text Classification.

• Securing LLMs: Filtering Token Injection Attacks Using Repetition Ratios

• Real-Time Next File Prediction with Neural Networks and Learning-to-Rank

• Optimizing Subscription Renewals with Machine Learning for Optimal Charge Timing.

• Content-Based Image Retrieval Using Classification and Semantic Word Embeddings

• Neural Network-Powered Subfolder Suggestions with Human-in-the-Loop Control

• ML-Predicted Expected Revenue (XR) for Faster Subscription Platform A/B Testing

• Defending LLMs: Detecting and Sanitizing Control Character Prompt Injection Attacks

• Transformer IOB Tagging for Accurate Date Detection and Reformatting in File Names

• LLM-Powered File Summarization and Q&A Using Embeddings and Clustering

• Boosting E-commerce Engagement with Scalable AI-Driven Recommendation Systems

• End-to-End Deep Learning for Video Recommendations to Maximize User Engagement

• Boosting Streaming Retention: Personalized Recommendations via Deep Neural Networks.

• Boosting Online Marketplace Engagement with Deep Learning Recommendations

• Boosting User Retention with an AI Pipeline for Real-Time Personalized Recommendations.

• Deep Learning for Scalable Real-Time User Personalization and Engagement

• Deep Learning Recommendation Engine for Optimizing User Engagement at Scale.

• Boosting Media Engagement via an End-to-End Neural Recommendation Pipeline.

• Scaling Real-Time Recommendations: A Two-Stage Embedding Pipeline for Coverage and Cold Starts

• Deep Learning Recommendation Engines for Optimizing Digital Content Engagement.

• Unified Data & Deep Embeddings for Scalable Real-Time Global Recommendations

• E-commerce ML Pipeline: Real-Time User Preference Prediction and Anomaly Detection

• Building an End-to-End Predictive System for E-commerce Recommendations

• End-to-End Deep Learning System for Real-Time E-commerce Product Ranking.

• Improving Marketplace Recommendations with Neural Embeddings and Real-Time Behavior

• Scalable Personalized Recommendations: Building Real-Time Machine Learning Pipelines

• Designing a Scalable Deep Learning Recommendation System for Large Consumer Platforms

• End-to-End Machine Learning for Real-Time E-commerce Product Ranking at Scale.

• ML-Powered Recommendation Pipeline for Driving Real-Time User Engagement.

• ML-Powered Real-Time Personalized Product Ranking System

• Deploying Large Language Models for Scalable Text Interpretation

• Unified Multi-Entity Graph Embeddings via Contrastive Triplet Learning

• Fine-Tuning Transformers for Safe, Persona-Based Accounting Query Generation.

• Real-Time Entity Extraction from Banking Transactions Using BERT NER

• Personalized Accounting Suggestions via Vector Embeddings and ANN Search

• Designing a Scalable Machine Learning Pipeline for Data-Driven Stock Selection.

• Architecting ML Pipelines for Scalable Real-Time Recommendation Systems

• Predicting & Reducing Churn with a Real-Time Gradient Boosting Pipeline

• Boosting Recommendation Relevance with Scalable Real-Time Machine Learning

• Scaling Deep Learning Recommenders: Architecture, Deployment, and Continuous Improvement.

• Predictive Modeling for Optimizing Shipping Costs and Carrier Selection

• Using LLM Opinion Scoring and Vector Search for Alternative Perspective Video Recommendations

• Cross-Lingual Video Classification with Multilingual Embeddings

• Scalable Multi-Label Video Classification Using Deep Visual Frame Aggregation

• Video Recommendation Pipeline: Ensemble Ranking for Freshness, Diversity, and Engagement

• Explicit User Choice Models for Real-Time, Influential Recommender Systems

• LightGBM for E-commerce Truck Slot Prediction: Optimizing Fulfillment Center Dock Usage

• Unsupervised Asian Product Title Segmentation Using Character Embeddings and GRUs.

• De-duplicating E-commerce Catalogs at Scale with Text/Image Embeddings and FAISS

• Optimizing Food Delivery: Global Matching, Dynamic Pricing & Deep Learning ETAs

• End-to-End Machine Learning Pipeline for Predicting User Engagement and Activity

• Neural Network Ranking & Bandits for Personalized Engagement & Cold-Start.

• Detecting Sophisticated Proxy Bots with Gradient Boosting and Behavioral Analysis.

• Optimizing Real-Time Delivery Dispatch Using Machine Learning and Mathematical Optimization

• Leveraging Text Embeddings to Improve Real Estate Listing Recommendations.

• Multi-Stage Hybrid Course Recommendations: Blending Collaborative Filtering & Response Prediction

• Blending Neural CF & Response Prediction for Personalized Course Recommendations

• Automated Floor Plan Generation using Object Detection on 360° Indoor Panoramas.

• Automated Ad Budgeting with ML-Synthesized Cost Curves

• Two-Stage Personalized Search Ranking with Embeddings and Gradient Boosted Trees

• Neural Network Revenue Prediction for Dynamic Ad Request Filtering

• Optimizing Personalized Mobile Notification Timing with Deep Reinforcement Learning

• Ranking Real Estate Filters Probabilistically via User Clickstream Patterns

• Smarter Aggregator Search: Leveraging Knowledge Graphs and Machine Learning Ranking

• Designing a Multi-Agent RAG System for Accurate API-Driven Q&A on Professional Platforms

• Transformer-Based Demand Forecasting for Fashion with Monotonic Discount Modeling

• LLM-Powered RAG Chatbot Streamlines BPMN Creation from Internal Knowledge Bases

• Collisionless Hashing for Scalable Real-Time Sparse Feature Recommendations.

• Adaptable Courier Photo Classification Pipeline via Transfer Learning and ResNet

• Building Scalable Real-Time Image Recognition with Transfer Learning

• Boosting Job Recommendation Relevance with CNN-Based User Activity Embeddings

• Unified Multitask Learning for Diverse Machine Learning Tasks

• Balancing Delivery Supply/Demand Using LightGBM Forecasting and Optimization.

• Predicting SQL Query Resource Needs with XGBoost and TF-IDF Features

• Dual-Model ML Pipeline for High-Risk Vendor Detection with Limited Labels

• Scaling Multi-Vertical Search using Federated Architecture and Learning-to-Rank.

• Three-Tower Deep Learning for Fresh, Calibrated Ad CTR Prediction.

• Predicting Home Buyer Intent: A Gradient Boosting Approach Using User Activity Data

• Enhancing Search Ranking Beyond GBDT Plateaus with Unified Deep Learning

• Two-Tower Networks for Unified, Personalized Social Account Recommendations.

• Real-Time Rideshare ID Verification Using On-Device ML, OCR & Fraud Detection

• Automated Observational Causal Inference Platform for Measuring Treatment Effects at Scale.

• Improving Home Valuation by Mining Listing Texts with Embeddings and Taxonomy Matching.

• Bayesian Time Series Anomaly Detection & FP-Growth for Automated Fraud Rules

• Predicting Food Delivery ETA with XGBoost and Real-Time Features

• Transformer Post-Processing for Accurate Ride & Delivery ETAs

• ML-Driven Push Notification Scheduling via Integer Programming Optimization.

• Predictive Ranking of Restaurants for Food Delivery Expansion Using XGBoost

• Extracting and Ranking Real Estate Features Locally Using BERT NER

• Enhanced Social Search Relevance via Multi-Aspect and Re-Ranking Architecture

• Predicting SaaS Churn & Upsell with Scalable, Explainable XGBoost and SHAP Insights

• Dynamic Node2Vec Embeddings for Scalable Blockchain Malicious Address Detection.

• Accelerating Content Moderation Model Updates Using AutoML Pipelines

• Using Denoising Autoencoders to Impute Real Estate Attributes for Accurate Price Prediction.

• Enhancing Sponsored Search Ranking with Short-Term Transformer-Based User Embeddings

• Machine Learning for Grocery Substitutions: From TF-IDF to Deep Learning Recommendations.

• Ad Strategy Simulation: Optimizing Bids and Auctions with Reinforcement Learning

• Product Title Matching: Efficiently Combining Lexical and Semantic Search with Retrieval-Rerank

• Dynamic Content Prioritization using XGBoost for Faster Violation Detection

• Solving Cold Start Restaurant Ranking with LambdaMART

• Scalable Personalized Vendor Ranking via Matrix Factorization and User Similarity

• Robust Skill Extraction and Mapping via Two-Tower Models and Knowledge Distillation

• Adaptive Anomaly Detection for Multi-Dimensional Time Series Using Seasonality and Feedback.

• Machine Learning for Personalized E-commerce: Ranking Collections & Items with Diversity.

• Efficient Visual Product Search Using Multitask Deep Learning Embeddings

• ML-Powered Personalization for Food Delivery: Tackling Cold Start & Diverse Preferences

• Cascade Modeling: Improving GBM Holiday Forecasts via Linear Correction.

• Combating Inaccurate Store Status: ML Predicts Closures via Driver Reports & Photos

• Mitigating Bias in Real Estate Text using Topic Modeling and Fair Machine Learning.

• Using LLMs for Resilient Automated Mobile Testing Across Evolving UIs and Languages.

• Enforcing Quality Software Change Titles with Rule-Based NLP

• Classification Guardrails for Fair Housing Compliance in Real Estate LLMs.

• Enhancing E-commerce Catalogs: LLMs for Automated Attribute Extraction and Entity Resolution.

• Rideshare Payment Fraud Detection: An ML & Dynamic Challenge Approach

• Multi-Task Deep Learning for Scalable, Probabilistic Food Delivery ETA Predictions.

• Stabilizing Search Ranks: Using Layered Systems, Twiddlers, and User Feedback Loops.

• Building a Multi-Model AI Assistant for Online Work Marketplace Talent Matching

• Generating Accurate SQL from Natural Language Using RAG and LLMs

• Scaling Multi-LLM GenAI Systems with RAG and Enterprise Guardrails

• Building Reliable RAG LLM Chatbots for Delivery Service Contractor Support

• Graph Neural Networks for Personalized Notifications: Re-engaging Food Delivery Users

• Designing an LLM-Powered AI Recruitment Agent with Semantic Search and Orchestration.

• Detecting Violating Marketplace Listings with Multimodal Embeddings and Focal Loss

• RAG Copilot for Internal Support: Automating Query Answers from Documentation

• LLM & Knowledge Graph Enhanced Search for Complex Attribute Queries.

• LLM-Powered Text-to-SQL: Semantic Retrieval & Self-Correction for Enterprise Data Querying

• Real-Time Airport Driver Wait Forecasting via Demand/Supply Modeling and Simulation

• Scalable Nationwide Home Valuation via Neural Networks and Quantile Regression.

• ML-Driven Dynamic Order Release: Balancing Driver Wait Times and Food Freshness

• ML Payment Routing: Boosting Renewals with Inverse Probability Weighting

• Hybrid ML Pipeline for Proactive and Reactive Viral Spam Detection.

• Canonical MMoE Ranker: Balancing Favorites and Purchases Across Marketplace Modules

• Architecting High-Scale Social Feeds: Neural Ranking, Embeddings & Candidate Sourcing

• LLM-Driven Engineering Analytics for Software Team Productivity and Oversight.

• Auto-Categorizing Data Fields at Scale Using Hybrid Rules and Machine Learning.

• Leveraging Generative AI to Enhance On-Demand Platform Experiences and Operations.

• Enhanced Spam Call Detection via Semi-Supervised Audio Preamble Removal

• Explainable Unsupervised Anomaly Detection for Evolving Marketplace Fraud

• Scalable Granular Forecasting: Efficient Ensemble Stacking with Ray

• Neural Networks & Sparse Embeddings for Large-Scale Personalized Feed Ranking

• Efficient Image Embeddings for E-commerce Visual Search and Recommendations.

• Scalable Multi-Stage Ranking for Personalized Delivery Marketing Recommendations

• Scalable Machine Learning for Accurate Rental Property Revenue Forecasting.

• Scalable Machine Learning for Dynamic Retail Pricing and Profit Optimization

• Hotel Sentiment & Topic Modeling from Social Media using Machine Learning

• Supervised Ranking on Social Graphs for Friend Recommendations

• Predicting Loan Defaults: A Cost-Sensitive Approach Using Reject Inference

• Scaling Real-Time Explore Recommendations using Embedding Retrieval and Neural Ranking

• Boosting Streaming Watch Time Using Collaborative Filtering Recommendations

• Predicting Retail Revenue Using Normalized Mobile Foot Traffic Data

• Zero-Downtime Online Migration for Massive Scale Sharded Document Databases

• Cascade Bandits for Bias-Corrected E-commerce Homepage Item Ranking

• Scalable Visual Classification for Diverse Documents Without Text Analysis.

• Extracting Keyphrases & Entities from Dense Text using NER & Knowledge Base Linking

• LLM Generation and Judging of ASTs for Natural Language Marketing Segmentation

• Monte Carlo Simulation: Finding Sample Size for Reliable Extension Quality Tracking.

• Multi-Modal, Multi-Task Learning for Hierarchical E-commerce Product Classification

• Scalable Hierarchical Product Classification Using Hashing and Logistic Regression

• Real-Time Product Embeddings for Enhanced E-commerce Semantic Search

• Predictive Test Optimization: Faster Feedback & Defect Routing with Machine Learning.

• Dynamic Document Taxonomy Building with User Interaction Embeddings, t-SNE & HDBSCAN.

• Transformer Embeddings for Scalable, Real-Time Recommendations with Filtered Retrieval.

• Scalable Real-Time Recommendations: Collision-Free Embeddings via Cuckoo Hashing for Sparse Data.

• Using Machine Learning to Predict Video Churn from Outdated Browser Playback Issues.

• Engine Fault Detection from Sound using Hybrid Signal Processing and Deep Learning

• Lightweight DCT-Based Blur Classification for Automated Car Image Inspection

• Dynamic Used Car Pricing: Optimizing Sales & Margins with Demand Modeling

• Learning-to-Rank for Personalized Real-Time Used Car Recommendations

• Real-Time Machine Learning for High-Recall, High-Precision Transaction Fraud Detection.

• Random Forest for Effective AML Risk Scoring and Alert Prioritization

• Using Edge ML & Logistic Regression for Secure Virtual Card Checkout Autofill.

• Bayesian Root Cause Analysis for Real-Time ML-Detected Microservice Anomalies

• Detecting Implicit Design Groups with Vision Transformers and Polygon Overlap

• Browser-Based LSTM Recognizes Hand-Drawn Shapes for Vector Conversion

• Optimizing App Store Ad Bids with Contextual Bandits and Survival Analysis

• Fine-Tuning Multilingual Transformers for Scalable Content Moderation

• CNN, LSTM, YOLO Image Analysis via Flask API for Social Platform Engagement

• Real-Time Multilingual Text Moderation Using Transformer Models

• Automating Merchant Identification and Categorization with Machine Learning Classification

• Real-Time Multi-Stage ML Ranking for Personalized Travel Search at Scale

• Graph Databases and BFS Features for Real-Time Reservation Fraud Detection.

• Machine Learning for Fraud Detection in Ride-Sharing Marketplaces.

• Predicting Carpool Detour Acceptance Using Gradient Boosting Models

• Improving Ride Acceptance Rates with Predictive Scoring and Continuous Experimentation.

• Real-Time Ride-Sharing Fraud Detection Using an ML Pipeline with Kafka & Feature Stores

• Using Machine Learning to Drive Timely Browser Updates and Enhance Security

• Accelerating Vehicle Image Labeling with Embedding Similarity and Iterative Refinement.

• Real-Time User Segmentation for Personalized Featured Marketplace Listings.

• Optimizing Fashion Markdowns: A Hybrid ML Approach Using Price Elasticity Modeling.

• Quantifying Ad Tier Uplift using Monotonic LightGBM for Marketplace Listings

• Capturing Evolving Styles: Sequential Fashion Recommendations with Transformers

• Personalized Fashion Recommendations at Scale Using Neural Collaborative Filtering Embeddings

• XGBoost Counterfactual Forecasting for Measuring Retail Promotion Sales Uplift

• Confidence-Gated Encoder-Decoder for Real-Time On-Device Class-Agnostic Segmentation

• Bi-LSTM for Accurate and Efficient Language ID in Short Text

• Powering E-commerce Autocomplete with Click/Conversion Data and a Specialized Index.

• Fair On-Device Face Recognition via Clustering Face/Body Embeddings & Sparse Coding.

• Personalized On-Device TTS: Fine-Tuning Pretrained Models with Noisy, Limited User Data

• ML Classification for Detecting Outdated Browsers and Recommending Personalized Upgrades

• Detecting Defective Products with Generative AI, Vision, and Multimodal Models.

• Pinpointing Delivery Locations: Pairwise Ranking ML for Noisy GPS Data

• Adaptive Conversational Music Recommendations Powered by Offline Reinforcement Learning.

• Commonsense Knowledge Graphs via LLMs for Enhanced E-commerce Recommendations

• AI-Driven Re-ranking with User Affinity Profiles for Personalized Experiences

• Building Ranked E-commerce Search Suggestions from User Clicks and Conversions

• Machine Learning Personalization for Scalable Travel Activity Search Ranking

• Deep Learning for Travel Search Ranking: Addressing Bias, Cold Start, and Diversity

• Detecting Sensitive Data & Secrets at Scale with ML, Regex, and CI Hooks

• Building an MDP-Powered Intelligent Automation Platform for Scalable Conversational AI.

• Scaling Travel Support with Generative Text: Recommendations & Real-Time Assistance

• Building Thematic Travel Collections with Machine Learning and Human Review.

• Automated Property Attribute Extraction using NER and Embedding Similarity

• High-Precision Themed Property Curation via ML and Human-in-the-Loop.

• Neural Network Ranking with Similarity Penalty for Diverse Marketplace Search.

• Using textCNN and Bayesian Inference to Rank Important Home Attributes from Guest Text.

• Hybrid Conversational AI: Integrating LLM Chain-of-Thought with Workflow Tools

• ML Case-study Interview Question:Automating Personal Data Classification with ML, Regex Detection, and Shift-Left Annotation

• ML Case-study Interview Question:Automated Unit Test Generation with LLMs: Scaling Coverage Securely

• ML Case-study Interview Q: Optimizing Payment Authorization Success Using Contextual Multi-Armed Bandits