All technologies A2C Agents Alembic Amazon SageMaker Anomaly detection Apache Beam AWS AWS Batch AWS Glue AWS Lambda Causal inference Chroma CI/CD Clark-Scarf Claude Clustering Contrastive learning Cross-attention CUDA C++ Differential privacy Distributed training Docker Domain adaptation DPO DQN EC2 EconML Edge inference Elasticsearch Fashion-CLIP FastAPI Federated learning Fine-tuning Flink Flower GCP Generalized-EBs GitHub Actions GPU optimisation Grafana Grounding DINO Horovod Human-in-the-loop Influencer marketing InfoNCE Istio Jenkins Kafka KEDA Kubeflow Kubernetes LangChain LoRA Marketing Marketing budget allocation MCP MILP MLflow MLOps Multimodal retrieval Neo4j Observability ONNX Runtime Opacus Operations research Optuna OutfitTransformer PM4py PPO Process mining Prometheus pytest Python PyTorch QDA Quantization RAG Ray Ray Tune Recommender systems Redis Reinforcement learning Responsible AI RLHF Scala SciPy / HiGHS SQLAlchemy SQLite Streamlit Submodular optimization Supply chain TensorFlow.js TensorRT-LLM Terraform Triton Inference Server VAE Variational autoencoder Vector search Vision-language models vLLM WASM Weakly supervised learning XGBoost Zero-shot classification
Fintech / Credit risk 2026
A causally-audited reinforcement-learning pipeline for loan/insurance offer optimization, built on real BPI Challenge 2017 event data.
Offers in demand model 42,821
Events / applications 193,849 / 31,509 PM4py EconML Apache Beam Kubernetes Kubeflow Process mining Causal inference View case study Privacy-preserving ML / Edge 2025
Next-word prediction trained across distributed clients where only differentially-private model updates ever leave the device.
Privacy budget after 15 rounds ε ≈ 1.12
Top-5 next-word accuracy 19.0% Flower Opacus Kafka Flink Scala Redis Grafana View case study Supply chain / Operations research 2025
RL agents that match classical optimal stock policies on fixed lead-times and outperform them when lead-times vary.
Optimality gap vs C-S (fixed LT) 0.02%
Lift vs alternatives (varying LT) +4.1% Reinforcement learning DQN A2C PPO Supply chain Operations research Clark-Scarf View case study Automotive / Supply chain quality 2025
A hybrid deep-learning and classic-ML anomaly detection platform that flags supply-chain irregularities and recommends classification.
Projected annual savings €1.6M+
Model families combined 4 Anomaly detection Variational autoencoder VAE Clustering XGBoost QDA Weakly supervised learning View case study Fashion retail / Influencer marketing 2026
Built on a company's own catalogue: compose outfits from its products, then pick the influencer marketing portfolio that best fits them under a budget.
Compatibility AUC (held-out) 0.888
Outfits used for training 27,000 Marketing Influencer marketing Marketing budget allocation Fashion-CLIP Vision-language models Multimodal retrieval OutfitTransformer View case study 