Build Intelligent Systems That Learn and Adapt
Expert machine learning consultant in Geneva specializing in building production-ready ML systems. From predictive models to recommendation engines, I help businesses leverage machine learning for competitive advantage. 8+ years experience deploying ML solutions that drive measurable business value.
Production-grade ML systems serving 30K+ users
Expertise in scikit-learn, TensorFlow, PyTorch
End-to-end ML pipeline development and deployment
MLOps best practices for reliable, scalable systems
Focus on business ROI, not just model accuracy
Experience across multiple ML domains (NLP, CV, recommendations)
Cloud-native deployments on AWS, Azure, GCP
Model monitoring and continuous improvement
Geneva-based with global ML project experience
Build models to forecast customer behavior, sales, demand, churn, and other business outcomes. Use advanced ML algorithms for accurate predictions.
Create personalized recommendation engines for products, content, or services. Collaborative filtering, content-based, and hybrid approaches.
Develop models for customer segmentation, fraud detection, quality control, and classification tasks. Support vector machines, random forests, and deep learning.
Build NLP solutions for sentiment analysis, text classification, named entity recognition, document processing, and language understanding.
Implement image recognition, object detection, visual search, and quality inspection systems. CNNs, YOLO, and modern vision transformers.
Deploy ML models to production with proper monitoring, versioning, A/B testing, and continuous delivery. Docker, Kubernetes, cloud platforms.
Machine learning is a subset of AI focused on systems that learn from data. While AI is a broad concept of machines performing intelligent tasks, ML specifically uses algorithms that improve through experience. Most practical AI applications today are built using ML techniques.
It depends on the problem complexity. Simple models might work with hundreds of examples, while deep learning typically needs thousands. However, techniques like transfer learning and data augmentation can work with smaller datasets. I'll assess your specific situation during consultation.
I implement bias detection and mitigation strategies including diverse training data, fairness metrics, regular audits, and techniques like reweighting and adversarial debiasing. Fairness and ethics are built into every ML project.
I follow a structured approach: 1) Problem definition and data assessment, 2) Exploratory data analysis, 3) Feature engineering, 4) Model development and validation, 5) Deployment and monitoring, 6) Iteration based on real-world performance.
Simple models can be prototyped in 1-2 weeks. Production-ready systems typically take 2-4 months including data preparation, model development, testing, and deployment. I always start with a quick proof of concept to validate feasibility.
Yes! Model interpretability is crucial. I use techniques like SHAP values, feature importance analysis, LIME, and visualization tools to explain model predictions. This is essential for stakeholder trust and regulatory compliance.