Industrializing AI models involves more than just training them. It involves integrating them into robust production environments, providing continuous supervision and adapting them to business constraints.
Automated workflows with GitHub Actions, DVC, Docker and Kubernetes for smooth, traceable updates.
Monitoring of latency, drift, errors and user feedback to guarantee the stability and relevance of models in production.
Deployment via TorchServe, Triton, BentoML or MLflow for fine-tuned version, performance and endpoint management.
Exchange encryption, access authentication, call auditing and abuse protection.
Autoscaling, load balancing and deployment on AWS, Azure or GCP to absorb load and guarantee high availability.
Setting up AI servers on private infrastructure or edge devices, for sensitive or offline use cases.
Creation of intelligent architectures, APIs, conversational agents, recommendation systems
Integration of models such as GPT, LLaMA, Mistral, Claude, etc. into business workflows
Adaptation of pre-trained models to specific corpora, supervised or reinforcement training
Design and training of deep learning models (CNN, RNN, Transformers) & machine learning (Random Forest, Scikit-Learn) for complex cases
Combining documentary research and generation for precise, contextualized answers
Design and deployment of modular, secure local AI architectures capable of processing data directly on the device
Containerization, CI/CD, monitoring, scalability, model security in production
Consultant specializing in the development of solutions based on theartificial intelligencethe language models (LLM) and neural networks.
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