In today's AI-driven landscape, developing models is only half the battle – deploying and maintaining them in production creates an entirely new set of challenges. This intensive 3-hour workshop bridges the gap between model development and production deployment, offering participants hands-on experience with industry-standard MLOps tools and practices used by leading tech companies.

Participants will work through real-world scenarios using tools like Hugging Face, MLflow, and BentoML to build production-ready ML pipelines. Through guided exercises, they'll learn to implement automated training pipelines, set up monitoring systems, and deploy scalable model serving solutions. The workshop features practical examples drawn from actual production systems, including real-time NLP services, computer vision pipelines, and recommendation systems.

Key topics covered include:
- Modern model management and versioning with Hugging Face Hub and MLflow
- Production deployment patterns using Docker, BentoML, and cloud platforms
- Monitoring and observability implementation with Prometheus and Grafana
- Pipeline orchestration using Kubeflow and GitHub Actions
- Model optimization techniques for production environments

This workshop is designed for ML engineers, data scientists, and technical leads who want to move beyond experimental notebooks to build robust, scalable ML systems. Participants will leave with working examples, production-ready templates, and practical knowledge they can immediately apply to their ML projects.