In this session, we will dive deep into the full lifecycle of traditional Machine Learning and Deep Learning workflows — from data preparation and model training to experiment tracking, model evaluation, and deployment strategies. Learn best practices for managing scalable ML pipelines, tracking experiments using tools like MLflow, and deploying models into production environments. We will cover key concepts such as model versioning, reproducibility, and deployment patterns for both ML and DL models. Whether you are building classification models, recommendation systems, or deep neural networks, this event will give you practical insights and hands-on techniques to streamline your workflows and deliver reliable AI solutions.

What You’ll Learn:

• Designing end-to-end ML & DL pipelines

• MLflow tracing for LLM observability

• Model training and hyperparameter tuning

• Experiment tracking with MLflow

• Model versioning and registry management

• Deployment strategies (batch, real-time, REST APIs)

• Tips for monitoring, scaling, and maintaining models in production