Deep Learning MLOps with MLflow, PyTorch, and TensorFlow

Managing deep learning experiments can quickly become chaotic as you tweak hyperparameters, neural network architectures, and training datasets. Without a structured workflow, tracking which training run produced the best model is nearly impossible. This text-based course guides you through the fundamentals of MLOps using MLflow to bring order to your deep learning projects. You will learn how to systematically log parameters, metrics, and artifacts, ensuring your experiments are fully reproducible and ready for deployment. What you'll learn: Understand core MLOps concepts and the role of experiment tracking in modern workflows; Configure MLflow tracking to capture hyperparameters, metrics, and system details; Log PyTorch and TensorFlow models, parameters, and loss curves during training; Manage model versions and transitions using the MLflow Model Registry; Compare different training runs to identify the best-performing neural network configurations; Apply modern best practices for packaging and deploying deep learning models. You will start by exploring foundational MLOps principles and setting up your MLflow tracking environment. From there, you will progress to instrumenting PyTorch and TensorFlow scripts, organizing experiments, and utilizing the model registry for version control. This course is designed for beginner data scientists, machine learning enthusiasts, and developers who want to transition from manual model training to structured workflows, with no prior MLflow experience required. Start building organized, professional, and reproducible deep learning pipelines today.