MLOps with Data Version Control: Tracking & Logging Deep Learning Models

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Data Version Control (DVC) offers robust support for deep learning models by effectively managing large model files and their dependencies, allowing versioned tracking of complex architectures. This ensures reproducibility in training, evaluation, and deployment pipelines, even in deep learning projects. In this course, you will discover how to track deep learning models through DVC. Using PyTorch Lightning, you will construct a convolutional neural network (CNN) for image classification. Then you will use DVCLive to log and visualize sample images and use the DVCLiveLogger to monitor model metrics in real time via Iterative Studio. Next, you will undertake deep learning model training with TensorFlow. You will set up a CNN for image classification and train your model while leveraging DVCLive to record and display training-related metrics. Finally, you will use the DVCLiveCallback to dynamically visualize metrics during training. This course will equip you with the expertise to effectively build and track deep learning models within DVC's ecosystem.