This course is based on the TensorFlow Dev Summit 2020, bringing together keynotes, technical sessions, and demos to explore the full spectrum of modern machine learning with TensorFlow. It provides both strategic and technical insights, helping developers, ML engineers, and researchers leverage TensorFlow effectively in production and research environments.

Learners will explore updates in TensorFlow 2.11, build deep learning models using TensorFlow and Keras, and take advantage of GPUs, TPUs, and distributed multi-worker setups. The course covers TensorFlow Lite for mobile and microcontroller devices, TensorFlow.js for web-based ML, and emerging areas like TensorFlow Quantum. TFX and production ML pipelines are explained in detail, alongside performance profiling, TF Model Optimization Toolkit (quantization and pruning), and the new TFRT runtime.

The course also emphasizes practical ML solutions, including collaborative model development with TensorBoard.dev, scaling tf.data pipelines, on-device ML deployment, and real-world applications from JPL, Jacquard, and Live Perception. Ethical considerations are covered via sessions on Responsible AI and Fairness Indicators.

By the end, learners will have a complete understanding of modern TensorFlow capabilities, from model development and optimization to production deployment and innovative ML applications across devices and domains.