Build & Optimize TensorFlow ML Workflows

This short course helps you build and optimize machine learning workflows using TensorFlow 2.x. You’ll start by structuring an end-to-end pipeline that includes data ingestion with tf.data, model definition with Keras, and custom training with checkpointing for reliability. You’ll then learn how to optimize your models for deployment using TensorFlow Lite, including post-training quantization and latency benchmarking. Along the way, you’ll see how ML engineers measure performance, evaluate tradeoffs, and deploy models to mobile and edge devices. Through hands-on practice and real-world examples, you’ll learn to think like an applied ML practitioner who builds efficient, production-ready TensorFlow systems

• Build and optimize machine learning workflows using TensorFlow 2.x
• Apply model optimization for deployment with TensorFlow Lite and post-training quantization
• Measure latency and benchmark model performance
• Develop efficient, production-ready TensorFlow systems

• Basic familiarity with machine learning concepts and terminology
• Readiness to engage in practical exercises