TensorFlow: Data and Deployment Specialization Course Syllabus

Overview: This specialization provides a comprehensive exploration of TensorFlow tools for data handling and model deployment across diverse platforms. Over approximately 51 hours of content, learners will gain hands-on experience deploying machine learning models in web browsers, mobile devices, and production environments. With a focus on practical implementation, the course guides professionals through building efficient data pipelines and mastering advanced deployment techniques using TensorFlow.js, TensorFlow Lite, TensorFlow Serving, and related tools. The flexible structure allows working professionals to complete the program at their own pace while building real-world projects.

Module 1: Browser-based Models with TensorFlow.js

Estimated time: 18 hours

• Introduction to TensorFlow.js and browser-based machine learning
• Training models directly in the browser using JavaScript
• Running pre-trained models in web applications
• Building a computer vision project with webcam input for object recognition

Module 2: Device-based Models with TensorFlow Lite

Estimated time: 10 hours

• Introduction to TensorFlow Lite for mobile deployment
• Converting models for efficient execution on low-power devices
• Running inference on Android and iOS platforms
• Optimizing models for battery-powered and resource-constrained devices

Module 3: Data Pipelines with TensorFlow Data Services

Estimated time: 11 hours

• Accessing and organizing training data using TensorFlow Data Services
• Building efficient ETL pipelines with tf.data API
• Creating train/validation/test splits programmatically
• Optimizing data pipeline performance for large datasets

Module 4: Advanced Deployment Scenarios with TensorFlow

Estimated time: 12 hours

• Deploying models in production using TensorFlow Serving
• Leveraging TensorFlow Hub for transfer learning and model reuse
• Visualizing model performance and debugging with TensorBoard
• Implementing federated learning to retrain models while preserving data privacy

Prerequisites

• Proficiency in Python programming
• Familiarity with machine learning concepts and neural networks
• Basic experience with TensorFlow or equivalent framework

What You'll Be Able to Do After

• Run machine learning models directly in web browsers using TensorFlow.js
• Deploy and optimize models on mobile devices with TensorFlow Lite
• Construct and optimize scalable data pipelines using TensorFlow Data Services
• Implement advanced deployment strategies using TensorFlow Serving and TensorFlow Hub
• Apply federated learning techniques to update models securely across distributed devices