Course

TensorFlow: Data and Deployment

DeepLearning.AI

Deepen your TensorFlow expertise with the TensorFlow: Data and Deployment Specialization. Dive into a comprehensive learning journey that explores various deployment scenarios, data processing, and model deployment on different devices and platforms. Led by Laurence Moroney and Andrew Ng, this Specialization offers hands-on experience and practical skills for deploying machine learning models effectively and securely.

  • Master browser-based model training and inference with TensorFlow.js
  • Prepare and deploy models on mobile devices using TensorFlow Lite
  • Efficiently manage and process training data with TensorFlow Data Services
  • Explore advanced deployment scenarios using TensorFlow Serving, TensorFlow Hub, and TensorBoard

Whether you aspire to deploy models on browsers, mobile devices, or embedded systems, this Specialization provides the knowledge and tools to do so efficiently and accurately. Embrace the global shift towards AI adoption and gain the skills to deploy machine learning models across any device or platform with confidence.

Certificate Available ✔

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TensorFlow: Data and Deployment
Course Modules

Discover a comprehensive learning journey encompassing browser-based model training, mobile device deployment, efficient data processing, and advanced deployment scenarios using TensorFlow Serving, Hub, and TensorBoard.

Browser-based Models with TensorFlow.js

MODULE 1: Browser-based Models with TensorFlow.js

  • Train and run inference in a browser
  • Handle data directly in a browser environment
  • Build an object classification and recognition model using a webcam

Device-based Models with TensorFlow Lite

MODULE 2: Device-based Models with TensorFlow Lite

  • Prepare models for battery-operated devices
  • Execute models on Android and iOS platforms
  • Deploy models on embedded systems like Raspberry Pi and microcontrollers

Data Pipelines with TensorFlow Data Services

MODULE 3: Data Pipelines with TensorFlow Data Services

  • Perform efficient ETL tasks using Tensorflow Data Services APIs
  • Construct train/validation/test splits of any dataset using Splits API
  • Optimize data for training pipelines using various TFDS API modules and functions
  • Enhance workflow efficiency through input parallelization

Advanced Deployment Scenarios with TensorFlow

MODULE 4: Advanced Deployment Scenarios with TensorFlow

  • Use TensorFlow Serving for web inference
  • Explore TensorFlow Hub for model repository and transfer learning
  • Evaluate model performance and share metadata using TensorBoard
  • Discover federated learning and retrain models while preserving data privacy
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