Method
Live-Online
Term
WINTER
SPRING
Units
2.0 QUARTER UNITS
Estimated Cost
$795

Skills you will gain

  • AI Application Development: Design and deployment of real-world enterprise AI systems.
  • Deep Learning Skills: Training of models for image recognition using TensorFlow/Keras.
  • Cloud Deployment Techniques: Implementation of AI services in cloud environments.
  • Advanced AI Concepts: Exploration of federated and continuous learning frameworks.
  • End-to-End AI Workflows: Execution of complete AI lifecycle from planning to support.

Course Description


This course provides developers a practical, industry-oriented training on how to develop integrated artificial intelligence (AI) applications for enterprises. Leveraging knowledge acquired through various elective courses, you will learn to apply your skills to cutting-edge AI applications during hands-on classroom sessions using machine learning frameworks.

In the classroom, we'll focus on convolutional neural networks and how they work, and perform training and inference using Tensorflow/Keras for image detection, recognition and segmentation. You'll learn various aspects of designing and deploying applications in the real world and work on a final project encompassing the new technologies you've learned.

 

Topics

  • DNN and how it fits in AI and traditional ML techniques
  • Concepts of supervised deep learning models
  • End-to-end application services design and considerations, deployment, and support
  • Understanding of various AI cloud services and their deployment models
  • Scoping a project, setting requirements, timelines, and deliverables
  • MLOps overview
  • Federated learning and on-device learning


Notes

Students will use the Google Cloud Platform - GCP for course exercises and assignments. 

 

Prerequisites / Skills Needed

Prerequisites:

  • AISV.X401: Deep Learning and Artificial Intelligence

Skills Needed:

  • A working knowledge of GCP. 
Syllabus Library
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This course applies to these programs:

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