Units
2.0 QUARTER UNITS
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
Prerequisites / Skills Needed
A working knowledge of GCP.
Notes
Students will use the Google Cloud Platform - GCP for course exercises and assignments.
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This course applies to these programs:
