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.

Learning Outcomes
At the conclusion of the course, you should be able to

• Describe the technical and non-technical aspects of planning, designing and deploying an AI-based enterprise application
• Train and fine-tune a deep learning model on the cloud with existing datasets
• End-to-end application services design and considerations, deployment, and support
• Develop and deploy an enterprise cloud based application
• Explain what federated learning and continuous learning are and why they are needed
• Describe and demonstrate AI techniques

Topics Include

• 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

Skills Needed

A working knowledge of AWS.

Prerequisites:
One of the following courses or equivalent coursework or industry experience: "Deep Reinforcement Learning," "GANs for Data Synthesis," "Natural Language Processing," or "Artificial Intelligence for Robotics."