AI Model Development & Deployment

Overview

This Specialization equips you with the skills to design, build, and deploy production-ready machine learning systems from end to end. You'll learn to architect custom neural networks, optimize deep learning models, engineer robust data pipelines, and implement MLOps best practices including CI/CD, automated testing, documentation, and cloud deployment. Through hands-on projects using PyTorch, TensorFlow, FastAPI, and cloud platforms like AWS SageMaker, you'll gain practical experience building scalable AI systems that meet real-world performance, reliability, and governance requirements.

Syllabus

Course 1: Design and Build Custom Neural Networks
Course 2: Optimize Deep Learning Models for Peak AI
Course 3: Engineer, Validate, and Govern ML Data
Course 4: Build Testable Python Packages for AI
Course 5: Deploy and Optimize Cloud AI Architectures
Course 6: Design Scalable AI Systems and Components
Course 7: Integrate and Optimize AI Services Seamlessly

Courses

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1 hour 44 minutes
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This course teaches you how to evaluate and design custom neural network architectures for real machine-learning tasks. You start by learning how to compare common model families—such as CNNs, RNNs, and Transformers—and match them to task needs, data patterns, and compute limits. You then learn how to construct custom architectures using layers, activations, and regularization techniques that improve generalization and training stability. Through videos, readings, hands-on practice, and guided coach support, you build models in PyTorch and test how design choices affect performance. By the end of the course, you can confidently select topologies, justify architectural decisions, and design models ready for real-world deployment.