AI Model Development & Deployment
About this course
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.
55/100
CourseAsk score
- What the provider tells you
- 31/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- design custom neural network architectures
- optimize deep learning models for performance and efficiency
- engineer reliable data pipelines for machine learning
- implement MLOps workflows including CI/CD and automated testing
- deploy machine learning services using FastAPI
- use AWS SageMaker to launch scalable AI applications
Course objectives
- apply deep learning frameworks to real‑world problems
- establish end‑to‑end ML production pipelines
- integrate DevOps principles into AI model lifecycle
Price shown by Coursera — confirm on their site.
Enroll on CourseraYou'll be redirected to Coursera to complete enrollment.
- Listed & compared by CourseAsk
- English · 0
Udemy
Coursera
Compared on these lists
Where this course ranks against the alternatives.