AI Product Developer
Become a full-stack AI Developer, mastering the skills needed to design, implement, and deploy production-grade AI applications from scratch.
- Duration60 Hours
- FormatOnline, instructor-led
- On completionCertificate
Course overview
This advanced, project-based course is designed for experienced developers and engineers who want to specialize in building AI-first applications. You will move beyond simple API calls to understand the complexities of the full AI product lifecycle, including model hosting, performance optimization, data pipeline integration, and deployment via cloud platforms (AWS/Azure/GCP). The focus is on implementing best practices for robust, scalable, and secure AI solutions.
- Job referrals
- Certificate of Course Completion
What you'll learn
Scalable AI Architecture
Design architectures that efficiently handle data pipelines and model inference at scale.
MLOps Best Practices
Implement versioning, monitoring, and automated deployment (CI/CD) for production models.
Full-Stack Development
Develop both the backend API and the frontend application layer for an AI service.
Cloud Deployment
Practical skills in deploying AI solutions on major cloud platforms.
Curriculum
5 modules · 60 Hours01Advanced AI Application Architecture
Designing scalable architectures for AI services (e.g., microservices), handling real-time vs. batch processing, and selecting appropriate cloud infrastructure.
02Model Integration and Optimization
Deep dive into serving and hosting models (e.g., using containers like Docker), model versioning, and performance optimization techniques for low-latency inference.
03Data Pipelines for AI
Building robust data ingestion and feature engineering pipelines, using MLOps principles, and managing data drift.
04Full-Stack AI Implementation
Developing the backend logic (API layer) and a front-end interface (using a modern framework) to interact with the deployed AI service.
05Testing, Monitoring, and Deployment
Implementing unit testing and integration testing for AI models, setting up model monitoring (e.g., for bias and drift), and implementing CI/CD pipelines.
Who it's for
Experienced Software Engineers
Developers who want to transition into building the infrastructure and application layer for AI products.
ML Engineers
Professionals who want to move beyond model building into the production and deployment lifecycle.
DevOps and MLOps Engineers
Individuals focused on automating the deployment, scaling, and monitoring of AI systems
Technical Leads
Senior team members responsible for defining the technical strategy for AI product development.
Before you start
Solid experience with Python, familiarity with Docker and Kubernetes concepts, and experience working with at least one major cloud platform (AWS, Azure, or GCP).
How you'll learn
Immersive instructor-led sessions
Participate in dynamic, hands-on sessions led by expert instructors to gain practical skills in a supportive learning environment.
Design and deploy a production-ready AI application
Implement all stages of the MLOps lifecycle, from data handling to model monitoring, resulting in a scalable, deployed application.
Build something real for your project portfolio
You will complete a hands-on project that can be added to your professional portfolio to demonstrate your skills to potential employers.
Learn anytime with unlimited 1-year access to contents and tools
Gain continuous learning with one year of unlimited access to all course materials, tools, and resources, allowing you to study at your own pace and revisit topics as needed.
Questions
What programming language will be used?
The primary language used for development and model integration will be Python.
Is model training covered?
This course assumes you can build or source a model; the focus is entirely on putting that model into production and building the surrounding application.
Will I learn how to deploy to my specific cloud?
The course covers generic cloud deployment concepts applicable to all major platforms, with specific examples using common cloud tools.
Is there certification upon completion?
Yes, upon successful completion, participants will receive a certificate demonstrating their competency as an AI product developer.
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