Comprehensive Diplomas · Intermediate
Diploma in AI Application Engineering
The one-year diploma for those who want to implement, deploy and integrate existing AI technologies — adapting industry-standard models like YOLO and open-source LLMs to real business problems, custom datasets and production systems.
- Level
- Intermediate
- Mathematics depth
- Essential
- Engineering depth
- Systems
- Modality
- Cohort-based · one-year diploma track
- Duration
- 1 year
- Status
- Open for inquiry
Audience
Who this is for
- Aspiring AI application developers and MLOps engineers
- Computer-vision / NLP implementers working with custom datasets
- Engineers targeting solutions-architect roles
Career paths
Prerequisites
- — Programming fundamentals in any language
- — Comfort with APIs and the web stack
- — No prior AI experience required
Outcomes
Skills acquired
- Adapt established models — YOLO-class detectors, open-source LLMs — to proprietary datasets
- Integrate AI models into web applications, databases and software ecosystems
- Package and deploy with Docker to cloud platforms (AWS, GCP, Azure)
- Optimize inference speed and cost for production workloads
Tools used
Curriculum Architecture
Module progression
Expandable, visual, ordered. Each module is a prerequisite-aware step, not an isolated video.
- 01The Applied AI Landscape: Selecting Industry-Standard Models
- 02Data Preparation: Building Custom Datasets from Business Data
- 03Custom Model Tuning: Training YOLO-Class Vision Models
- 04Custom Model Tuning: Fine-Tuning Open-Source LLMs (PEFT / LoRA)
- 05Transfer Learning & Domain Adaptation
- 06Training vs. Adapting: Choosing the Right Lever per Problem
- 07Evaluation & Error Analysis for Applied Systems
- 08API & Pipeline Integration: Connecting Models to Applications
- 09Models to Databases & Software Ecosystems
- 10Deployment & Scaling: Docker & Containerization
- 11Cloud Deployment: AWS, GCP & Azure · Inference Speed & Cost
- 12Capstone: Implement an Industry-Standard Architecture for a Real Business Problem
Projects
- A YOLO-class detector trained on a proprietary dataset — data preparation through evaluation
- An open-source LLM fine-tuned on domain data and served through an integrated API pipeline
- A Dockerized, cloud-deployed application with optimized inference as the graduation capstone
Assessment philosophy
Assessment is engineering review: written error analyses, measured system behavior, defended design decisions. We evaluate whether you can explain and justify what you built — because production will.
Stack position: Data → Models → Applications → Deployment → Scale.
FAQ
Frequently asked questions
Do I need a mathematics background?
It depends on the program. Foundation-tier programs start from the mathematics itself; advanced tiers list working linear algebra as a prerequisite. The Mathematics for AI program exists precisely to close that gap.
Is this a bootcamp?
No. CAS Studies is an engineering institute. Programs are built around architectures, derivations and projects with written error analysis — not tutorial replays.
How long does a program take?
The two diploma tracks run on fixed lengths — AI Architectural Engineering spans 18 months, AI Application Engineering spans 1 year. All other program durations are announced per cohort, by modality.
Will I build real systems?
Yes. Every program ends in projects that resemble production work: evaluated models, grounded answer systems, supervised agents — with measurement, not vibes.